Léonard Guillou

Aurore Grandin

Coralie Chevallier

L Safra

Temporal discounting mediates the relationship between socioeconomic status and social trust

Keywords: Temporal discounting, perceived relative affluence, social trust, information shock. Chapter

Politicians pay attention to their image, and their face is a key part of how people see them. The face they display can inform us on how they want to be seen. More specifically, the attributes they try to show through their face, instructs us on what is valued by the people. More than collected from the French National Assembly, creating a homogeneous, continuous and exhaustive time series of faces. Perceived facial trustworthiness and dominance scores were evaluated by an algorithm previously trained on avatars generated using the model developed by Oosterhof & Todorov (Oosterhof & Todorov, 2008). This study was pre-registered before Chapter Three 60 analysis. We hypothesized that perceived facial trustworthiness levels increased with economic development. Using mixed effects linear models, the valuation of perceived facial trustworthiness across time was assessed and the relationships with various economic data (GDP per capita, mean income, unemployment, inequalities, etc.) were explored. In line with our hypothesis, we found that perceived facial trustworthiness levels increased with time (for time in years, b = 0.015, p < 0.001) and were correlated with economic data in the predicted way (e.g., GDP pc: b = 0.48, p < 0.001; national unemployment: b = -0.073, p < 0.001, these are standardized betas). These results are in line with the literature showing a relationship between economic development or socioeconomic status and social trust.

General introduction

1. Social trust, a behavioral trait, and its determinants 1.1. What is social trust and why study it?

Social trust is the belief that most people can be trusted, that they have good intentions toward others instead of trying to exploit them. In other words, it is the idea that most people are good cooperation partners. Social trust is of central importance in the context of whether interacting with someone. It seems fair to engage in a social interaction with someone to the extent that this partner will reciprocate by providing a return on your investment or increase your reputation. In this thesis we focus on a specific type of social trust, namely generalized social trust. The latter concerns unknown individuals, about whom we have no clues about their trustworthiness. Therefore, it describes a general belief about others and do not focus on specific individuals. We often use the terms social trust and generalized social trust interchangeably.

To better understand what social trust is, it is necessary to describe what reciprocal cooperation is (see figure 1). When an individual A decides to engage in a social interaction with another individual B, A pays a cost. This cost can be the time spent in the interaction, the outside options dropped or whatever the thing asked by B is. If B is a good cooperation partner, i.e., a Chapter One could help A in the future or spread the word that A is a good cooperation partner. In this context, A trusts B means that A believes that B will recip investment.

Figure 1. Reciprocal cooperation.

It could be argued that in industrialized societies, we often meet unknown individuals whom we will never see again, so they rarely have the opportunity to give us a return on investment.

What is important to keep in mind is that first in the environment of evolutionary adaptedness of the human species, individuals lived in relatively small groups and the number of individuals they could encounter on their territory was a lot smaller than nowadays ((R. B. [START_REF] Lee | Hunter-Gatherers and Human Evolution: New Light on Old Debates[END_REF] but see [START_REF] Singh | Human social organization during the Late Pleistocene: Beyond the nomadic-egalitarian model[END_REF]). Therefore, social interactions often occurred between genetically related individuals, and the probability that two unrelated individuals who had participated in a social interaction would meet again was much higher at that time. Second, if

A and B interact in a social interaction with people observing the interaction, the signaling function of appearing as a good cooperation partner should not be neglected. Indeed, to be able to get help from other individuals in the future, one should appear as a good cooperation partner now.

Generalized social trust brings benefits at different levels, from the individual to the society. At the individual level, social trust enables cooperation between unrelated individuals, in particular when they have conflicting interests [START_REF] Balliet | Trust, conflict, and cooperation: A meta-analysis[END_REF]. It increases the acceptance of vaccination [START_REF] Rönnerstrand | Social capital and immunisation against the 2009 A(H1N1) pandemic in Sweden[END_REF] and cooperation in social dilemmas with a large number of actors [START_REF] Mannemar Sønderskov | Explaining large-N cooperation: Generalized social trust and the social exchange heuristic[END_REF]. Social trust is also important for personal well-being [START_REF] Rothstein | All for All: Equality, Corruption, and Social Trust[END_REF] and civic engagement [START_REF] Crystal | Sources of Civic Orientation Among American Youth: Trust, Religiious Valuation, and Attributions of Responsibility[END_REF]. At the societal level, social trust has been shown to be related with economic growth, trade and financial market expansion (Bjørnskov, 2017;[START_REF] Guiso | Trusting the Stock Market[END_REF][START_REF] Guiso | Cultural biases in economic exchange[END_REF][START_REF] Knack | Does Social Capital Have an Economic Payoff? A Cross-Country Investigation[END_REF].

Despite all these benefits, social trust levels vary considerably between individuals [START_REF] Alesina | Who trusts others?[END_REF] and between societies. In Europe, social trust is higher in northern countries, especially in Denmark, Sweden and Finland and is lower in southern countries, especially Italy, Portugal and Greece [START_REF] Albanese | Who trusts others more? A cross-European study[END_REF]. If social trust brings benefits to individuals and societies, how can we explain that it varies that much, even in countries sharing a lot of their culture? To answer this question, it is necessary to describe social trust more precisely, to understand how and why it varies.

What are the determinants of social trust?

There are two major types of environmental determinants of social trust [START_REF] Albanese | Trust, risk and time preferences: Evidence from survey data[END_REF].

First, social environment, i.e., past experiences with others, gives an individual an idea of the probability to encounter a good cooperation partner or a cheater [START_REF] Fehr | On The Economics and Biology of Trust[END_REF]. Secondly, nonsocial environment, like the amount of resources available, modulates what can be seen as intrinsic preferences of an individual [START_REF] Albanese | Trust, risk and time preferences: Evidence from survey data[END_REF].

A large literature focuses on the characteristics of the social environment that influence social trust. For example, [START_REF] Glanville | How do We Learn to Trust? A Confirmatory Tetrad Analysis of the Sources of Generalized Trust[END_REF] emphasize the importance of contemporary and localized trust experiences in determining trust levels of individuals. Diversity of the social environment, such as ethnic [START_REF] Hooghe | Ethnic Diversity and Generalized Trust in Europe: A Cross-National Multilevel Study[END_REF], linguistic [START_REF] Alesina | Segregation and the Quality of Government in a Cross Section of Countries[END_REF] or political [START_REF] Beugelsdijk | Diversity and trust: The role of shared values[END_REF] diversity, is often seen as decreasing social trust. The idea is that the more people share values, the more they perceive a shared destiny, and therefore, feel close to their fellow citizens and are able to identify with one another. As a consequence, trust between them is increased [START_REF] Beugelsdijk | Diversity and trust: The role of shared values[END_REF]. Yet, these studies focus on a type of trust, closer to community trust or trust between fellow citizens, that is slightly narrower than the generalized social trust that interests us. Moreover, there is no clear theoretical ground explaining why social diversity decreases social trust. Finally, another type of trust determinant, that does not exclude the effect of diversity -and may even be negatively correlated with it [START_REF] Beugelsdijk | Diversity and trust: The role of shared values[END_REF] -and is better supported by theory is the one we focus on in this thesis, namely the amount of resources in the environment. Therefore, in the rest of this thesis, we will focus on a non-social determinant of social trust.

The effect of resources on social trust has been studied extensively for decades. Early environment is known to have long lasting effects on social trust. As an earliest effect of the mother's environment, [START_REF] Petersen | Birth Weight and Social Trust in Adulthood: Evidence for Early Calibration of Social Cognition[END_REF] found that a lower birth weight was associated with lower social trust in adulthood. Using data from Germany after the second World War as an exogenous negative shock on childhood resources, (Hörl et al., 2016) showed that children exposed to more hunger during childhood displayed lower levels of trust as adults. Finally, higher childhood socioeconomic circumstances [START_REF] Mell | Early life adversity is associated with diminished social trust in adults[END_REF][START_REF] Stamos | Investigating the effect of childhood socioeconomic background on interpersonal trust: Lower childhood socioeconomic status predicts lower levels of trust[END_REF] are positively associated with social trust. In addition to these early effects, social trust of adults is still sensitive to socioeconomic status. For example, [START_REF] Brandt | Changes in Income Predict Change in Social Trust: A Longitudinal Analysis: Socioeconomic Status and Trust[END_REF] have shown using longitudinal data, that a model using income variations to predict social trust variations was more viable than the opposite model using social trust to predict income. Interestingly, even in the case of leader choice, socioeconomic status seems to modulate the importance of trust. For example, it has been shown that childhood harshness predicts long lasting preferences for less trustworthy leaders [START_REF] Safra | Childhood harshness predicts long-lasting leader preferences[END_REF].

How could low trust be adaptive in some environments?

Variations in social trust might be explained by differences in the perception of costs and benefits associated to cooperation. In any social interaction there is a cost paid by the agents.

When two individuals interact, they lose their outside options [START_REF] Debove | Partner choice creates fairness in humans[END_REF]. Outside options can be described as what individuals could have done during the timespan they were interacting. For example, rather than helping someone finding his way on the street, one could continue walking and arrive earlier at work or at home. Being cooperative or being selfish is the result of balancing different outside options with the benefits of the social interaction. The perception of the cost of giving up ones outside options is not the same depending on the amount of resources available to an individual. It is possible to extend and generalize this idea by including the delay between the investment in the social interaction and the reward from this interaction. The phenomenon is the same: while an individual is waiting for the return on his investment to happen, he is dropping outside options he would have been able to explore using the amount of resources invested in the interaction. This can be called waiting cost [START_REF] Mell | Both collection risk and waiting costs give rise to the behavioral constellation of deprivation[END_REF]. If the resources of an individual are low, the cost of not using these resources right away is high. A second element whose perception can influence social trust is collection risk (Pepper & Nettle, 2017). It is the risk of not collecting a reward from the social interaction, either because of cheating from the cooperation partner, or because of an event happening before the reward has been received. Once again, the cost of not receiving a return on investment is higher for an individual with few resources than for an individual with plenty of it.

Interestingly, these two types of risks and costs can be reflected i Time preferences can be described as the fact of being more present or future oriented, valuing more short-term benefits or long-term ones. Especially, people having a salient perception of waiting cost should prefer sooner-smaller rewards over later-larger ones. Indeed, if the cost of waiting for a larger reward is higher than this reward, the individual should choose the sooner reward (even if smaller) that does not require a waiting time and consequently does not trigger a waiting cost. In their paper on the behavioral constellation of deprivation , (Pepper & Nettle, 2017) explicitly describe temporal preferences as being one major element explaining behavioral diversity. They sketch the mechanism mainly based on collection risk but in their commentary [START_REF] Mell | Both collection risk and waiting costs give rise to the behavioral constellation of deprivation[END_REF] explain that waiting cost perfectly fits it this view.

These theoretical ideas [START_REF] Lie-Panis | Cooperation as a signal of time preferences[END_REF][START_REF] Mell | Both collection risk and waiting costs give rise to the behavioral constellation of deprivation[END_REF]Pepper & Nettle, 2017) can explain why the amount of resources available to individuals is a determinant of trust. If an individual has few resources, waiting cost and collection risk are salient to him. As a consequence, this individual values more sooner-smaller rewards over later-larger ones. In the end, reciprocal cooperation that is based on long-term rewards seems more hazardous to him and he therefore trusts less.

Nevertheless, it is important to note that this pathway -from resources to risks and costs perception and temporal preferences, and finally to social trust -has been suggested to be more complex. Some argue that social trust influences risk perception by decreasing it [START_REF] Siegrist | Perception of risk: The influence of general trust, and general confidence[END_REF][START_REF] Siegrist | Perception of Hazards: The Role of Social Trust and Knowledge[END_REF]. However, a recent review on this topic [START_REF] Siegrist | Trust and Risk Perception: A Critical Review of the Literature[END_REF] explains that trust is only correlated with risk perception in specific situations, namely when people have little knowledge about an issue they care about. These types of risks, like climate change [START_REF] Shi | Knowledge as a driver of public perceptions about climate change reassessed[END_REF], nuclear power [START_REF] Costa-Font | Attitudes as an Expression of Knowledge Risk Analysis[END_REF], or gene technology (Connor & Siegrist, 2010), are completely different from collection risk. Moreover, as [START_REF] Siegrist | Trust and Risk Perception: A Critical Review of the Literature[END_REF] acknowledges, this study field is mostly constituted of correlational studies, with little to no causal testing.

To sum up, it is not optimal to have a very high level of social trust when one has low resources.

argue that the modulation of social trust is the result of an evolved psychological mechanism taking into account the benefits of cooperation but also its costs and risks. To further understand this mechanism, we need to explain the concept of phenotypic plasticity.

How do living organisms respond to their environment?

Phenotypic plasticity

Phenotypic plasticity is the fact that two individuals with the same genotype do not express the same phenotype when they are in different environments. Their genotype is not changed by the environment, but the way in which this genotype is expressed in a phenotype can vary according to environmental characteristics. In other words, phenotypic plasticity is the ability of an individual organism to change (reversibly or not) some aspects of its phenotype in direct response to a change in its environment.

To illustrate this idea, let us take a famous example. Daphnia, also called water flea, are planktonic crustaceans, living in various freshwater environments and feeding on unicellular algae, protists or bacteria. What is very surprising about this species is that there are individuals with very different morphologies, some of them even have a kind of spiked helmet on their head while others do not. One might expect these differences to be linked to the genetic makeup of the Daphnia individuals, but this is not the case. Two genetically identical individuals can express these different phenotypes. What is even more interesting is that these different morphotypes are not randomly distributed and are not found identically in all environments. In fact, Woltereck discovered in 1909 that it is the characteristics of the environment that determine the morphotypes of individuals [START_REF] Woltereck | Weitere experimentelle Unter-suchungen uber Artveranderung, speziell uber das Wesen quantitativer Artunterschiede bei Daphniden[END_REF]. However, these are not environmental constraints inducing an abnormal development such as nutritional deficiencies or high or low temperatures. In reality, it is the presence or absence of predator cues that determines the development of Daphnia into a particular morphotype. The phenomenon of phenotype variation in response to environmental cues is now called phenotypic plasticity This mechanism is encoded in the genome. Further work on phenotypic plasticity has shown its major importance in explaining within species phenotype variability, from morphology to behaviors, and in any type of biological organism.

Phenotypic plasticity is based on the fact that the optimal phenotype is not always the same, depending on the environment. To take again the example of the Daphnia presented above, it is clear that the armor phenotype protects against predators and is therefore optimal in an environment where predation pressure is high. Conversely, the production of this armor is energy-consuming and is not optimal in an environment with low predation pressure. The general mechanism behind phenotypic plasticity is as follows: an organism perceives environmental cues, which leads to a change in the regulation of the expression of specific genes, and the consequence is a change in the phenotype of the organism.

Despite the well-known example of Daphnia, plants, more than animals, have long been known to be the champions of phenotypic plasticity. Indeed, their sessile lifestyle, obliges them to cope with variable environmental conditions, with no way out [START_REF] Schlichting | The Evolution of Phenotypic Plasticity in Plants[END_REF]). An example of phenotypic plasticity in plants is the shape of Pinus sylvestris individuals. In response to the shading by neighbors, in a context of competition for light, these trees grow tall. On the contrary, in open areas, there is no advantage to investing in height, there may even be disadvantages such as greater wind load: as a result, these trees are much smaller. The height phenotype of the Pinus Sylvestris is plastic, as it varies between local environments, and this variation is an adaptive response to the presence or absence of shading neighbors [START_REF] Farjon | Pines: Drawings and Descriptions of the Genus Pinus[END_REF].

Phenotypic plasticity can take different forms. It can be radical or discrete; in the latter case it can also be called polyphenism. For example, the monkeyflower plants (Mimulus douglasii), small annual herbaceous species native to the USA, respond to photoperiod with two distinct flower types. They produce large open flowers in response to short days, and small closed flowers in response to longer days [START_REF] Barnett | Plastic breeding system response to day length in the California wildflower Mimulus douglasii[END_REF] (see figure 2). Another striking example is the polyphenism in size observed in leafcutter ants (Atta cephalotes). Depending on the food they receive as larvae, these ants develop into a large major worker or a small minor worker [START_REF] Wheeler | Developmental and Physiological Determinants of Caste in Social Hymenoptera: Evolutionary Implications[END_REF]) (see figure 2). Phenotypic plasticity can also be more subtle and continuous.

In this case, the term proposed by Woltereck, reaction norms is more appropriate. Indeed, there is a continuum of phenotypes that can be represented with a line (see figure 3). An example of continuous plasticity is the plumage coloration of wild house finches that is linearly correlated with the amount of carotenoids in their diet [START_REF] Hill | Dietary carotenoids predict plumage coloration in wild house finches[END_REF]. As another example, in response to eating more meat, spadefoot toad tadpoles undergo an increased expression of a peptidase gene (pm20d2) [START_REF] Levis | Genetic accommodation in the wild: Evolution of gene expression plasticity during character displacement[END_REF]. Depending on an environmental change in food availability, spadefoot toad tadpoles have the ability to alter their gene expression to produce the appropriate enzymes. The discrete or continuous form of plasticity probably depends on the type of environmental cue and the linear or non-linear relationship between the environmental variable and the optimal trait value in that environment [START_REF] Lefevre | Social gradient in initiation and transition to daily use of tobacco and cannabis during adolescence: A retrospective cohort study: Tobacco and cannabis use during adolescence[END_REF]. Left: large open and small closed flowers of monkeyflower plants (Mimulus douglasii). Right: large major and small minor workers of leafcutter ants (Atta cephalotes). From [START_REF] Pfennig | Phenotypic Plasticity & Evolution: Causes, Consequences, Controversies[END_REF]. A: continuous. B: discrete (corresponds to polyphenism). C: relatively flat (corresponds to a canalization). From [START_REF] Pfennig | Phenotypic Plasticity & Evolution: Causes, Consequences, Controversies[END_REF].

These various examples show that, in most of the cases, phenotypic plasticity is adaptive. The ability to display plastic phenotypes, can provide a fitness advantage to individuals. In this case, as there are differences in fitness between plastic and non-plastic individuals, a selection pressure can operate and the question of the evolution of phenotypic plasticity can be asked.

An interesting example about phenotypic plasticity, which also addresses the question of its evolution, is the study of the Trinidadian guppy [START_REF] Torres-Dowdall | Local adaptation and the evolution of phenotypic plasticity in trinidadian guppies (Poecilia reticulata): Adaptation and the evolution of phenotypic plasticity[END_REF]. The ancestral environment of all the guppies in this paper includes predators. But some populations have migrated to river areas without predators. Interestingly, all individuals originating from high and low predation rivers react in the same way to the presence of chemical predator cues. When reared in the presence of predator cues, all guppies have a fairly small size at maturity, a specific head shape and swim close to the surface. In contrast, when reared in the absence of predator cues, only guppies from low-predation rivers have a different head shape and a bigger size at maturity, which may be a competitive advantage. When individuals migrate to a different environment from their ancestral one, selection favors more plastic individuals, as they are able to cope more efficiently with new environmental characteristics. In this case, plasticity precedes local adaptation.

Interestingly, humans are no exception to the rule: they also have several traits that exhibit phenotypic plasticity. A very simple example is tanning (see [START_REF] Quillen | The Evolution of Tanning Needs Its Day in the Sun[END_REF]). After a certain time of exposure to ultraviolet light, as DNA damage accumulates, tanning, also called facultative melanogenesis, begins [START_REF] Gilchrest | DNA Photodamage Stimulates Melanogenesis and Other Photoprotective Responses[END_REF]. This phenotypic plasticity is adaptive because melanin plays a major role in photoprotection [START_REF] Coelho | Short-and Long-Term Effects of UV Radiation on the Pigmentation of Human Skin[END_REF] by absorbing ultraviolet radiations [START_REF] Kollias | Absorption Mechanisms of Human Melanin in the Visible, 400 720nm[END_REF] before they reach the deeper cells and damage them. However, because ultraviolet light is needed for skin production vitamin D, this plasticity is reversible, so lighter-skinned individuals are able to synthesize vitamin D even with little ultraviolet light [START_REF] Chaplin | Vitamin D and the evolution of human depigmentation[END_REF].

Finally, it is important to note that phenotypic plasticity is not always adaptive. In some cases, the environment simply constraints the optimal expression of the phenotype. Second, phenotypic plasticity applies not only to morphological changes but also to behavioral changes.

But several questions still remain. How does behavioral plasticity differ from other types of plasticity? Is behavioral plasticity always reversible? These questions are addressed in the next section.

Phenotypic plasticity and behavioral plasticity in non-human animals

Phenotypic plasticity can occur for morphological traits but also for behavioral traits. In the above presentation of Torres-study on Trinidadian guppies [START_REF] Torres-Dowdall | Local adaptation and the evolution of phenotypic plasticity in trinidadian guppies (Poecilia reticulata): Adaptation and the evolution of phenotypic plasticity[END_REF], the focus was on morphological plasticity, but behavioral plasticity is also reported and studied in the paper. In this example, the behavioral trait that shows phenotypic plasticity is swimming behavior. In the presence of predator cues, guppies swim close to the surface whereas in the absence of predator cues, they explore the entire water column. Swimming close to the surface may prevent being attacked by bottom-dwelling predators, but it reduces the volume of water that can be explored in foraging situations. Therefore, the modifiable nature of swimming behavior in response to predator cues seems adaptive.

Behavioral plasticity of individuals can enable the maintenance of the species in the face of environmental changes. More specifically, behavioral plasticity has a strong ecological importance in a period of climate change. For example, [START_REF] Ducatez | Behavioural plasticity is associated with reduced extinction risk in birds[END_REF] have shown that birds with higher levels of behavioral plasticity (as measured by a higher propensity to innovate) are less likely to become extinct than birds with lower levels of behavioral plasticity.

Whether behavior is more plastic than other traits is still debated [START_REF] Chenard | The Special Case of Behavioral Plasticity?[END_REF].

However, morphological traits, are often definitely established by the end of development.

Apart from a few examples such as tanning, when the organism's growth period has come to an end, few morphological changes can still occur. Instead, behaviors are more labile, less constrained in their trait values and reversible in the most cases. Therefore, behavioral plasticity may precede and lead other types of plasticity and local adaptation [START_REF] Levis | Genetic accommodation in the wild: Evolution of gene expression plasticity during character displacement[END_REF].

Incidentally, Mayr wrote in 1963 in Animal Species and Evolution [START_REF] Mayr | Animal Species and Evolution[END_REF] a new niche or adaptive zone is, almost without exception, initiated by a change in behavior

In line with this, it has been shown that behavioral change can even drive evolutionary diversification. For example, it has been shown that pigeons and doves, which shifted from terrestrial to arboreal foraging, underwent an accelerated evolutionary divergence leading to morphological specialization [START_REF] Lapiedra | Behavioural changes and the adaptive diversification of pigeons and doves[END_REF].

While all behaviors are reversible, some remain stable throughout the life of organisms. They are often a response to early life conditions. [START_REF] Bateson | Developmental telomere attrition predicts impulsive decision-making in adult starlings[END_REF] have shown that starlings reared in larger broods, which therefore experienced competition early in life, had shorter telomeres and made more impulsive foraging decisions as adults. Since high telomere attrition is a sign of poor physiological status and short life expectancy, prioritizing immediate survival and reproduction over long-term benefits, i.e., having a short time horizon, may be an adaptive behavior. Another example of long-lasting environmental effects on plastic behaviors is the environmentally induced maternal effects. In rats, maternal stress can induce specific epigenetic markings, such as DNA methylation, in the brain cells of their offspring. This affects behavior and stress response of the offspring and can be transmitted to the next generation of offspring [START_REF] Champagne | Transgenerational effects of social environment on variations in maternal care and behavioral response to novelty[END_REF]. In this case, the behavioral plasticity can even be called transgenerational plasticity because it is somehow transmitted to the offspring over several generations. Maternal effects are a particular type of adaptive phenotypic plasticity because they overcome cues detection by the behavior to the offspring, who can therefore express this adaptive behavior even before experiencing the characteristics of the environment.

Behaviors are not only sensitive to early life circumstances, but also to environmental changes in adulthood. In this case, behavioral plasticity is often reversible. In the cichlid fish Astatotilapia burtoni, adult males have the ability to switch reversibly from a territorial, dominant phenotype to a cryptic and non-aggressive phenotype, depending on the cues they perceive from their social environment, especially from other males [START_REF] Burmeister | Rapid Behavioral and Genomic Responses to Social Opportunity[END_REF].

These behavioral changes are the result of hormonal changes. Environmentally induced differences in the endocrine system are among the underlying causes of phenotypic plasticity and may give rise to even more complex mechanisms. Male song sparrows Melospiza melodia morphna are territorial in both spring and fall. Their vernal territoriality is known to be testosterone-based, but not their autumnal one, as testosterone blockers inhibit vernal but not autumnal territoriality [START_REF] Wingfield | Testosterone and territorial behaviour in sedentary and migratory sparrows[END_REF]. [START_REF] Soma | Oestrogen regulates male aggression in the non breeding season[END_REF] have shown that territorial behavior is controlled by two different endocrine pathways depending on the season.

In spring, when these birds breed, testosterone is involved in breeding and territorial behavior.

In autumn, the endocrine pathway controlling territoriality changes to avoid a general production of testosterone that would trigger maladaptive breeding behavior in the bad season.

Many behaviors are plastic in response to a wide variety of environmental cues. These changes are sometimes reversible, sometimes more stable. This body of research, although initially from a non-human animal literature, has been applied to humans, and is a very promising avenue for understanding human diversity.

Phenotypic plasticity and behavioral diversity in the human species

The ecological factors that lead to the evolution of reaction norms in non-human animals should lead to similar selected reaction norms in the human species (even for behaviors) [START_REF] Galipaud | Adaptation and plasticity in life-history theory: How to derive predictions[END_REF]. In this thesis we use behavioral plasticity to explain the behavioral differences in social trust associated with variations in the socioeconomic status observed in human population. Of course, this does not preclude the idea that other mechanisms such as transmitted culture [START_REF] Richerson | Not By Genes Alone: How Culture Transformed Human Evolution[END_REF], genetic drift [START_REF] Keinan | Measurement of the human allele frequency spectrum demonstrates greater genetic drift in East Asians than in Europeans[END_REF] and local adaptation (for a review see [START_REF] Fan | Going global by adapting local: A review of recent human adaptation[END_REF]) are also relevant in explaining behavioral diversity.

The concept of socioeconomic status is used in a variety of fields ranging from economics and sociology to medicine and psychology. Socioeconomic status is a complex variable that refers to an individual's ranking in the economic and social hierarchy of her society. As an objective measure, it often takes into account variables such as education, occupation, wealth and income.

But there are also subjective measures of socioeconomic status, which involve individuals' perceptions of their rank in their society's income distribution or their own perception of their economic well-being.

socioeconomic status on their behaviors. More specifically, there are behavioral gradients, related to socioeconomic status [START_REF] Nettle | Social class through the evolutionary lens[END_REF], that Pepper and Nettle call the constellation of deprivation (Pepper & Nettle, 2017).

One of the behavioral gradients influenced by socioeconomic status concerns health. For example, a lower socioeconomic status is associated with more smoking [START_REF] Harrell | Smoking initiation in youth[END_REF][START_REF] Jarvis | Prevalence of hardcore smoking in England, and associated attitudes and beliefs: Cross sectional study[END_REF][START_REF] Kotz | Confronting smokers with previously undetected airflow limitation for smoking cessation[END_REF][START_REF] Lefevre | Social gradient in initiation and transition to daily use of tobacco and cannabis during adolescence: A retrospective cohort study: Tobacco and cannabis use during adolescence[END_REF][START_REF] Melotti | Adolescent Alcohol and Tobacco Use and Early Socioeconomic Position: The ALSPAC Birth Cohort[END_REF]) more use of illicit drugs and alcohol drinking [START_REF] Boyle | Smoking, drinking and use of illicit drugs among adolescents in Ontario: Prevalence, patterns of use and sociodemographic correlates[END_REF]; J. Z. [START_REF] Daniel | Is socioeconomic status in early life associated with drug use? A systematic review of the evidence: Socioeconomic status and drug use[END_REF][START_REF] Droomers | Educational differences in leisure-time physical inactivity: A descriptive and explanatory study[END_REF][START_REF] Lefevre | Social gradient in initiation and transition to daily use of tobacco and cannabis during adolescence: A retrospective cohort study: Tobacco and cannabis use during adolescence[END_REF][START_REF] Méjean | The contribution of diet and lifestyle to socioeconomic inequalities in cardiovascular morbidity and mortality[END_REF] poorer diet and less physical activity [START_REF] Brennan | Socioeconomic status and risk factors for obesity and metabolic disorders in a population-based sample of adult females[END_REF][START_REF] Droomers | Educational differences in leisure-time physical inactivity: A descriptive and explanatory study[END_REF][START_REF] Everson | Epidemiologic evidence for the relation between socioeconomic status and depression, obesity, and diabetes[END_REF][START_REF] Mclaren | Socioeconomic Status and Obesity[END_REF][START_REF] Panagiotakos | Dietary habits mediate the relationship between socio-economic status and CVD factors among healthy adults: The ATTICA study[END_REF] and with lower subjective expectation of life, less interest in their health or in the future, and an increased belief in the role of chance in determining health [START_REF] Wardle | Socioeconomic differences in attitudes and beliefs about healthy lifestyles[END_REF].

Socioeconomic status also affects reproductive behaviors and investment in offspring. People of lower socioeconomic status have children earlier and are more subject to teen pregnancies [START_REF] Smith | Socioeconomic differentials in wealth and health[END_REF][START_REF] Imamura | Factors associated with teenage pregnancy in the European Union countries: A systematic review[END_REF][START_REF] Johns | Perceived environmental risk as a predictor of teenage motherhood in a British population[END_REF][START_REF] Nettle | Dying young and living fast: Variation in life history across English neighborhoods[END_REF]. They also invest less in their offspring, with less breast-feeding [START_REF] Kohlhuber | Breastfeeding rates and duration in Germany: A Bavarian cohort study[END_REF] less reading to them [START_REF] Kiernan | Economic deprivation, maternal depression, parenting deprivation and child development[END_REF] and less interest in their education [START_REF] Hango | Parental investment in childhood and educational qualifications: Can greater parental involvement mediate the effects of socioeconomic disadvantage?[END_REF].

Interestingly, there is a more general concept that is also affected by socioeconomic status: time preferences. People of lower socioeconomic status have been found to discount future benefits more steeply than high socioeconomic status individuals [START_REF] Green | Temporal discounting in choice between delayed rewards: The role of age and income[END_REF]Harrison et al., 2002;Reimers et al., 2009). They are more impulsive, less future-oriented, and more pessimistic about their future than individuals of higher socioeconomic status [START_REF] Adams | Time perspective in socioeconomic inequalities in smoking and body mass index[END_REF][START_REF] De Wit | IQ and nonplanning impulsivity are independently associated with delay discounting in middle-aged adults[END_REF][START_REF] Robb | Socioeconomic Disparities in Optimism and Pessimism[END_REF]. This relationship between time preferences and socioeconomic status has also been found in non-WEIRD (Western, Educated, Industrialized, Rich, and Democratic) samples, such as in Vietnamese villages (Tanaka et al., 2010) and in Tsimané Amerindians [START_REF] Kirby | Correlates of delay-Amerindians of the Bolivian rain forest[END_REF].

Several factors have been explored to understand why socioeconomic deprivation is associated with these sub-optimal behaviors in contemporary societies. First, this could be due to a lack of information. If individuals do not know what is unhealthy or how to avoid it, this could explain why they continue to display these behaviors. However, using the example of teenage Indeed, a specially designed sex education program did not reduce conceptions or terminations by age 20 [START_REF] Henderson | Impact of a theoretically based sex education programme (SHARE) delivered by teachers on NHS registered conceptions and terminations: Final results of cluster randomised trial[END_REF]. Second, these behaviors could be explained by financial considerations. For example, the high cost of fruits and vegetables, especially organic ones, compared to industrial food, partly explains why poor individuals have a bad diet [START_REF] Darmon | Does social class predict diet quality?[END_REF]. But this explanation cannot apply to smoking [START_REF] Harrell | Smoking initiation in youth[END_REF][START_REF] Jarvis | Prevalence of hardcore smoking in England, and associated attitudes and beliefs: Cross sectional study[END_REF][START_REF] Kotz | Explaining the trying, but in the succeeding[END_REF][START_REF] Lefevre | Social gradient in initiation and transition to daily use of tobacco and cannabis during adolescence: A retrospective cohort study: Tobacco and cannabis use during adolescence[END_REF][START_REF] Melotti | Adolescent Alcohol and Tobacco Use and Early Socioeconomic Position: The ALSPAC Birth Cohort[END_REF], taking drugs or drinking [START_REF] Boyle | Smoking, drinking and use of illicit drugs among adolescents in Ontario: Prevalence, patterns of use and sociodemographic correlates[END_REF]; J. Z. [START_REF] Daniel | Is socioeconomic status in early life associated with drug use? A systematic review of the evidence: Socioeconomic status and drug use[END_REF][START_REF] Droomers | Educational differences in leisure-time physical inactivity: A descriptive and explanatory study[END_REF][START_REF] Lefevre | Social gradient in initiation and transition to daily use of tobacco and cannabis during adolescence: A retrospective cohort study: Tobacco and cannabis use during adolescence[END_REF][START_REF] Méjean | The contribution of diet and lifestyle to socioeconomic inequalities in cardiovascular morbidity and mortality[END_REF] which are costly behaviors also more prevalent in deprived areas.

Finally, the notion of selected phenotypic plasticity can provide new insights into this phenomenon. This is the hypothesis supported by (Pepper & Nettle, 2017). According to them the constellation of deprivation is the result of an adaptive response to an unpredictable environment where extrinsic mortality is high. The environmental characteristics explain why low socioeconomic status individuals are on average more short-term oriented than high socioeconomic status individuals.

Covarying traits, especially life history traits like reproduction, investment in offspring and survival have already been studied extensively in non-human animals. This research is based on the observation that at the species level, there is not a single global optimum but several local optima regarding life history traits. Depending on the characteristics of the environment, and more precisely on the characteristics of each ecological niche, the optimal life history strategy is not the same [START_REF] Szekely | The Mass-Longevity Triangle: Pareto Optimality and the Geometry of Life-History Trait Space[END_REF]. This results in different selection pressures between species for life history traits, such as number of litters per unit time, number of pups per litter, age at first litter, parental investment, etc. This is often known as r-and K-selection [START_REF] Macarthur | The Theory of Island Biogeography[END_REF][START_REF] Pianka | On r-and K-Selection[END_REF][START_REF] Reznick | R-AND K-SELECTION REVISITED: THE ROLE OF POPULATION REGULATION IN LIFE-HISTORY EVOLUTION[END_REF] body size. This idea is based on the fact that there are necessarily trade-offs in the allocation of resources to the different functions of an organism (Law, 1978;[START_REF] Stearns | Trade-Offs in Life-History Evolution[END_REF]. It is not possible for an organism to invest a lot in its soma, to have a large size and mass and at the same time to reach sexual maturity very quickly and produce a lot of offspring. For example, in an environment where resources are very scarce but there is little competition, it may be easier to survive if the organism has a low metabolic rate, a small body mass and a long development.

On the opposite, if resources are present but the competition for it is fierce, the best strategy could be to have a very quick growing at the beginning of development to have an advantage over the other newborns. Finally, if the predation pressure is high, especially on youngs, the strategy selected is often to invest more in the quantity of offspring than in their quality. All these studies concern between species comparisons. But what about at the scale of populations or individuals of the same species?

A first approach to answer this question is to determine if the within species variations are random or follow covariation patterns and if these covariations map ecological conditions. [START_REF] Réale | Personality and the emergence of the pace-of-life syndrome concept at the population level[END_REF] proposed that the pace-of-life syndrome theory, suggesting that closely related species in different environments should show a covariation between metabolic and life history traits depending on their environment, could be applied to within species differences and should include more behavioral traits. Evidence is mixed concerning the pace-of-life syndrome hypothesis but the importance of taking into account animal personalities in the understanding of ecological and evolutionary processes is now recognized [START_REF] Sih | Ecological implications of behavioural syndromes[END_REF][START_REF] Wolf | Animal personalities: Consequences for ecology and evolution[END_REF].

A similar body of research that focuses more on the trade-off between survival and reproduction and less on physiological markers, is the fast-slow continuum paradigm. This body of research comes from the same background as the pace-of-life syndrome : it is impossible to maximize all traits, thus there must be trade-offs in energy allocation, and these trade-offs can partially be understood using the r/K selection paradigm. The idea is that there is a continuum between a fast and a slow strategy [START_REF] Promislow | Living fast and dying young: A comparative analysis of life-history variation among mammals[END_REF]. The fast strategy is characterized, among others, by a prioritization of current reproduction over future reproduction and body maintenance, which results in quick growth, early and high reproduction but fast senescence and early death. The slow strategy is characterized by the opposite cluster of traits.

At the individual level, life history theory should operate through phenotypic plasticity in response to environmental cues (Sear, 2020).

Despite the observation of between individuals differences, that often map between species differences, the question of the extent to which one can apply originally between species to between individuals paradigms, is still to be answered. Therefore, some researchers suggest a middle-ground position on this question [START_REF] Galipaud | Adaptation and plasticity in life-history theory: How to derive predictions[END_REF]. One particular danger that needs to be avoided in answering this question, is the ecological fallacy (Del Giudice, 2020). It is the idea, that phenomena occurring at the individual level can be directly inferred from the one observed at the species level, which is often wrong. (Del Giudice, 2020) reviews several arguments supporting a careful transfer from between species level theories to within species studies. First, environmental characteristics often impose life history traits of individuals to covary in a certain way to be adaptive. Second, they are often evolutionarily and developmentally constrained, and functionally non-independent, such that several traits can only covary. Third, the basic trade-offs, like the one between current and future reproduction, shape the fitness landscape at all levels, from the individual to the species. To conclude on this question, (Del Giudice, 2020) argues that there are several arguments supporting the idea of certain traits covarying along a continuum. Whether this continuum is the broadest fast-slow continuum, whether it can be applied to individual differences in several species and whether environmental characteristics strongly or weakly shape life histories of individuals are still open questions. Nevertheless, a careful use of these theories, applied to the human species, can lead to useful insights on the causes of its diversity.

From the observation of covarying behaviors and in line with its application in non-human animals, life history theory has been applied to humans for many years (see [START_REF] Del Giudice | Rethinking the fast-slow continuum of individual differences[END_REF]Sear, 2020) for reviews). Researchers have often focused on trade-offs related to reproduction. First, reproduction, because it is directly related to fitness, appears as a good candidate for possible adaptive phenotypic plasticity. The general idea developed by researchers using the life history theory paradigm and focusing on reproduction is that there is a trade-off between reproduction and survival. Indeed, reproduction can divert energy to being allocated to body repair and maintenance [START_REF] Kirkwood | Evolution of Senescence: Late Survival Sacrificed for Reproduction[END_REF]. For example, [START_REF] Allal | An evolutionary model of stature, age at first birth and reproductive success in Gambian women[END_REF], studied the relationship between height, as a proxy of investment in the body, and age at first birth and child surviving. Comparing their model to actual data on age at first birth, that is highly correlated to reproductive success in their sample, they found that age at first birth can be modeled as a trade-off between the time spent growing and the survival of offspring.

Another example developed is demographic transition. As human populations grow more and more, due to better diet and less child mortality, competition increases. This increase in competition between offspring from different parents could explain why people gradually invest more and more in their children leading to a decrease in fertility, known as the demographic transition [START_REF] Mace | Evolutionary ecology of human life history[END_REF]. In line with the idea that competition matters in people decision-making, [START_REF] André | Does extrinsic mortality accelerate the pace of life? A barebones approach[END_REF] reformulate the theoretical background underlying the relationship between extrinsic mortality and early reproduction. The usual scheme is that people experiencing a high rate of extrinsic mortality reproduce earlier because they may die before if they wait too long (Pepper & Nettle, 2017). [START_REF] André | Does extrinsic mortality accelerate the pace of life? A barebones approach[END_REF], explain that this relationship is better explained by competition. When the extrinsic mortality rate is high, the population grows but cannot reach the saturation of the environment, therefore an adaptive response is to reproduce early to give its offspring an extra amount of time to reproduce when the population is still not too numerous and the competition not to fierce. When the saturation of the environment is complete, the competition is so fierce that the adaptive behavior is to invest a lot in each offspring to give it the best chances to be successful in the competition.

Interestingly, this is in line with the literature on r/K selection literature [START_REF] Reznick | R-AND K-SELECTION REVISITED: THE ROLE OF POPULATION REGULATION IN LIFE-HISTORY EVOLUTION[END_REF] and demographic transition [START_REF] Mace | Evolutionary ecology of human life history[END_REF]. Finally, [START_REF] André | Does extrinsic mortality accelerate the pace of life? A barebones approach[END_REF], acknowledge the fact that mortality is not the only cha behaviors: there are other mechanisms related to time horizon [START_REF] Mell | Time is money. Waiting costs explain why selection favors steeper time discounting in deprived environments[END_REF]) that can explain these behaviors.

There are strong debates within the community on whether life history theory research should focus on single trade-offs (e.g., current/future reproduction), clusters of related trade-offs (e.g., all reproduction related trade-offs: current/future reproduction, early/delayed reproduction, mating/parenting, quality/quantity of offspring, etc.) or on the entire fast-slow continuum (Del [START_REF] Del Giudice | Rethinking the fast-slow continuum of individual differences[END_REF]. The fast-slow continuum applied to humans is very similar to the r-K selections paradigm. Fast individuals mature and start reproduction early, have a high number of offspring but invest little in them and die young. Slow individuals take long to mature and start reproduction, produce few offspring and invest a lot in them and have a long lifespan. But this continuum also includes psychological traits with slow individuals being for example less materialistic, more optimistic or having a longer time horizon than fast individuals [START_REF] Griskevicius | When the Economy Falters, Do People Spend or 126 Save? Responses to Resource Scarcity Depend on Childhood Environments[END_REF]. In his review, (Del Giudice, 2020) warns against the systematic use of the fast-slow continuum. Even if the studied trait might fit into a fast-slow continuum, its relationship may be non-linear, and it may interact with several other traits.

(Sear, 2020) makes a similar critic to the use of the fast-slow continuum. As she describes it, life history research in humans is similar to the one in non-human animals in some ways and different in others. Of course, because it focuses on humans, within-species analysis is the only possible one. But there are several different approaches: researchers can either study trade-offs (clearly identifying the two traits at stake) or strategies (the idea that many different traits always covary) and they can either rely on environmental or on genetic basis to explain the differences we observe between individuals. According to the literature that shows little support to a clear strategy in life history traits variation [START_REF] Sheppard | Using sequence analysis to test if human life histories are coherent strategies[END_REF], Sear recommends focusing on well identified trade-offs rather than strategies and to rely on environmental rather than genetic underpinnings to explain them.

In this thesis, we place ourselves in this line, focusing on a specific trait and explaining its variations by a plastic response to environmental characteristics. The objective of my thesis is to use phenotypic plasticity and life history theory on another object of human behavior: social trust.

As the literature on poverty developed above might suggest, life history theory is not restricted to reproduction, survival, investment in offspring, etc. This paradigm can also be applied to more psychological and social traits [START_REF] Ellis | Fundamental 123 Dimensions of Environmental Risk: The Impact of Harsh versus Unpredictable Environments on the Evolution and Development of Life History Strategies[END_REF]. In particular, it has been suggested that variation in social trust can be construed as adaptive responses to certain environmental determinants. Testing this hypothesis requires to have both an accurate estimation of individuals' level of social trust, the relevant characteristics of individuals' environment and to be able to identify the key underlying psychological mechanisms.

How to study social trust as an adaptive response

to environmental characteristics?

Measuring social trust

Before searching for a psychological mechanism, the first step in studying social trust is measuring it in participants. As trust is a belief that has a central place in social decision-making processes, its consequences can be assessed in behaviors. Therefore, economic games can be used to evaluate the trust that participants place in other participants. The standard laboratory experiment to measure trust is the trust game of [START_REF] Berg | Trust, Reciprocity, and Social History[END_REF]. This experiment involves two participants. A certain amount of money is given to each of them. The first one can give all or part of his money to the second participant. If he does, then this money is tripled and given to the second participant. Then, the second participant has also the opportunity to give all or part of his money to the first one. If the first participant does not trust at all the second one, then she expects no money return from the second participant. Therefore, the logical strategy for her is to keep all the money. Conversely, if the first participant completely trusts the second one, then she expects the second participant to give back a big part of the money after it has been tripled. Therefore, the logical strategy is to give as much money as possible to receive a bigger amount in return. Therefore, the transfer from the first participant is interpreted as trust in the second participant. It could be argued that altruism plays a role in this economic game and could bias its results. To answer this question, [START_REF] Brülhart | Does the trust game measure trust?[END_REF]) used a design creating poor and rich participants. They found that, unlike what altruism would predict, trustors do not give more money to poor participants than to rich participants, they just give more money to participants they trust more. This trust game can be played as a single round (e.g., [START_REF] Brülhart | Does the trust game measure trust?[END_REF]) or as a sequence of rounds (e.g., [START_REF] Bourgeois-Gironde | Discriminating strategic reciprocity and acquired trust in the repeated trust-game[END_REF]. Players can have no information on the participant they are paired with (e.g., [START_REF] Berg | Trust, Reciprocity, and Social History[END_REF]) or, for instance, see an avatar chosen by the other participant to represent her ( (Tingley, 2014)). This economic game, among others, has been used extensively and in a variety of designs. However, its validity and the one of economic games in general, has been challenged by researchers. For example, [START_REF] Galizzi | On the External Validity of Social Preference Games: A Systematic Lab-Field Study[END_REF] showed that economic games are very poor at predicting self-reports and real-life behaviors. Economic games should be used carefully as the World Values Survey and the European Values Study to evaluate social trust. Nevertheless, there are also debates concerning the use of this exact question. In their review, [START_REF] Bauer | Measuring Trust (E. M. Uslaner[END_REF] suggest the use of specific situations involving different trustees to calculate an average value of generalized trust. Moreover, the World Values Survey also includes questions with 1-10 scales as answers: Would you say that people usually only take care of themselves or that they try to be helpful most of the time? and Do you think that most people would try to take advantage of you if they had the opportunity or that they would try to be fair? . Indeed, [START_REF] Lundmark | Measuring Generalized Trust: An Examination of Question Wording and the Number of Scale Points[END_REF] report that scales with several points outperform the dichotomous counterpart.

Understanding the psychological mechanism behind social trust (Chapter 2)

I line with the idea that social trust has a time component and is influenced by the perception of various risks and costs (such as waiting cost), temporal preferences appear as a good candidate mechanism to explain the link between resources and social trust.

Measuring temporal preferences

Temporal preferences are often measured via temporal discounting, that is the idea that things lose value with time. For example, receiving 5

. The actual value of these 5 receive them, than if one has to wait six months. Temporal discounting is universal, but its strength varies between individuals. Some people prefer 5

and others prefer the opposite. Measuring temporal discounting is a way to determine if people value more short-term benefits or long-term ones. Interestingly, prior research has demonstrated that temporal discounting measures in the lab were correlated with real life behaviors like, smoking, diet, physical exercise [START_REF] Chabris | Individual laboratory-measured discount rates predict field behavior[END_REF], credit card borrowing [START_REF] Meier | Present-Biased Preferences and Credit Card Borrowing[END_REF], age at first sexual activity and relationship infidelity (Reimers et al., 2009).

The first step in assessing temporal preferences is to measure indifference points. Considering that £70 are proposed at the long delay (like in (Reimers et al., 2009)). An indifference point corresponds to the amount of money offered in a fixed short delay that values the same for an individual as £70 proposed at a specific longer delay. This can be repeated at will for different long delays. When all the indifference points are identified, it is possible to draw the curve linking these points, and then to try to identify the function related to this curve.

To interpret the results of a temporal discounting task, curvilinear regression models are often [START_REF] Mazur | An adjusting procedure for studying delayed reinforcement. The Effect of Delay and of Intervening Events on Reinforcement Value: Quantitative Analyses of Behavior[END_REF]) [START_REF] Green | Temporal discounting in choice between delayed rewards: The role of age and income[END_REF] and [START_REF] Rachlin | Notes on discounting[END_REF]. These three models have slightly different functions to account for the hyperbolic shape of the temporal discounting function. If the number of indifference points measured is too low to fit a hyperbolic function, the area under the curve (AUC) can be used instead, as suggested by [START_REF] Frye | Measuring Delay Discounting in Humans Using an Adjusting Amount Task[END_REF].

Measuring economic resources

To study the association between individuals' level of resources and behavior, socioeconomic status is a very often used construct. Socioeconomic status has been used for decades in a variety of fields, for example in studies on obesity [START_REF] Mclaren | Socioeconomic Status and Obesity[END_REF], anxiety and depression [START_REF] Murphy | Depression and Anxiety in Relation to Social Status: A Prospective Epidemiologic Study[END_REF], parenting [START_REF] Hoff | Socioeconomic Status and Parenting[END_REF] or academic achievement [START_REF] White | The relation between socioeconomic status and academic achievement[END_REF]. This complex construct is supposed to reflect an i income, education or occupation. Several methods have been developed to assess it and they can be pretty complex [START_REF] Cirino | Measuring Socioeconomic Status: Reliability and Preliminary Validity for Different Approaches[END_REF] and country specific as the perception of some occupations may vary between countries.

It is important to note that socioeconomic status is not just an abstract value of income or education but also a osition in the society and (Pepper & Nettle, 2017).

Therefore, it is useful to study subjective socioeconomic status socioeconomic status. Interestingly, higher subjective socioeconomic status is associated with a decreased risk of developing a common cold (Cohen et al. 2008), and more generally with better health outcomes, even when accounting for income, education and occupation [START_REF] Präg | Subjective socioeconomic status and health in crossnational comparison[END_REF]. There are well validated measures of perception of oneself position in the society:

MacArthur Scale of Subjective Social Status (Adler et al., 2000) and [START_REF] Griskevicius | When the Economy Falters, Do People Spend or 126 Save? Responses to Resource Scarcity Depend on Childhood Environments[END_REF]) -item questionnaire.

Assessing social trust variations and its determinants in the past (Chapters 3, 4, 5)

Making the hypothesis that current social trust variations result from an evolved psychological mechanism, we should observe a similar association between resources and social trust in the past. However, testing this association in an historical perspective requires to adapt the tools used to estimate societies' level of social trust.

Cognitive fossils

To be able to observe variations of social trust in the past and look for determinants, the first challenge is to have access to the psychology of people in the past. Quite obviously, it is not possible to use the same measurement tools, i.e., questionnaires and economic games, as psychology, namely cultural items. Cultural items are

This has been shown for different types of cultural items as TV shows [START_REF] Fisher | Why Who Shot J. R. Matters: Dallas as the Pinnacle of Human Evolutionary Television[END_REF], romance novels [START_REF] Salmon | The Pop Culture of Sex: An Evolutionary Window on the Worlds of Pornography and Romance[END_REF], epic poems [START_REF] Gottschall | The rape of Troy: Evolution, violence, and the world of Homer[END_REF] as well as comedies and tragedies [START_REF] Nettle | The Wheel of Fire and the Mating Game: Explaining the Origins of Tragedy and Comedy[END_REF]. More specifically, (M. [START_REF] Costa | Aesthetic Phenomena as Supernormal Stimuli: The Case of Eye, Lip, and Lower-Face Size and Roundness in Artistic Portraits[END_REF] showed that there is a constant deviation from reality in artistic face representations. Artists tend to increase eyes size and roundness, and lip height and roundness and to decrease lower-face roundness: traits that are considered to increase attractiveness of faces. This means that painters do not directly represent reality but often enhance to meet the preferences of their public. From another perspective, it is also possible to assess the preferences of the public by measuring the success of certain works of art. For example, [START_REF] Morin | How portraits turned their eyes upon us: Visual preferences and demographic change in cultural evolution[END_REF] showed that the portraits most likely to appear in today's art books are those where the gaze is direct. This is probably due to the fact that direct-gaze faces are considered as more likable or attractive [START_REF] Conway | Evidence for adaptive design in human gaze preference[END_REF][START_REF] Ewing | Have you got the look? Gaze direction affects judgements of facial attractiveness[END_REF]. In this case, the portraits that have best survived the ages are those that better meet the preferences of informing us on their past psychology.

Cultural items have already been used to track historical psychological changes. For example, using theater plays, [START_REF] Martins | The rise of prosociality in fiction preceded democratic revolutions in Early Modern Europe[END_REF] found that cooperationrelated word use increased over-time. Interestingly, this rise precedes the English Civil War and the French Revolution, suggesting that cultural items can be used to assess psychological changes, even before they are translated in historical changes. Similarly, Safra et al. (Safra et al., 2020) have used portraits to track historical changes in facial representation as a proxy of people value in cultural items based on facial representation?

Faces as communication tools

Darwin [START_REF] Darwin | The Expression of the Emotions in Man and Animals[END_REF] already suggested in 1872, that facial expressions had two functions: first, preparing the organism to react to a situation and second, communicating emotions to others. In line wi e.g., [START_REF] Ekman | An argument for basic emotions[END_REF]) work, [START_REF] Shariff | What Are Emotion Expressions For?[END_REF] suggest that emotions expressions were first selected as physiological adaptations (for instance, disgust in [START_REF] Chapman | In Bad Taste: Evidence for the Oral Origins of Moral Disgust[END_REF] ) and were secondly selected as communication tools. In other words, facial expressions may have passed from emotional cues to emotional signals through a process of exaptation. This is true even if the face is fixed and does not necessarily express emotions.

This explains why a face elicits insuppressible inferences concerning its wearer. Our module of emotions detection activates automatically, and we cannot help but infer emotions from the characters, based on what they say and what they look like [START_REF] Asch | Forming impressions of personality[END_REF]. [START_REF] Willis | First Impressions: Making Up Your Mind After a 100-Ms Exposure to a Face[END_REF]) tested this idea using pictures. They found that a very quick exposure to a picture (less than 100 ms) was enough for participants to draw personality inferences. These ratings after minimal exposure highly correlated with ratings made without any time constraint.

We cannot help but make personality inferences when we look at a face, even for a very short time. Moreover, cross-cultural studies showed that individuals from different ethnic origins use similar cues for facial judgments [START_REF] Xu | Similarities and Differences[END_REF]. It is known for long that ratings of most faces are consensual [START_REF] Secord | Occupational and physiognomic stereotypes in the perception of photographs[END_REF]. Interestingly, there is also a consensus between children as young as 3 and adults [START_REF] Cogsdill | Inferring Character From Faces: A Developmental Study[END_REF], suggesting that there is a clear predisposition to judge people based on their facial appearance, that does not require a long social learning. It has even been found that congenitally and non-congenitally blind individuals but also sighted individuals produce the same facial expressions in response to emotions [START_REF] Matsumoto | Spontaneous facial expressions of emotion of congenitally and noncongenitally blind individuals[END_REF]. Matsumoto and Willingham also found no cultural differences between their participants. The cross-cultural universality of facial expressions is a key argument for their strong evolutionary roots. This theory is opposed to the one considering that emotions and their resulting expressions are socially construed, which would imply a high cultural variability [START_REF] Barrett | Emotional Expressions Reconsidered: Challenges to Inferring Emotion From Human Facial Movements[END_REF][START_REF] Nelson | Universality Revisited[END_REF]. But recent data tend to point in the direction of universality. In a recent paper, [START_REF] Cowen | Sixteen facial expressions occur in similar contexts worldwide[END_REF] studied 6 million videos from 12 regions subdivided in 144 countries and found that 70 % of the variance in the contextexpression association was preserved between these 12 regions. Similarly, Cowen and Keltner [START_REF] Cowen | Universal facial expressions uncovered in art of the ancient Americas: A computational approach[END_REF], showed that facial expressions like pain, determination, anger, elation and sadness expressed by ancient American sculptures were accurately perceived by Western participants.

Despite the early and universal tendency of inferring personality traits from faces, the accuracy of these inferences appears very limited. For example, no correlation was found between rated honesty from faces and actual honesty, derived from longitudinal personality measures (Zebrowitz et al., 1996). According to Todorov et al. [START_REF] Todorov | Social Attributions from Faces: Determinants, Consequences, Accuracy, and Functional Significance[END_REF], most studies that found a correlation between facial judgements and objective variables, as political affiliation [START_REF] Olivola | Republicans Prefer Republican-Looking Leaders: Political Facial Stereotypes Predict Candidate Electoral Success Among Right-Leaning Voters[END_REF], did not properly control for confounding factors that help in the political identification. Olivola et al. [START_REF] Olivola | Social attributions from faces bias human choices[END_REF] reexamined their results and found that the capacity of participants to accurately discern Republican from Democrat candidates was almost only driven by gender and ethnicity that are good predictors of political affiliation. Controlling e. As the critics against physiognomy personality. Therefore, their low relevance could be corrected by other information [START_REF] Todorov | Social Attributions from Faces: Determinants, Consequences, Accuracy, and Functional Significance[END_REF]: in ecological conditions, facial expressions are generally not the only source of information when trying to infer personality traits.

Perceived facial trustworthiness

Todorov et al. [START_REF] Todorov | Understanding evaluation of faces on social dimensions[END_REF] suggested that trait inferences could be represented within a 2D space defined by valence/trustworthiness and power/dominance evaluation of faces. The first axis (valence) is positively correlated with the perception of positive traits like trustworthiness and negatively correlated with negative traits like aggressiveness. The second axis (power) is positively correlated with judgements of dominance, confidence and aggressiveness. They also state that, evaluation of emotionally neutral faces is an overgeneralization of the mechanism for inferring emotional states, according to the facial cues used. To perform this study, Todorov et al. [START_REF] Todorov | Evaluating Faces on Trustworthiness After Minimal Time Exposure[END_REF] used avatar faces they created using the software FaceGen (Oosterhof & Todorov, 2008).

Judgements about trustworthiness are consensual and very quick [START_REF] Krumhuber | Facial dynamics as indicators of trustworthiness and cooperative behavior[END_REF][START_REF] Willis | First Impressions: Making Up Your Mind After a 100-Ms Exposure to a Face[END_REF], and again there is little evidence that trustworthiness perception predicts actual behavior [START_REF] Rule | Accuracy and consensus in judgments of trustworthiness from faces: Behavioral and neural correlates[END_REF]. Yet, they have consequences on how people behave towards whom they consider trustworthy or not. It has been shown that, during a trust game, 5year-olds, 10-year-olds and adults place their trust in partners that they consider trustworthy, based on facial cues [START_REF] Ewing | Perceived trustworthiness of faces drives trust behaviour in children[END_REF]. In another trust game experiment, where participants had to choose an avatar (Tingley, 2014), receivers who chose more trustworthy avatars were sent more money. These effects disappeared when avatars were not chosen but randomly assigned. In another concrete situation, negative trustworthiness judgements are also predictive of extreme criminal sentencing outcomes [START_REF] Wilson | Facial Trustworthiness Predicts Extreme Criminal-Sentencing Outcomes[END_REF]. For a review on the impact impressions from faces (especially trustworthiness) on choices, see [START_REF] Olivola | Social attributions from faces bias human choices[END_REF].

Overall, despite its low accuracy, perception of facial trustworthiness is universally used as a not.

Because people cannot help to infer personality from facial expressions, we can send a specific signal using this mechanism. In other words, we can make choices of what we want others to (Tingley, 2014) trust game experiment, participants voluntarily chose more often the most trustworthy avatars to represent them. Moreover, Mehu et al. [START_REF] Mehu | Smiles when sharing[END_REF] showed that Duchenne (spontaneous) smiles are more used than non-Duchenne smiles by participants in situations of sharing. They suggest that it is due to the fact that Duchenne smile is a signal advertising altruistic intention.

Finally, in Safra et al. [START_REF] Safra | Tracking historical changes in trustworthiness using machine learning analyses of facial cues in paintings[END_REF], the question of the reliability of measured perceived social trust was asked. Using the Selfiecity database [START_REF] Tifentale | Selfiecity: Exploring Photography and Self-Fashioning in Social Media[END_REF], the authors showed that the level of perceived facial trustworthiness measured in selfies correlated with the social trust level measured (by the World Values Survey) in the city they had been taken in. This indicates that cultural items, especial facial representations, are a reliable source of information on the psychology of the individuals who created them or are represented on them.

The same happens for faces represented on cultural items, even if they are not real faces but just representations. Whether it is a picture, an avatar or a drawn or painted portrait does not change much the fact that the observer detects the activation of facial muscles and infers emotions on personality traits from it.

Cultural items representing faces have already been used in several studies. In computer vision and machine learning, Salem et al. [START_REF] Salem | Analyzing human appearance as a cue for dating images[END_REF], showed that facial characteristics like glasses, clothing or hair style can be used to accurately predict when a photo was taken. Ginosar et al. [START_REF] Ginosar | A Century of Portraits: A Visual Historical Record of American High School Yearbooks[END_REF], added the study of smiling to date photograph portraits of yearbooks. They found that students smiled more and more over time. Finally, [START_REF] Cowen | Universal facial expressions uncovered in art of the ancient Americas: A computational approach[END_REF], showed that cultural items, as sculptures, can be used to study facial expressions. In line with the fact that they are universal, they found that facial expressions like pain, determination, anger, elation and sadness expressed by ancient American sculptures were accurately perceived by Western participants.

To sum up, the goal of the thesis is to explore the relationship between economic resources and social trust. To do so, on the one hand we rely on current data from current participants to study psychological mechanisms. On the other hand, we strive to access the psychology of people from the past.

In the second chapter of this thesis, we will focus on the role of temporal discounting in the association between social trust and level of resources. Social trust and income are associated both within and across countries, such that higher income typically correlates with increased trust. While this correlation is well-documented, the psychological mechanisms sustaining this relationship remain poorly understood. The potential mediating effect of temporal discounting on the relationship between resources and social trust will be tested experimentally.

The object of the third chapter is to assess whether the link between social trust and the level of resources is also present in the past, as it should be the case if this association is mediated by cognitive mechanisms. We will use cultural items, namely pictures of French MPs, as a proxy of the psychology of people from the past and see if it correlates with economic data. Indeed, politicians pay attention to their image, and their face is a key part of how people see them. The face they display can inform us on how they want to be seen. More specifically, the attributes they try to show through their face, instructs us on what is valued by the people.

In the fourth chapter, we will use similar methods but on much older data, namely painted portraits of European sovereigns. This will allow us to test whether what we observe in twentieth-century photographs can be generalized to older faces and to another type of cultural item: painted portraits. What is similar with the previous chapter is that we still have leaders here even though their mode of accession to power is obviously different from that of deputies.

The fifth chapter will be the closest to a replication of the article by [START_REF] Safra | Tracking historical changes in trustworthiness using machine learning analyses of facial cues in paintings[END_REF]. Indeed, we will use a dataset constituted by a museum which gathers portraits which are not all representing the same type of person. On the other hand, they probably correspond much more to the tastes of aristocrats. The interest is that there are very high-quality data since the portraits were digitized by the curators of the Château de Versailles.

In the sixth chapter we will discuss our results in the light of the literature, review the limitations of our studies and suggest future research directions.

Abstract

Social trust and income are associated both within and across countries, such that higher income typically correlates with increased trust. While this correlation is well-documented, the psychological mechanisms sustaining this relationship remain poorly understood. One plausible candidate is people's temporal discounting: on the one hand, trust has a strong time component -it exposes the individual to immediate costs in exchange of uncertain and delayed benefits; on the other hand, temporal discounting is robustly influenced by income. The goal of our studies was to test whether temporal discounting mediates the relationship between income and trust and whether experimentally manipulating perceived income has a downstream impact on temporal discounting and trust. To do so, participants who underestimated their relative income position received information about their true position in the income distribution in order to correct their misperception. Our results indicate that temporal discounting partially mediates the effect of income on social trust in a pre-registered online study on British participants (N = 855). However, receiving a positive information shock on one's income position had no impact on either temporal discounting or social trust. In a second pre-registered study, we replicated the finding that temporal discounting partially mediates the effect of income on social trust in a representative sample of the British population (N = 1130).

Social trust can be defined as the belief that other people are reliable cooperation partners. When one decides to take part in a cooperative interaction, it makes sense to invest in the interaction to the extent that one can trust that the cooperation partner will provide an adequate return on investment. This return can take the form of later reciprocation or of a reputational gain.

Formally then, social trust is the belief that most people will reciprocate -offer a return on investment -if one invests in a social interaction with them.

Introduction

Social trust is a belief central to decision-making processes and it affects a range of prosocial behaviors like cooperation in situations of conflict of interests [START_REF] Balliet | Trust, conflict, and cooperation: A meta-analysis[END_REF], acceptance of vaccination [START_REF] Rönnerstrand | Social capital and immunisation against the 2009 A(H1N1) pandemic in Sweden[END_REF], or cooperation in social dilemmas with a large number of actors [START_REF] Mannemar Sønderskov | Explaining large-N cooperation: Generalized social trust and the social exchange heuristic[END_REF]. At the societal level, social trust is linked to a host of positive outcomes, such as improved economic growth (Bjørnskov, 2017;[START_REF] Knack | Does Social Capital Have an Economic Payoff? A Cross-Country Investigation[END_REF]. But despite its positive outcomes, social trust varies considerably between individuals and societies [START_REF] Albanese | Who trusts others more? A cross-European study[END_REF]. Why is this the case? Do variations in social trust follow a predictable pattern? Can these variations be traced back to specific psychological and environmental features?

One factor explaining the variability of social trust is that it is sensitive to socioeconomic status.

Existing works suggest that childhood socioeconomic circumstances [START_REF] Mell | Early life adversity is associated with diminished social trust in adults[END_REF][START_REF] Stamos | Investigating the effect of childhood socioeconomic background on interpersonal trust: Lower childhood socioeconomic status predicts lower levels of trust[END_REF] as well as current income [START_REF] Alesina | Who trusts others?[END_REF] are positively associated with social trust. [START_REF] Brandt | Changes in Income Predict Change in Social Trust: A Longitudinal Analysis: Socioeconomic Status and Trust[END_REF] also showed with longitudinal data, that increases in socioeconomic status predict increases in social trust, and that this model is more viable than a model using increases in social trust to predict increases in socioeconomic status. Similarly, exogenous negative shocks on childhood resources have an impact on adult levels of social trust. Using data from Germany after the second World War, Hörl et al. (Hörl et al., 2016) showed that children exposed to more hunger during childhood had lower levels of trust as adults. Petersen and Aarøe [START_REF] Petersen | Birth Weight and Social Trust in Adulthood: Evidence for Early Calibration of Social Cognition[END_REF] even found an effect of lower birth weight on social trust in adulthood.

While the association between social trust and socioeconomic status is robustly established, the psychological mechanism by which this takes place is not known. An interesting characteristic of social trust is its temporal component, underlined by researchers from several fields, such as education [START_REF] Verducci | Social Trust[END_REF], sociology [START_REF] Sztompka | Trust: A sociological theory[END_REF] or psychology and neuroscience [START_REF] Krueger | Toward a Model of Interpersonal Trust Drawn from Neuroscience, Psychology, and Economics[END_REF], who agree on the future-oriented character of social trust. For instance, Krueger and Meyer-Lindeberg [START_REF] Krueger | Toward a Model of Interpersonal Trust Drawn from Neuroscience, Psychology, and Economics[END_REF] define social trust as accepting one's vulnerability towards one's expectation regarding the behavior of another party that will produce some positive outcome in the future . Social trust is based on reciprocity [START_REF] Krueger | Toward a Model of Interpersonal Trust Drawn from Neuroscience, Psychology, and Economics[END_REF] and, thus is associated with certain and immediate costs and with uncertain and delayed benefits.

Therefore, one possibility is that social trust reacts to individual variations in temporal preferences, which are themselves affected by socioeconomic status. People with short-term preferences should therefore be less prone to investing in social interactions, for which the return on investment is delayed. In terms of behaviors, the trade-off would be between choosing the sooner smaller reward of defecting over the later larger reward offered by reciprocation from the interaction partner or a reputational gain. In a recent paper, Pepper and Nettle (Pepper & Nettle, 2017) suggested that the varying length of time-horizon between individuals could explain several socioeconomic gradients in social behaviors, including financial decisions [START_REF] Falk | The Nature and Predictive Power of Preferences: Global Evidence[END_REF][START_REF] Howlett | The Role of Self-Regulation, Future Orientation, and Financial Knowledge in Long-Term Financial Decisions[END_REF], educational attainment [START_REF] Falk | The Nature and Predictive Power of Preferences: Global Evidence[END_REF], academic engagement and performance [START_REF] Brown | The Substance of Things Hoped for: A Study of the Future Orientation, Minority Status Perceptions, Academic Engagement, and Academic Performance of Black High School Students[END_REF], smoking [START_REF] Adams | Commentary] Time for a change of perspective on behaviour change interventions?[END_REF] or eating behaviors [START_REF] Adams | Time perspective in socioeconomic inequalities in smoking and body mass index[END_REF].

In this paper, we apply this logic to social trust: our first hypothesis is that temporal discounting is a key psychological mechanism by which socioeconomic status impacts social trust. In other words, we hypothesize that temporal discounting mediates the relationship between socioeconomic status and social trust.

The link between temporal discounting and social trust has only been tested in a couple of studies, with mixed findings. A first study, conducted in Vietnam, did not show any effect of temporal discounting on trust, using a trust game [START_REF] Nguyen | Trust and Trustworthiness Under the Prospect Theory: A Field Experiment in Vietnam[END_REF] but this null effect might be due to a lack of statistical power (N = 156 only). A second study found that temporal discounting is an important covariate of self-reported trust [START_REF] Albanese | Trust, risk and time preferences: Evidence from survey data[END_REF] but in this study, temporal discounting was measured with a single question, which limits the accuracy of the temporal discounting estimate. Our experimental design tackles both limitations and allows us to test whether variations in temporal discounting are associated with variations in social trust inside a mediation model. To avoid the pitfalls of the two studies above-mentioned we used well-validated measures of time discounting and social trust and we made sure to have sufficient statistical power.

Moving beyond correlations, the goal of our paper is also to study the causal structure of this association by experimentally manipulating perceived income. Income [START_REF] Green | Temporal discounting in choice between delayed rewards: The role of age and income[END_REF]Harrison et al., 2002;Reimers et al., 2009) and relative deprivation [START_REF] Callan | Personal relative deprivation, delay discounting, and gambling[END_REF][START_REF] Tabri | Me, Myself, and Money II: Relative Deprivation Predicts Disordered Gambling Severity via Delay Discounting, Especially Among Gamblers Who Have a Financially Focused Self-Concept[END_REF] are indeed well-known drivers of temporal preferences. Economists typically measure temporal preferences using delay discounting experiments in which participants are asked to choose between a smaller but sooner reward -for example $2 tomorrow -and a larger later reward -for example $5 in one week. In these types of experiments, lower socioeconomic status individuals consistently display steeper temporal discounting [START_REF] Green | Temporal discounting in choice between delayed rewards: The role of age and income[END_REF]Harrison et al., 2002;Reimers et al., 2009). Less educated adolescents, compared to adolescents with higher education, also show the same pattern (N. C. [START_REF] Lee | Age and educational track influence adolescent discounting of delayed rewards[END_REF]. Callan et al. [START_REF] Callan | Personal relative deprivation, delay discounting, and gambling[END_REF] even found that an experimental manipulation of personal relative deprivation had less income than similar others displayed higher temporal discount rates. In this context, our second hypothesis is that inducing fluctuations in temporal discounting by manipulating perceived relative income should impact social trust.

To manipulate socioeconomic status, we capitalize on work demonstrating that many people underestimate their wealth and that it is possible to correct these misperceptions, with measurable effects on attitudes and behaviors. For instance, faulty beliefs about inequalities are common in the US (Norton & Ariely, 2011) or in Australia [START_REF] Norton | The Not-So-Common-Wealth of Australia: Evidence for a Cross-Cultural Desire for a More Equal Distribution of Wealth: The Not-So-Common-Wealth of Australia[END_REF]. In both countries, people underestimate the level of wealth inequality in their country. The same occurs [START_REF] Karadja | George Mason University Department of Economics Working Paper[END_REF] or in Argentina [START_REF] Cruces | Biased Perceptions of Income Distribution and Preferences for Redistribution: Evidence from a Survey Experiment[END_REF]. In these countries, people underestimate their relative position in their country's income distribution. These misperceptions can be explained by t [START_REF] Hauser | Mis)perceptions of inequality[END_REF].

Interestingly, studies have shown that providing economic information (for instance, about inequalities or income distribution) as an experimental treatment can have an impact on [START_REF] Cruces | Biased Perceptions of Income Distribution and Preferences for Redistribution: Evidence from a Survey Experiment[END_REF][START_REF] Karadja | George Mason University Department of Economics Working Paper[END_REF] or tolerance for inequalities [START_REF] Kuziemko | How Elastic Are Preferences for Redistribution? Evidence from Randomized Survey Experiments[END_REF]. In this paper we apply a similar method in order to test whether experimentally manipulating perceived income has a downstream impact on temporal discounting and trust.

To test our hypotheses, we designed two online studies. In study 1, we tested a causal mediation model with time discounting mediating the effect of socioeconomic status on social trust. To do so, we used an experimental design by randomly informing a subsample of participants about their true relative income position in the British society. Since objective socioeconomic status cannot be easily manipulated experimentally (except by giving people extra income), we focused on the way people perceive their resource level (i.e., their perceived relative income).

Specifically, we corrected people's perception of their income when they felt that their relative income position was lower than it actually was. In other words, the goal of our intervention was to provide a positive psychological income shock . Our main question was whether correcting people's misperception of their relative income would influence temporal discounting and as a consequence, social trust. We hypothesized that respondents who learnt that they had a higher relative income than they thought (positive income shock) would discount future benefits less, and that as a result they would display more social trust. The experiment targeted only participants who hold wrongly negative beliefs about their position in society. For ethical reasons, participants who believed they had a higher relative income than they actually had, did not receive any correction. We also studied this mediation pathway with a correlational approach testing if the effect of being of low or high socioeconomic status on social trust was mediated by temporal discounting.

In study 2 we replicated our results from study 1, particularly the mediating role of temporal discounting on the relationship between socioeconomic status and social trust and between time discounting and social trust. In this study we did not test any treatment, data were only observational. This replication was made on a sample representative of the British population.

Study 1 2.1 Methods

Participants

Participants were recruited online via Prolific Academic (https://www.prolific.co). We used -screening criteria to filter participants by nationality and approval rate. We recruited only British participants with a minimum approval rate of 90 %. Participants were excluded if they reported a personal monthly income above £12,500, which corresponds to the highest tax bracket and top earning percentile in the UK, in order to minimize potential income reporting mistakes. We also excluded students because their reported income may not reflect their actual living standards (e.g., students may report no income but receive parental support).

To determine sample size, we set an a priori power level and a minimum detectable effect that the intervention was expected to have on temporal discounting [START_REF] Djimeu | Power calculation for causal inference in social science: Sample size and minimum detectable effect determination[END_REF].

We used the formula recommended by Bloom [START_REF] Bloom | The Core Analytics of Randomized Experiments for Social Research[END_REF]:

Where MDE = Minimum Detectable Effect; N = Number of observations; = standard deviation of effect; P = proportion of treated (50 %); = 1.96 (using a two-tailed hypothesis at the .05 significance level); = 0.84 (setting statistical power to 80 %). We based our power calculation on measures of temporal discounting taken in a British sample [START_REF] Grandin | Socioeconomic status, time preferences and pro-environmentalism[END_REF], see general appendix). We found that we needed 807 participants to identify a standardized minimum detectable effect of 0.2. To compensate for attrition, we recruited 888 participants, which corresponds to 807 plus 10 % attrition. The study used for the power analysis ( [START_REF] Grandin | Socioeconomic status, time preferences and pro-environmentalism[END_REF], see general appendix) did not contain any question on social trust. Therefore, we had no means of knowing whether a study with 888 participants was sufficiently powered to detect an effect of an income shock on social trust mediated by temporal discounting.

Materials

The questionnaires and discounting task were presented using Qualtrics (https://www.qualtrics.com).

Socioeconomic status. We included two measures of socioeconomic status: an objective measure and a subjective measure. Objective socioeconomic status refers to income and education. We asked respondents about their personal income rather than their household overall income, because studies have shown that questions about household income induce lower response rates and produce lower quality data (Micklewright & Schnepf, 2010).

Participants were asked to report their personal monthly income in a free-text box in order to avoid unintentional priming effects that come with the use of income brackets (Haisley et al., 2008). Moreover, in order to minimize reporting mistakes such as reporting yearly instead of monthly income, or unintentionally typing an additional zero, we calculated their annual income based on the monthly income they reported and asked them to confirm that it corresponded to their actual earnings. In addition to personal income, we asked participants to specify which

We acknowledge that it is not only income or occupation that generate differences in behavior, but the experience of various deprivations that are often associated with being of lower socioeconomic status (Pepper & Nettle, 2017). Therefore, we also computed a measure of perceived relative affluence (subjective socioeconomic status) combining the MacArthur Scale of Subjective Social Status (Adler et al., 2000) and [START_REF] Griskevicius | When the Economy Falters, Do People Spend or 126 Save? Responses to Resource Scarcity Depend on Childhood Environments[END_REF] three-item questionnaire.

of this scale as representing where people stand in the United Kingdom. At the top of the scale [START_REF] Cogsdill | Inferring Character From Faces: A Developmental Study[END_REF] are the people who are the best off in terms of overall income. At the bottom ( 1) are the people who are the worst off in terms of overall income. Where would you place yourself in vicius et al.'s [START_REF] Griskevicius | When the Economy Falters, Do People Spend or 126 Save? Responses to Resource Scarcity Depend on Childhood Environments[END_REF] three-instead on the area under the curve (AUC) [START_REF] Frye | Measuring Delay Discounting in Humans Using an Adjusting Amount Task[END_REF]. This measure carries partial information, because different curve shapes can result in the same area under the curve. We focused on AUC because our main goal is to estimate between-participants differences in temporal discounting steepness rather than to precisely model participants' discounting curves.

Using area under the curve was therefore an optimal choice considering the trade-off between measurement precision and study duration.

Social Trust. Social trust is a complex behavior, which is difficult to fully capture in a brief study, but studies suggest that questionnaires are often better than economic games in predicting real-life behaviors [START_REF] Galizzi | On the External Validity of Social Preference Games: A Systematic Lab-Field Study[END_REF][START_REF] Laury | Altruism spillovers: Are behaviors in context-free experiments predictive of altruism toward a naturally occurring public good? Law, R[END_REF][START_REF] Voors | Exploring whether behavior in context-free experiments is predictive of behavior in the field: Evidence from lab and field experiments in rural Sierra Leone[END_REF]. Here, we focused on three questions that are routinely used in national and international ld you say that most try to take advantage of you if they had the -10 scale was used for the second and third questions.

Procedure

Participants started with questions about socioeconomic status and demographics. We used these answers to calculate how much our participants underestimated or overestimated their relative position in the British income distribution. We calculated the sum of their reported income and taxable benefits to determine which decile they belonged to. We used statistics (https://www.gov.uk/government/collections/personal-incomes-statistics) as a reference to annual income (comprising taxable benefits). We compared this decile to their answer to the MacArthur scale, so as to estimate how much participants had a biased perception of their position in the income distribution. We defined the bias of a participant as the difference between her perceived and actual income decile. Participants who underestimated (/overestimated) their relative income by 1 decile point or more were categorized as having a negative (/positive) bias. The remaining participants were defined as having no bias. The survey stopped there for participants who estimated their position accurately or overestimated it (null or positive bias). Those who underestimated their position (negative bias) were randomly assigned to the experimental or control condition. In the experimental condition, participants were presented with a correction of their misperception of relative income, based on their reported income and taxable benefits. These participants therefore received a positive income shock. Participants then completed the temporal discounting task. Next, they answered questions on environmental attitudes for another study. Questions on social trust came last.

Data treatment

All data treatment and analyses were carried out in R. They were all pre-registered (see:

https://osf.io/pyj24). The only deviation from the pre-registration plan is that the exclusion for reaction times was applied at the single trial level. In what follows, we focus on the mediation analyses.

Exclusion of participants. Participants were excluded from all analyses if they failed one or more catch trials in the temporal discounting task. They were also excluded if they responded too fast (< 500 ms) or too slowly (> 2 minutes) to single question screens or if they responded too fast (< 3 s) or too slowly (> 5 minutes) to the other survey pages (with more than one question per screen). We used these filters to make sure that participants were attentive and read the questions carefully. This left a sample of 855 participants (427 participants in the control group and 428 in the treated group), with 442 women. Ages ranged 21 to 72, M = 39.76, SD = 10.46.

Socioeconomic status. We monthly income and benefits. Education level was transformed from an unordered categorical sum of z-scored total income and z-scored education level. Subjective socioeconomic status was the sum of z-scored items Griskevicius et al.'s [START_REF] Griskevicius | When the Economy Falters, Do People Spend or 126 Save? Responses to Resource Scarcity Depend on Childhood Environments[END_REF] questionnaire and MacArthur scale.

Information shock (treatment) corresponds to a binary variable indicating whether a participant was in the control group or the treated group.

Temporal discounting. To calculate individual temporal discount rates, we isolated the indifference point for each delay and for each participant and then calculated the area under the discounting curve linking these points (Myerson et al., 2001). The AUC between two points on the curve is calculated as (x2 -x1) * [(y1 + y2) / 2], where x1 and x2 are the successive delays and y1 and y2 are the indifference points for those delays. Then the AUC between 3 weeks and 3 months and the one between 3 months and 2 years were summed, resulting in a single value of AUC per participant. Lower values of AUC indicate steeper discounting [START_REF] Frye | Measuring Delay Discounting in Humans Using an Adjusting Amount Task[END_REF].

Social Trust. A general trust binary variable was constructed from the first question ( Generally dealing with people?

-10 scale was used for the two additional questions ( Would you say that people usually only take care of themselves or that they try to be helpful most of the time? and Do you think that most people would try to take advantage of you if they had the opportunity or that they would try to be fair? ). The answers obtained to these three questions were z-

Statistical analyses

Correlational Mediation model

Our first main hypothesis (H1) is that the relationship between social trust and socioeconomic status is mediated by individual discount rates. To test the mediation model, we used the causalsteps procedure described by Baron and Kenny (Baron & Kenny, 1986). Following recommendations from Miller et al. [START_REF] Miller | Mediation in Strategic Management Research: Conceptual Beginnings, Current Application, and Future Recommendations[END_REF] and Aguinis et al. [START_REF] Aguinis | Improving Our Understanding of Moderation and Mediation in Strategic Management Research[END_REF], we tested the significance of the indirect pathway with the R mediation package [START_REF] Tingley | mediation: R Package for Causal Mediation Analysis[END_REF] using the function mediate. Despite the name of this package: R Package for Causal Mediation Analysis , the mediation analysis described in this section is not causal because it is not based on a treatment but only on observational data. In the present case, the treatment variable (i.e., the treat argument in the mediate function) is socioeconomic status (alternatively objective or subjective).

Causal mediation model

Our second main hypothesis (H2) is that the effect of the positive information shock on social trust is mediated by individual discount rates. To test this hypothesis, we used the same steps as for H1 but with the binary variable treatment (0: control, 1: treated) as the treatment variable (i.e., the treat argument in the mediate function).

Results

For descriptive statistics, see Table 1. For a graph of temporal discounting curves, see Figure 1.

Causal mediation model

To test our second main hypothesis (H2) that the effect of the income shock on social trust is mediated by individual discount rates, we used the same steps as for H1 but with the binary variable treatment (0: control, 1: treated) as the treatment variable (i.e., the treat argument in the mediate function).

The mediation analysis shows no direct effect of the income shock on social trust and no mediation (Average Causal Mediating Effect of temporal discounting: b = 0.001, p = .85, Proportion of the total effect that is mediated: -2.5 %, p = .95) (see table 4).

Discussion

effect on their temporal discounting and social trust. There are several possible reasons explaining this outcome. First, participants may not have believed the information we provided.

We specified that the income statistics come from GOV.UK and are based on the whole British population above 18 (taxpayers only) but this may not have been enough to convince participants that the information was accurate. Given that we did not ask a debriefing question to probe participants' confidence in the income correction, we have no way of knowing whether this was indeed an issue. Second, a correction of people's misperception of their relative income may not be a powerful enough treatment to affect temporal discounting or social trust. In line with this idea, Haushofer et al. (Haushofer et al., 2013) found that negative income shocks had a larger effect on temporal discounting than positive income shocks. Karadja et al. [START_REF] Karadja | George Mason University Department of Economics Working Paper[END_REF], on the other hand, did find an effect of positive income shocks but in a sample that was mostly negatively biased, unlike our sample, which was mostly positively biased. To sum up, we replicated the known correlations between socioeconomic status and temporal discounting on the one hand, and between socioeconomic status and social trust on the other hand. More interestingly we found that temporal discounting and social trust were correlated and that the effect of socioeconomic status on social trust was partially mediated by the temporal discounting. However, our study sample was very specific. Specifically, participants were excluded if they overestimated their relative income, which may limit the generalizability of our findings. Therefore, we decided to replicate our main results on a representative sample of the British population.

Study 2

In this study, our goal was to replicate the mediating effect of temporal discounting on the relationship between socioeconomic status and social trust, on a representative sample of the British population. The sample tested in Study 1 is indeed not representative because we only included participants who received an income and held wrongly negative beliefs about their relative affluence.

Methods

Participants

Participants were recruited online via Prolific Academic (https://www.prolific.co). Our final sample included a representative sample of 1188 participants, stratified across three demographics: age, sex and ethnicity via the representative sample option in Prolific Academic (including 10 % more participants for attrition). Sample size was determined a priori based on [START_REF] Schoemann | Determining Power and Sample Size for Simple and Complex Mediation Models[END_REF].

Materials

We used the same materials as in study 1.

The questionnaires and discounting task were presented using Qualtrics (https://www.qualtrics.com).

Procedure

Participants started with questions about socioeconomic status and demographics. Then, they completed a temporal discounting task, and questions on social trust came last. All the measures were the same as those included in study 1.

Data treatment

All data treatment and analyses were carried out in R and were pre-registered (see preregistration of study 1 https://osf.io/pyj24 and replication pre-registration: https://osf.io/4cjkh).

As in study 1, the only deviation from the pre-registered analysis plan is that the exclusion for reaction times was applied at the single trial level. As in study 1, we focus on the mediation analyses.

Exclusion of participants. The same criteria were applied as those in Study 1. This left a sample of 1130 participants with 585 women, 543 men and 2 others. Ages ranged 19 to 87, M = 45.56, SD = 15.31.

Temporal discounting. To calculate individual temporal discount rates, we followed the same method as in Study 1.

Social Trust. As in Study 1, the answers obtained to the 3 questions about social trust were zprosociality.

Statistical analyses

Correlational Mediation model. Our main hypothesis (H1) is that the relationship between social trust and socioeconomic status is mediated by individual discount rates. To test this hypothesis we conducted regression analyses relying on the causal-steps procedure set forth by Baron and Kenny (Baron & Kenny, 1986) to detect a mediation effect, following recommendations from Miller et al. [START_REF] Miller | Mediation in Strategic Management Research: Conceptual Beginnings, Current Application, and Future Recommendations[END_REF] and Aguinis et al. [START_REF] Aguinis | Improving Our Understanding of Moderation and Mediation in Strategic Management Research[END_REF].

We tested the significance of the indirect pathway with R mediation package [START_REF] Tingley | mediation: R Package for Causal Mediation Analysis[END_REF] using the function mediate. As in study 1, we set two values (control.value and treat.value) of the treatment to make the contrast.

Results

For descriptive statistics, see table 5. Correlational Mediation model.

To test our main hypothesis (H1) we conducted regression analyses relying on the causal-steps procedure set forth by Baron and Kenny (Baron & Kenny, 1986) to detect a mediation effect.

The results suggest a partial mediation: the effect size of both objective and subjective socioeconomic status is lowered by the addition of temporal discounting in the model (see table 6).

To test the significance of the mediation pathway we used R mediation package [START_REF] Tingley | mediation: R Package for Causal Mediation Analysis[END_REF] using the function mediate. We tested again two mediation pathways: one with Objective socioeconomic status and one with Subjective socioeconomic status. In both cases, the positive effect of socioeconomic status on social trust is partially mediated by temporal discounting (with Objective socioeconomic status: Average Causal Mediating Effect of temporal discounting: = 0.04, p < .001, Proportion of the total effect that is mediated: 28 %, p = .002;

with Subjective socioeconomic status: Average Causal Mediating Effect of temporal discounting: = 0.05, p < .001, Proportion of the total effect that is mediated: 13 %, p < .001).

These values are calculated for control.value = -0.

Discussion

Using a correlational mediation model, we found that the relationship between socioeconomic status and social trust is mediated by individual discount rates. This replication on a sample stratified across three demographics: age, sex and ethnicity, confirms the results obtained in study 1.

General discussion

The two studies reported in this paper investigate the link between socioeconomic status and social trust. Our goal was to identify a proximate psychological mechanism to explain this relationship. Our hypothesis was that temporal discounting plays an important role in the association between socioeconomic status and social trust. In line with this idea, we found a negative correlation between temporal discounting and social trust in two different samples, including a representative sample of the British population. In addition, we found that temporal discounting partially mediated the effect of socioeconomic status on social trust. The mediation scheme brought to light is an important step in the identification of proximate mechanisms explaining the relationship between socioeconomic status and behaviors like social trust.

The link between temporal discounting and social trust provides a new understanding of how social trust varies across individuals. Our results are in line with the idea that social trust is modulated by long-term expectations and that it is therefore sensitive to temporal discounting.

One possible explanation is that when someone has to decide whether or not to invest in a social interaction, their temporal preferences influence how much they are willing to wait in order to receive a return on their social investment. This is in line with the scientific literature linking temporal discounting and social behaviors, which suggests that several socioeconomic gradients in social behaviors can be traced back to variations in temporal preferences (Pepper & Nettle, 2017).

This study and its replication are the first to combine a multi-facet measure of socioeconomic status, a precise measure of temporal discounting (that involves more than a single choice), and an explicit measure of social trust. In addition, both our studies are pre-registered, we made sure that we had sufficient power to test our hypotheses and we used a representative sample in the replication study. For those reasons, our results make an important contribution to the limited literature on this topic [START_REF] Albanese | Trust, risk and time preferences: Evidence from survey data[END_REF][START_REF] Nguyen | Trust and Trustworthiness Under the Prospect Theory: A Field Experiment in Vietnam[END_REF].

We also tried to alter temporal discounting using one of its most important predictors, socioeconomic status. We borrowed this idea from Karadja et al. [START_REF] Karadja | George Mason University Department of Economics Working Paper[END_REF] but there is a growing literature looking at the impact of income and inequality perception on people's psychology. Some studies have focused on inequality perception in a company (Card et al., 2012) or in a given society [START_REF] Bublitz | Misperceptions of income distributions: Cross-country evidence from a randomized survey experiment[END_REF][START_REF] Cruces | Biased Perceptions of Income Distribution and Preferences for Redistribution: Evidence from a Survey Experiment[END_REF][START_REF] Karadja | George Mason University Department of Economics Working Paper[END_REF] but political sciences have also investigated this topic (Fernández-Albertos & Kuo, 2018). These misperceptions are observed in many societies but are highly variable [START_REF] Bublitz | Misperceptions of income distributions: Cross-country evidence from a randomized survey experiment[END_REF]. Overall, correcting negative misperceptions seems to have less of an effect than correcting positive misperceptions. For instance, people who learn that they are relatively poorer than they thought demand higher levels of redistribution but people finding out that they are richer than they thought do not change their attitudes towards taxation and redistribution [START_REF] Cruces | Biased Perceptions of Income Distribution and Preferences for Redistribution: Evidence from a Survey Experiment[END_REF].

In line with this prior literature, we found no effect of correcting participants' negative misperceptions about their relative income.

Several limitations can also explain this null effect. First, our participants may not have believed us when we told them their real place in the income gradient of the British population because of the way we presented the corrective information. It is possible that other ways of informing participants may be more effective, for example showing them the true income distribution in their society and placing them on it [START_REF] Bublitz | Misperceptions of income distributions: Cross-country evidence from a randomized survey experiment[END_REF][START_REF] Kuziemko | How Elastic Are Preferences for Redistribution? Evidence from Randomized Survey Experiments[END_REF], or giving them a percentage of people earning less than them [START_REF] Bublitz | Misperceptions of income distributions: Cross-country evidence from a randomized survey experiment[END_REF][START_REF] Cruces | Biased Perceptions of Income Distribution and Preferences for Redistribution: Evidence from a Survey Experiment[END_REF][START_REF] Kuziemko | How Elastic Are Preferences for Redistribution? Evidence from Randomized Survey Experiments[END_REF]. The possibility that participants did not believe the information shock could also be linked to a negativity bias, which leads people to construe negative information as truer than positive information [START_REF] Hilbig | Sad, thus true: Negativity bias in judgments of truth[END_REF]. In our case we only tested a positive information shock for ethical reasons, but a negative shock might have been easier to believe for participants.

Second, the treatment itself is perhaps not appropriate to impact temporal discounting and social trust. Temporal discounting and social trust are affected by childhood harshness [START_REF] Mell | Early life adversity is associated with diminished social trust in adults[END_REF][START_REF] Stamos | Investigating the effect of childhood socioeconomic background on interpersonal trust: Lower childhood socioeconomic status predicts lower levels of trust[END_REF] and these long lasting effects may be a lot stronger than our information shock. People may need repeated evidence of their true relative wealth on a longer timescale to express new temporal preferences. Moreover, Fehr [START_REF] Fehr | On The Economics and Biology of Trust[END_REF] and Albanese et al. [START_REF] Albanese | Trust, risk and time preferences: Evidence from survey data[END_REF] underline the fact that the component of social trust coming from preferences (like temporal preferences) is probably not easily malleable.

Third, we cannot exclude the possibility that our mediation model does not reflect what causally happens in reality and that other mediation models are more appropriate. For instance, alternative models might test whether temporal discounting or social trust lead to differences in socio-economic status, or whether social trust is a mediator of the relationship between socioeconomic status and temporal discounting. Given that it is not possible to compare different mediation models statistically, our strategy has been to focus on the strongest model theoretically and to restrain our analyses to that pre-registered model. This model is backed by a number of theoretical assumptions outlined in the introduction. Specifically, Becker & Mulligan [START_REF] Becker | The Endogenous Determination of Time Preference[END_REF] have emphasized that wealth causes patience, rather than the opposite. Brandt et al. [START_REF] Brandt | Changes in Income Predict Change in Social Trust: A Longitudinal Analysis: Socioeconomic Status and Trust[END_REF] have shown that a model in which income predicts social trust explain the data better than a model with the reversed causal link.

Fourth, the relationship between socio-economic status and social trust is likely mediated by multiple psychological mechanisms beyond temporal discounting. Risk aversion, for instance, plays a role in the risk-benefit calculus that unconsciously guide social trust decisions [START_REF] Albanese | Trust, risk and time preferences: Evidence from survey data[END_REF]. Given that social trust is based on reciprocity [START_REF] Krueger | Toward a Model of Interpersonal Trust Drawn from Neuroscience, Psychology, and Economics[END_REF], deciding to trust someone or not indeed requires pitching the certain and immediate costs associated with cooperation against more uncertain and delayed benefits. This risk-benefit balance is affected by two distinct factors: perceived collection risk and perceived waiting cost [START_REF] Mell | Both collection risk and waiting costs give rise to the behavioral constellation of deprivation[END_REF]. A collection risk arises when the benefits of cooperation do not end up materializing because of an unexpected event, such as premature death [START_REF] Mell | Both collection risk and waiting costs give rise to the behavioral constellation of deprivation[END_REF].

Waiting costs refer to the fact that while waiting for a return from a cooperative interaction, an individual could have used her investment for something else with a direct reward [START_REF] Mell | Both collection risk and waiting costs give rise to the behavioral constellation of deprivation[END_REF]. Individuals who have high waiting costs cannot afford to invest resources in cooperative interactions because they need to use these resources in the present. Both waiting costs and collection risks have a strong temporal component and recent studies have indeed found that risk and temporal preferences are somewhat correlated [START_REF] Johnson | Delay discounting and risky choice: Meta-analytic evidence regarding single-process theories[END_REF]. In addition to temporal and risk preferences, social trust is also affected by people's beliefs about their environment [START_REF] Albanese | Trust, risk and time preferences: Evidence from survey data[END_REF]. Social trust therefore also reflects people's trust experience in local interactions [START_REF] Glanville | How do We Learn to Trust? A Confirmatory Tetrad Analysis of the Sources of Generalized Trust[END_REF] and what people have learnt from good and bad past experiences with encounters. These experiences vary according to, for instance, existing values, norms of reciprocity, shared goals within a group [START_REF] Beugelsdijk | Diversity and trust: The role of shared values[END_REF].

Further research could take several directions. In keeping with our study, it would be interesting to design new treatments that really impact participants' perception of relative affluence, for example, by using more convincing information shocks and checking if participants believe the experimenter. It could also be possible to focus on other methods to shift temporal discounting and to study the consequence on social trust. For instance, several studies have used episodic future thinking to modulate temporal discounting (T. O. [START_REF] Daniel | Episodic future thinking reduces delay discounting and energy intake in children[END_REF]Liu et al., 2013;[START_REF] Sze | Bleak Present, Bright Future: Online Episodic Future Thinking, Scarcity, Delay Discounting, and Food Demand[END_REF]. Another possible direction is to conduct longitudinal studies to compute time series analyses and test whether an exogenous increase in socioeconomic status is followed by a decrease of temporal discounting and then an increase in social trust. If an efficient treatment is found to shift people's perception of relative affluence or temporal discounting and if this consequently impacts social trust, the results would be of interest for policy makers when designing interventions to promote prosocial behaviors.

Introduction

Since the establishment of democracy at the end of 19th c., France has undertaken massive political changes. First, its political institutions have become more inclusive with the introduction of female suffrage (Bouglé-Moalic, 2012), the participation of overseas populations [START_REF] Genova | Constructing Identity in Post-War France: Citizenship, Nationality, and the Lamine Guèye Law, 1946 1953[END_REF], the lowering of the age of voting from 21 to 18 years, as well as the rise of tolerance toward gay and lesbians, racial minorities, the pours and females (although much remains to be done) [START_REF] Chabal | France since the 1970s: History, Politics and Memory in an Age of Uncertainty[END_REF][START_REF] Fenby | France: A Modern History from the Revolution to the War with Terror[END_REF]. Second, its political life has become less violent, with a constant decline in death occurring during strike and demonstration, death resulting from political terrorism and state violence (although again, much remain to be done) [START_REF] Ritchie | Terrorism. Our World in Data[END_REF][START_REF] Tilly | Social Movements[END_REF]. Finally, as in other Western countries, the State has become more prosocial, with the creation of the health system, the generalization of retirement, the increase in free and mandatory schooling and so on. Overall, the part of the economy dedicated to social spending has risen from 1 % to 45 % of national income [START_REF] Alesina | Fighting Poverty in the US and Europe: A World of Difference[END_REF][START_REF] Dutton | Origins of the French Welfare State: The Struggle for Social Reform in France[END_REF].

What are the origins of such massive changes? Several hypotheses have been proposed, from the redistributive effect of war to the rise of socialist ideas [START_REF] Alesina | Fighting Poverty in the US and Europe: A World of Difference[END_REF]. In this article, we explore another idea that might complement existing approaches, namely that the decline of violence, the rise of tolerance and inclusivity is related to a change in mentalities. In line with previous studies documenting a shift in mentalities during the 20th c. [START_REF] Inglehart | Culture Shift in Advanced Industrial Society[END_REF][START_REF] Inglehart | Modernization, Cultural Change, and Democracy: The Human Development Sequence[END_REF], we hypothesize that social trust has increased and social interactions have become more cooperative over the 20th c., which made tolerance and inclusion more acceptable.

To test this idea, we studied the evolution of the official portraits of French representatives for the last 130 years. We posited that the rising importance of trust should be reflected in the portraits of people. Experimental works have indeed revealed that specific facial features, such as a smiling mouth or wider eyes, are consistently recognized as cues of trustworthiness. When doing a portrait, people can thus control to a certain extent how much they want to appear trustworthy. Here, we assumed that people are more likely to appear trustworthy when cooperative interactions are important and social trust plays an important role in their environment. In line with this idea, previous work has suggested that the level of trustworthiness displayed in portraits is correlated with the level of social trust in the environment [START_REF] Safra | Tracking historical changes in trustworthiness using machine learning analyses of facial cues in paintings[END_REF].

Hundreds of thousands of portraits are available from the late 19th and early 20th century. In this mass of archives, official portraits of MPs offer a good material to study the importance of trust in history. First, since the beginning of the 20th century, these photographic portraits are official and the majority of them were taken for the purpose of being included in publicly accessible face books. They are thus of high quality and standardized: in the vast majority of cases, the deputy's face is centered and frontal. This makes the series homogeneous in terms of purpose, context and technique for the photos. Second, because of the length of the French Republic, they constitute a long series, homogenous in terms of social and political positions:

over the last 130 years, MPs have had the same status, were seated in the same chamber, were elected for the same period of time, and played the same role in the French political system.

This dataset is well balanced over time with always over 300 images per legislature. This corresponds to a total of more than 15,900 images spread across 28 paperclips between 1906 and 2017 (last legislative election). All the photographs available in these paperclips have been collected, making this sample exhaustive. Each deputy is elected in a specific small geographical area and these geographical areas cover the whole national territory, making the series well-balanced in terms of spatial distribution. Finally, because they are elected, we can assume that MPs' opinion about trust is aligned with the opinion of the majority of their electorate (although there are a number of important biases toward rich, old, white and males).

Building on the work of Safra et al. 2020, we first assessed the facial perceived trustworthiness scores of MPs photographs. To do so, a random-forest algorithm was trained on avatars generated via FaceGen, using the model developed by Oosterhof and Todorov (Oosterhof & Todorov, 2008). Our algorithm is only based on the characteristics of the activation of the facial muscles (measured by the software OpenFace). This gives the opportunity to free oneself from any intuition or cultural bias that participants might have in giving perceived trust scores to the faces of people whose political opinions they know, or people whose clothing or hair style differs from current fashion. As it was possible to estimate the perceived trustworthiness in faces changed over time. Thus, we could measure how much people valued being trustworthy in leaders throughout the 20th century.

We also studied the social determinants of perceived trustworthiness in portraits. Social trust correlates, at the individual and societal levels, with economic variables. The higher the level of resources of an individual or a country, the higher its level of social trust. Existing work suggests that higher income [START_REF] Alesina | Who trusts others?[END_REF][START_REF] Brandt | Changes in Income Predict Change in Social Trust: A Longitudinal Analysis: Socioeconomic Status and Trust[END_REF], as well as higher childhood socioeconomic status [START_REF] Stamos | Investigating the effect of childhood socioeconomic background on interpersonal trust: Lower childhood socioeconomic status predicts lower levels of trust[END_REF][START_REF] Mell | Early life adversity is associated with diminished social trust in adults[END_REF]) are positively associated with social trust. Similarly, exogenous negative shocks on childhood resources have a negative impact on adult levels of social trust. Hörl et al (Hörl et al., 2016) showed that children exposed to more hunger in Germany after the second World War, had lower levels of social trust as adults.

Petersen and Aarøe [START_REF] Petersen | Birth Weight and Social Trust in Adulthood: Evidence for Early Calibration of Social Cognition[END_REF] even found an effect of lower birth weight, testifying of restricted level of resources in utero, on social trust in adulthood.

This bulk of evidence suggests that the evolution of trustworthiness in portraits should be associated with variations in economic development over history, and that the rise of the importance of trust can be partly ascribed to the rise of economic development in France during the 20th c. To test this idea, we collected GDP per capita data at the national, regional and departmental levels but also data more related to individuals living standards as mean incomes.

Moreover, as GDP cannot account for all economic characteristics, we also collected data on unemployment and poverty as well as data on inequalities. There is a vast literature on the links between economic variables and social trust, but this literature does not agree on the most important predictor of social trust (e.g., inequalities, income or GDP). Some studies underline the negative effect of inequalities on social trust (D. L. [START_REF] Costa | Understanding the American Decline in Social Capital, 1952[END_REF][START_REF] You | Corruption and inequality as correlates of social trust: Fairness matters more than similarity[END_REF].

Fairbrother and Martin [START_REF] Fairbrother | Does inequality erode social trust? Results from multilevel models of US states and counties[END_REF] are less categorical, their study shows only limited support for the effect of inequalities on social trust. In US states, they found that trust is negatively correlated with the level of inequality, but only cross-sectionally, not longitudinally. Other papers find both an effect of GDP and of inequalities (e.g., (Haushofer et al., 2013)). Our hypotheses were tested with several economic data from different sources, comparing the capacity of these different economic data to explain the variance in perceived facial trustworthiness.

Material and methods

Data collection and pre-processing

As many pictures as possible were collected from books that were accessible to non-specialized personnel from 1906 to now. Our dataset contains 15,910 pictures divided in 28 books (see

Computation of trustworthiness and dominance levels

Facial action units were automatically extracted by OpenFace version 1.01 using OpenCV 3.3.0 (https://github.com/TadasBaltrusaitis/OpenFace). OpenFace was used with MATLAB_R2016a. 15,219 faces were detected by OpenFace on the 15,910 collected. We only lost 4.3 % of the pictures at this stage. This is due to the good quality of this time series.

Facial action units were then transformed into trustworthiness and dominance evaluation scores using a random forest algorithm optimized on the avatars controlled for trustworthiness generated by FaceGen (Oosterhof & Todorov, 2008) as in [START_REF] Safra | Tracking historical changes in trustworthiness using machine learning analyses of facial cues in paintings[END_REF] (see figure 1 in the supplementary materials in the general appendix for examples).

OpenFace does not detect faces with a 100 % accuracy (see figure 2 for examples), so face detection quality was rated by three raters (Lou Safra, Léonard Guillou and an intern). The intern was blind to the hypotheses. Each of the 3 raters has independently classified the faces as accurately detected [START_REF] Bavel | g social and behavioural science to support COVID-19 pandemic response[END_REF] or not (0). To do so, raters evaluated how well landmarks placed by

OpenFace were consistent with actual face traits. The three scores have then been added to attribute a unique detection score to each picture, further used as a weight in statistical analyses.

Pictures having been rated three times 0 have been therefore excluded of the analyses. Due to scanning issues, some pictures were duplicated in the dataset. These pictures (same picture name and same picture date) were excluded from the analyses. After these exclusions our sample was of 12,828 pictures. Which means that 15.7 % of the detected faces were excluded by the raters. Ra Finally, gender, trustworthiness, and dominance scores, were added to the general file containing all information.

Statistical analyses

Quality checks

To control for the quality of our dataset and algorithm, we checked that the algorithm was susceptible to the same biases as Western participants on our dataset. These biases are of two kinds. First, female faces are on average perceived as more trustworthy and less dominant than male faces [START_REF] Todorov | Understanding evaluation of faces on social dimensions[END_REF][START_REF] Todorov | Social Attributions from Faces: Determinants, Consequences, Accuracy, and Functional Significance[END_REF]. Second, younger faces are on average perceived as more trustworthy and less dominant than older faces [START_REF] Todorov | Understanding evaluation of faces on social dimensions[END_REF][START_REF] Todorov | Social Attributions from Faces: Determinants, Consequences, Accuracy, and Functional Significance[END_REF]. These two effects are our quality checks. To test them, we computed two linear mixed-effects models. A first one with perceived trustworthiness level as dependent variable and gender and picture date as independent variables. A second one identical to the previous one but with age instead of gender.

As a last control, we also checked the c trustworthiness and mean national generalized trust in France, measured in the five waves (from 1981 to 2017) of the European Values Study (EVS). The weighted mean of generalized social trust per year in France was computed for each wave. Then each level of social trust was attributed to the closer in time legislature. To test the association between mean facial was computed. Then we ran a mixed-effects linear model with facial perceived trustworthiness as dependent variable and generalized social trust as variable of interest, as well as the control variables described in the previous section.

General information on statistical analyses

Almost half of the MPs (3,026 on a total of 6,188) were represented by more than one picture due to the re-elections of many MPs. Therefore, in all the analyses, we decided to use linear mixed-random factor. This is a deviation from the preregistration, in which we said we would use linear models and not linear mixed-effects models.

In all the models described below, we included several control variables: age, gender and perceived facial dominance. These variables are potential confounding variables due to their correlation with perceived trustworthiness [START_REF] Todorov | Understanding evaluation of faces on social dimensions[END_REF][START_REF] Todorov | Social Attributions from Faces: Determinants, Consequences, Accuracy, and Functional Significance[END_REF] and because the average age and number of women varied a lot across time. We also included technical controls: who scanned the pictures (administrators of the Assemblée Nationale for the years : 1914, 1936, 2007, 2012 and 2017), if the pictures are in color (since 1997), and if the pictures are numeric (since 2007). For some models one or more of these controls were automatically dropped concerned by the model.

Finally, the detection score (between 1 and 3) was included in each model as weights.

Time effect

To test our main hypothesis that mean perceived trustworthiness increased over time, we used a linear mixed-effects model with facial level of trustworthiness as dependent variable and time as variable of interest, as well as the control variables described above.

Effect of economic variables

National GDP per capita was collected from the Maddison project 2018 (Inklaar et al., 2018).

It was also collected on World Inequality Database (https://wid.world), as well as per adult national income, per adult national wealth and the top one percent shares of income and wealth to assess inequalities. Regional GDP per capita was collected on the INSEE website (https://www.insee.fr/fr/accueil), as well as national, regional and departmental unemployment, national poverty and national and regional mean income. All economic variables were transformed to z-scores.

To test our hypotheses on the effect of economic factors, we designed a linear mixed-effects model with each economic variable as variable of interest as well as the control variables described in the previous section. In order to be sure that the effect of economic variables is not just the result of the increase of many other variables with time, we used Bayes factors to compare each model containing an economic variable with the model with time instead. We also used Bayes factors to compare performances of models including different economic variables.

To be able to disentangle the general effect of time from the effect of economic variables, we also computed models with both time and an economic variable to see if the significant effect of the economic variable stood. We only chose the models with a Variance Inflation Factor lower than 3 for the economic variable. Again, we compared each model with time and economic variable against a model with only time, to see if the inclusion of an economic variable explained a sufficient share of the variance.

Inclusion of other controls and subsets.

To assess the robustness of our results, we conducted additional models including political variables as controls. Indeed, variations in the proportion of each political party within the assembly varies and of the majority party can constitute a potential confound. Moreover, the voting system may lead to the election of different kinds of MPs, which may also influence the cial trustworthiness. To be able to control for the political orientation of the MPs, we coded the political party and group into a far right (1) to far left (7) numeric scale. First, we created the list of unique combinations of political party and year as well as the list of unique combinations of political group value and year. Then, using Wikipedia, we rated each party/year and group/year combination on the numeric scale. Many MPs have several values for political party and group. We thus computed a mean of numeric political party and group ratings. We also collected three other political variables for each legislature: the voting system, the type of assembly (constituent or national) and the majority.

To be able to be sure that the variations of facial perceived trustworthiness observed in our dataset were not only due to an increase in the habit of smiling on pictures -which could have reflected an increase in happiness over time -we tested if our results were robust to the inclusion of the binary detection and the detected activation intensity by OpenFace of the zygomaticus major (AU12) a muscle characteristic of smiling.

To further check the robustness of our results, we re-ran our models on several subsets of the dataset. First, as women are only present in our dataset since 1945 and their number increased a lot in the recent years, it may be possible that part of the effects we observe are driven by these variations, even when controlling for gender. We thus replicated our analyses on a subset containing only male MPs. The dataset is reduced to 11,951 observations. Second, as photographic techniques changed with time, part of the effect may be due to the appearance of color pictures, even if we already control for it in our models. We therefore re-ran the main analyses, removing color pictures (i.e., pictures since 1997). The dataset is reduced to 11,951 observations. Finally, it is also possible that the way of scanning the pictures has an impact on how OpenFace measures action units and as a result on the calculated levels of facial perceived trustworthiness. We thus re-ran the main analyses, removing the pictures that were scanned by the Assemblée Nationale administrators (i.e., 1914, 1936, 2007, 2012, 2017). The dataset is reduced to 10,166 observations.

Additional analysis

Additionally, to our main analyses, we also tested the effect of our political variable, namely political party. This exploratory analysis aimed at seeing if MPs from the left are more trustworthy than MPs from the right, or if it is the other way around. To do so, we computed a linear mixed-effects model with facial level of trustworthiness as dependent variable and political party as variable of interest, as well as the control variables described above.

Results

Descriptive statistics

Quality checks

First, our two quality checks (age and gender effects) were tested to make sure our series of pictures was of good quality. As expected, in a mixed-effects model with time as control variable, age is significantly negatively correlated with perceived facial trustworthiness (b = -0.07, p < 0.001). In a similar model, male gender is negatively associated with facial perceived trustworthiness (b = -0.14, p < 0.001) (see table 2). So, our two quality checks are verified. According to this result, perceived facial trustworthiness evaluations appear as a good proxy of social trust at the country level.

Time effect

Our first main hypothesis, that perceived facial trustworthiness evaluations were correlated with time, was tested using a linear model. In a mixed-effects model with facial perceived trustworthiness as dependent variable, time as variable of interest and the controls described before, time is positively correlated with perceived facial trustworthiness (unstandardized b = 0.015, p < 0.001), such that, as time increases, perceived facial trustworthiness increases (see table 3, first reported model). Our hypothesis that perceived facial trustworthiness levels increased with time is confirmed (see figure 3).

Effect of economic variables

National GDP from Clio infra and from WID is in both cases significantly positively correlated Unsurprisingly, unemployment, poverty and inequalities have the opposite effect to GDP per capita on perceived facial trustworthiness.

To make sure that the effect of the economic variables was not just the result of their correlation with time, we computed Bayes factors for each model with an economic variable against the model with time instead. In this case, high Bayes factor would mean that the model with the economic variable is better than the one with time instead. All BFs < 10, except for regional income and poverty (see table 3). The model including time better explains the data than 11 models out of 13 including an economic variable. Lastly, we compared models including an economic variable with one another. More precisely we compared the model with national GDP against the model with national unemployment, the one with poverty, the one with income inequality and the one with wealth inequality. In the four cases, these models are not better than GDP (all BF < 0.1). Only the model with national income better accounted for the increase in perceived facial trustworthiness than GDP (BF = 5.2*10 13 ) (see table 4). Specifically, it is interesting to note that GDP and income are better predictors of perceived facial trustworthiness than the variables related to inequalities (income inequality and wealth inequality). This goes against the literature that mainly focuses on inequalities to explain social trust (D. L. [START_REF] Costa | Understanding the American Decline in Social Capital, 1952[END_REF][START_REF] You | Corruption and inequality as correlates of social trust: Fairness matters more than similarity[END_REF]. Overall, all the economic variables we tested were correlated with perceived facial trustworthiness in the expected direction. Moreover, GDP per capita and national income seem to be the best predictors of perceived facial trustworthiness variations. Nevertheless, it is important to note that time is a better predictor of perceived facial trustworthiness than most of the economic variables.

Economic variables, controlling for time

In line with the idea of disentangling the effect of economic variables and the effect of time, we computed models including both an economic variable and time. To be able to rely on the outputs of our models, we decided to compute variance inflation factors (VIF), using the function vif.mer, adapted from the function vif, and to keep only models where the VIF of the economic variable was lower than 2.5. As a result, the models we kept are the ones with:

regional GDP, national, regional and departmental unemployment as well as poverty. Except in the case of regional GDP (that was not significant anymore), the effects of the four other variables remained highly significant and kept the same direction. We computed again bayes factors, they were lower than 10 for regional GDP and departmental unemployment but they were higher than 10 in the case of national and regional unemployment and poverty (see table

2 in supplementary materials in the general appendix).

We found that, even when we controlled for the linear increase of perceived facial trustworthiness with time, the vast majority of economic variables remained significant. It means that economic variables are not correlated with facial perceived trustworthiness only because they could be both correlated with time.

Controlling for political variables

To control for the effect of political affiliation, we re-ran our main models including several political variables as controls: political affiliation, majority, voting system and type of assembly. Almost no quantitative change was observed in the time effect (b = 0.013, p < 0.001), national GDP (b = 0.48, p < 0.001), national unemployment (b = -0.073, p < 0.001) (see table 3 in the supplementary materials in the general appendix). This means that the variations in perceived facial trustworthiness in MPs are not simply due to a change in th about political parties.

Controlling for smile

An alternative hypothesis to ours, is that the increase of trustworthiness across time is simply due to the fact that people are happier across time, which can be reflected by their facial expressions, or just that smile has become more fashionable on pictures [START_REF] Ginosar | A Century of Portraits: A Visual Historical Record of American High School Yearbooks[END_REF].

Controlling for the two action units related to the smile, our results did not change qualitatively.

However, it is necessary to notice that these two action units are moderately correlated with the perceived facial trustworthiness level: 39.1 % (p < 0.001) for AU12 and 38.9 % (p < 0.001) for This means that the perceived facial trustworthiness variations are not only due to more and more smiling but more by an overall change in facial displays. This is not highly surprising, as smile is only one component of perceived trustworthiness, that is a more complex construct based on many facial cues (Oosterhof & Todorov, 2008).

Robustness of our results to subseting

To check their robustness, our main analyses were re-run with different subsets of the dataset:

excluding women or removing color pictures or removing the pictures that were not scanned by ourselves. These subset analyses replicate the results with the complete dataset (see table 5 in the supplementary materials in the general appendix). It shows that our results are not driven by some incidental differences between years due to sex ratio, pictures techniques our data collection techniques.

Additional analysis

Interestingly, there is a significant effect of political affiliation on perceived facial political affiliation is close to the extreme left, the more they have a low level of perceived facial trustworthiness (b = -0.065, p < 0.001) (see table 6 in the supplementary materials in the general appendix).

Discussion

The goal of this paper was to investigate psychological changes across time and more precisely variations of social trust valuation since the early 20th century. To do so, we collected more than ten thousand official pictures of French members of Parliament and measured their level of perceived facial trustworthiness. We then analyzed how these levels of perceived facial trustworthiness varied across time and how they correlated with economic variables. In line with our hypotheses, we found that levels of perceived facial trustworthiness increased in time from 1906 to 2017. We also found that this increase is associated with a general increase in living standards. The strength of our study is the high quality and quantity of data. The high number of data points (12,828) gives us the opportunity to bring to light small but interesting effects and to have a high confidence in our results. Moreover, our study is one of the very few trying to quantitatively assess past psychological changes. Finally, we replicated our result concerning the effect of time on non-Caucasian faces from a non-European country:

Singaporean MPs.

Interestingly, we show that, as successive MPs voted more and more pro-social laws, their facial expressions followed a similar pattern. Therefore, we can say that there is nothing specific to politicians that prevents them from looking more trustworthy. We can hypothesize that this gives insights on the voters preferences for more pro-social leaders.

Our results are in line with the literature on the link between amount of resources and social trust. There is a strong theoretical and empirical ground supporting the idea that one key determinant of social trust is the amount of resources available in the environment, described by socio-economic status of individuals [START_REF] Alesina | Who trusts others?[END_REF][START_REF] Brandt | Changes in Income Predict Change in Social Trust: A Longitudinal Analysis: Socioeconomic Status and Trust[END_REF]Hörl et al., 2016;[START_REF] Mell | Early life adversity is associated with diminished social trust in adults[END_REF][START_REF] Stamos | Investigating the effect of childhood socioeconomic background on interpersonal trust: Lower childhood socioeconomic status predicts lower levels of trust[END_REF]. Trust can be seen as an investment which entails some risks and costs. In a more economically deprived environment, the opportunity cost for an individual of investing in a social interaction rather than in an activity that meets his or her primary needs can be high. Furthermore, the return on investment made by the trustee may occur well after the initial investment by the truster, in which case the waiting cost for the truster may be high. Finally, if the trustee cheats and never realizes the return on investment of the truster, and if the truster has few resources, then the loss of resources can have great consequences. Therefore, differences in social trust associated with levels of resources are expected.

Our results are also in line with similar studies on portraits [START_REF] Safra | Tracking historical changes in trustworthiness using machine learning analyses of facial cues in paintings[END_REF] and theater plays [START_REF] Martins | The rise of prosociality in fiction preceded democratic revolutions in Early Modern Europe[END_REF] showing an increase of social trust indicators with time and a link with economic data. We tried as much as possible to disentangle the effect of economic variable from the effect of time, using Bayes factors to show that models including an economic variable are often better than the model including only time. For the economic variables minimally correlated with time, we even included both in models. We showed again that economic variables were not negligible in explaining variations of facial perceived trustworthiness. This result confirms that the link we observe today between resources and social trust, already existed several decades ago.

In addition, thanks to the fact that our time series is entirely contained is the 20th century, we were able to access various economic data and therefore to make fine analyses. In the models that did not include time, we tested economic variables against each other by comparing different models with Bayes factors. We showed that variables related to economic development like GDP per capita and mean national income better explained variations of perceived trustworthiness than inequalities related variables. This partially goes against the literature that focuses a lot on inequalities to explain social trust rise and fall (D. L. [START_REF] Costa | Understanding the American Decline in Social Capital, 1952[END_REF][START_REF] Fairbrother | Does inequality erode social trust? Results from multilevel models of US states and counties[END_REF][START_REF] You | Corruption and inequality as correlates of social trust: Fairness matters more than similarity[END_REF].

Our study has several limitations. First, the low number of time points in our dataset, due to the limited number of legislative elections, made it impossible to use time lag analyses, that would have given the opportunity to test if increases in GDP precede increases in our proxy of social trust or if it is the other way around. Secondly, even if we controlled as much as possible for political variables, the political nature of this series of pictures may bias the results. Lastly, perceived facial trustworthiness assessed from pictures is only a proxy of social trust and not a direct measure. Nevertheless, as passed people psychology cannot be directly assessed, no direct measure can be performed, in any case.

Further research could try to investigate more deeply the relationship between perceived facial trustworthiness and political affiliation. For example, one could try to link facial expressions and political programs. This might confirm the idea that the preference for more trustworthy faces is related to a general preference for more pro-social leaders, also reflected in their political program. It seems also important to try to replicate these results using another face detection software and another algorithm.

Introduction

Since the Middle Ages, despite very dark times and many remaining inequalities, European countries have apparently become more and more democratic and more inclusive with increasing power given to parliaments, the enlargement of voting rights, the decrease in state violence, and so on [START_REF] Fukuyama | Political order and political decay: From the industrial revolution to the globalization of democracy[END_REF][START_REF] Pinker | The Better Angels of Our Nature: The Decline of Violence In History And Its Causes[END_REF].

The origins of these changes remain elusive. Some suggest that this apparent general democratization is the result of changes in the balance of power between different social classes [START_REF] Acemoglu | The Narrow Corridor: How Nations Struggle for Liberty[END_REF][START_REF] Fukuyama | Political order and political decay: From the industrial revolution to the globalization of democracy[END_REF]. We suggest that it is the result of an underlying change in mentalities. With tolerance, cooperation and more generally pro-sociality being more and more valued by the people.

In line with this idea, recent work has shown that social trust has become more and more important in portraits over time. Using an algorithm on portraits from the National Portraits Gallery and the Web Gallery of Art, Safra et al. [START_REF] Safra | Tracking historical changes in trustworthiness using machine learning analyses of facial cues in paintings[END_REF] have automatically measured the level of perceived facial trustworthiness of portraits. They have shown that this level has on average increased over time. However, this study suffers from a number of bias. In particular, it could be the case that the observed rise of trustworthiness is an artifact that results from the sample of the study, and the rising proportion of middle-class portraits. If middle class people are more likely to want to look trustworthy, the increasing number in the database over time mechanically would lead to an increase of trustworthiness. To control for this bias, we decided to create a database in which portraits belongs to individuals with similar social status.

To do so, 1,224 portraits of European sovereigns were collected online. Rulers care about their image. Even if they are not chosen by their people, they always have an interest in not appearing completely opposed to their preferences. Therefore, we can assume that observing the representation of rulers will g

Another interest is that they more or less all have the same social status. Therefore, any change in the facial representations we could observe could not be attributed to the replacement of a social class by another on the portraits.

Following the work of Safra et al. [START_REF] Safra | Tracking historical changes in trustworthiness using machine learning analyses of facial cues in paintings[END_REF], we focused on facial expressions variations through time. To do so, a random-forest algorithm was trained on avatars generated via FaceGen, using the model developed by Oosterhof and Todorov (Oosterhof & Todorov, 2008). Our algorithm is only based on the characteristics of the activation of the facial muscles (measured by the software OpenFace). This gives the opportunity to free oneself from any intuition or cultural bias related to clothing, hairstyle, or the background of the portrait.

Unfortunately, OpenFace cannot properly detect some faces (e.g., profile ones, with some beards, etc.). After having used OpenFace and the algorithm on our portraits, the remaining sample size was N = 966.

We also studied the underlying determinant of this increase in pro-sociality. Our candidate is economic resources. Indeed, social trust correlates, at the individual and societal levels, with economic variables. The higher the level of resources of an individual or a country, the higher its level of social trust. Existing work suggests that higher income [START_REF] Alesina | Who trusts others?[END_REF][START_REF] Brandt | Changes in Income Predict Change in Social Trust: A Longitudinal Analysis: Socioeconomic Status and Trust[END_REF], as well as higher childhood socioeconomic status [START_REF] Mell | Early life adversity is associated with diminished social trust in adults[END_REF][START_REF] Stamos | Investigating the effect of childhood socioeconomic background on interpersonal trust: Lower childhood socioeconomic status predicts lower levels of trust[END_REF] are positively associated with social trust. Similarly, exogenous negative shocks on childhood resources have a negative impact on adult levels of social trust. Hörl et al (Hörl et al., 2016) showed that children exposed to more hunger in Germany after the second World War, had lower levels of social trust as adults. Petersen and Aarøe [START_REF] Petersen | Birth Weight and Social Trust in Adulthood: Evidence for Early Calibration of Social Cognition[END_REF] even found an effect of lower birth weight, testifying of restricted level of resources in utero, on social trust in adulthood. This bulk of evidence suggests that the link between social trust and the level of resources might be notably mediated by psychological factors. If this is the case, this association should not only be found across individuals and populations at a given time points but also across history, which is our hypothesis.

Therefore, we collected growth domestic product data from the Maddison Project database [START_REF] Bolt | Maddison style estimates of the evolution of the world economy[END_REF] for each country we had in our dataset. Several mixed effects linear models were computed to assess the effect of GDP on perceived facial trustworthiness and disentangle it from the effect of time.

Material and methods

Data collection and pre-processing

Four stages of data collection followed one another. The first step was to strictly select the websites we visited: only museum websites (because we suspected that portraits were of higher quality on these websites). Unfortunately, there is not a very large quantity of portraits of sovereigns available for free and in good quality on museum websites. After this step, the absence of results led us to perform a power analysis to identify a goal sample size that was rather high. To achieve it, as a second collection step, we followed the same method as in the first step (using only museum websites). As we could have expected, this did not yield enough portraits. Therefore, we eventually had to relax our methods for the third and fourth collection steps.

Step 1

Our first method was to focus on museum websites to collect very good data. We collected the names of sovereigns using Wikidata API, as well as birth and death dates. These lists were drawn for the following countries: England (or United-Kingdom), Denmark, Spain, France, Poland, Portugal, Holy Roman Empire, Sicilia-Sardinia and Sweden. We then entered all these names, one by one, in the search queries of the major art museums. This first sample was composed of 421 portraits (pre-registered here: https://osf.io/jbs64).

Unfortunately, the quality checks performed on this sample did not yield significant results:

neither the age nor the gender effect was significant. Therefore, the analyses were stopped.

This sample size (N = 421) is greatly smaller compared to the ones from the National Portraits Gallery (N = 1962) and the Web Gallery of Art (N = 4106). Therefore, we decided to perform a power analysis to evaluate the needed sample size to be able to expect potential significant results.

Power analysis

It was not possible to compute a proper power analysis to choose the sample size. Indeed, there is only one study that followed a protocol similar to ours: [START_REF] Safra | Tracking historical changes in trustworthiness using machine learning analyses of facial cues in paintings[END_REF]. This study did not use sovereign portraits and its data are from a single database: the National Portraits Gallery.

Therefore, its sample is not similar to the one of our study.

Nevertheless, we used this study results to perform a power analysis, keeping in mind that it was only partial information. 164 different sample sizes from N = 100 to N = 1490 were tested.

Random sub-samples (1000 for each sample size tested) were drawn from the dataset of the National Portraits Gallery of [START_REF] Safra | Tracking historical changes in trustworthiness using machine learning analyses of facial cues in paintings[END_REF]. In order to have samples more similar to the one of sovereigns, portraits dated later than 1950 were excluded before drawing our subsamples. Indeed, there were 202 portraits (10 %) dated later than 1950 in the dataset of Safra et al. [START_REF] Safra | Tracking historical changes in trustworthiness using machine learning analyses of facial cues in paintings[END_REF] compared to 22 (2.3 %) in our dataset. Then we computed a linear model to explain perceived facial trustworthiness with perceived facial dominance, age and gender as control variables and portrait date (= time) as variable of interest. Finally, we ploted the p-value of the effect of time in function of the sample size. In blue is the 0.05 threshold and in green the 0.01 threshold (see figure 1). From this analysis, using the mean p-values and the 95 % CI around them, we see that samples bigger than 800 always give p-values lower than 0.05 (excepting potential outlier samples).

Therefore, as we knew that some faces would be excluded because of a bad detection by OpenFace or other exclusion criteria, we decided to collect a total of around 1,200 portraits to secure a sample size of at least 800.

Step 2

In addition to the 421 previous portraits, we had to collect around 800 more portraits to achieve our goal sample size of 1,200 pictures securing at least 800 portraits after multiple exclusions.

The first 222 new portraits were collected the same way as the 421 previous ones but adding several states: Genoa, Medici, Milan, Montferra, Savoy, Venice, Luxembourg, Belgium, Netherlands, Scotland, Norway, and Russia. We collected the names of sovereigns using Wikidata API, as well as birth and death dates. We then entered all these names, one by one, in the search queries of the major art museums. This led us to a total of 643 portraits, which was still under our goal sample size. Therefore, we relaxed our method and decided not to use museum websites anymore.

Step 3

Using the API of Wikidata, the names, dates of birth and death of the European sovereigns have been collected a second time. But this time we added other information: the link to the sovereign's Wikidata illustration (if it was an oil on canvas); the name of the spouse; the link to the spouse's Wikidata illustration (if it was an oil on canvas). Then the Wikidata portraits of the sovereigns and their spouses were collected as well as their metadata (painting date, painter's name, technique). Finally, if a new portrait was the same as one of the previous ones, then it was eliminated. Again, this method did not achieve the expected number.

Step 4

In order to reach the expected sample size, we had to collect several more portraits. To do so we randomly selected 100 and then 150 names in our list of sovereigns to look for more portraits. This time, we adopted the most relaxed methodology: we used Google image to look for portraits representing these sovereigns. The same metadata as before were collected. Finally, a total of 581 portraits were collected from steps 3 and 4.

For all portraits (regardless of the collection methodology), profile, low quality and undated ones were not collected. The portraits that were not painted during the lifetime of the sovereign were also removed.

Before using OpenFace on the 803 new portraits, the total sample size was of 1224 (421 old + 803 new ones).

Computation of trustworthiness and dominance levels

Level of perceived trustworthiness and perceived dominance of faces were computed based on the facial action units automatically extracted by Open Face 3.1 (https://github.com/TadasBaltrusaitis/OpenFace) and transformed into perceived trustworthiness and perceived dominance evaluation scores using a random forest algorithm optimized on the avatars controlled for perceived trustworthiness and perceived dominance generated by FaceGen (Oosterhof & Todorov, 2008).

The OpenFace 3.1 software is not able to process all types of images. Some pictures were excluded because the software did not detect any face on it (due to basic characteristics such as face orientation or insufficient contrast between the background and the face).

Pictures were rated by 3 evaluators. Each of the 3 evaluators (two interns and Léonard Guillou)

independently classified the face as accurately detected or not: 0: badly detected; 1: well detected. The three scores were added to attribute a unique detection score to each picture.

These detection scores were used as weights for the statistical analyses. Images that were scored three times 0 were therefore excluded from the analyses.

Moreover, when all the data from different searches were combined, we were able to identify and eliminate duplicate images.

Statistical analyses

Quality checks

First of all, to assess the quality of our data and the proper capacity of our algorithm to predict scores of perceived facial trustworthiness, we checked if we replicated well-known idiosyncratic biases of participants: namely age and gender effects. Indeed, younger and more feminine faces are on average perceived as more trustworthy than older and more masculine ones.

If these two quality checks would not have been verified, the study would have stopped there.

To check our quality controls, we used linear models (lm function in R):

age effect: perceived facial trustworthiness as dependent variable and age as independent variable.

gender effect: perceived facial trustworthiness as dependent variable and gender as independent variable.

We added time as control in both models.

General information on statistical analyses

We compiled data from different countries that are therefore not all equally independent. As a consequence, all the following models computed were mixed effects linear models with country as random effect variable. In all our models we also included several controls that are wellknown potential confounding variables: age, gender, and perceived facial dominance. Indeed, these three variables are known to correlate with perceived facial trustworthiness (Todorov et al., 2008[START_REF] Todorov | Social Attributions from Faces: Determinants, Consequences, Accuracy, and Functional Significance[END_REF] and might vary with time. Detection scores were also always added as weights.

Time effect

To test our main hypothesis, we used a linear mixed effects model (lmer function in R) with perceived facial trustworthiness as dependent variable, time in years as independent variable and the controls and weight described above.

Effect of GDP

To test our hypothesis that GDP per capita is the variable responsible for the increase in prosociality in Europe, we collected GDP data from [START_REF] Bolt | Maddison style estimates of the evolution of the world economy[END_REF]. On this website, GDP data are available for the current countries. These data go back to the 16th century for some countries. Unfortunately, several kingdoms do not correspond to a current country (e.g., the Holy Roman Empire) and therefore cannot be assigned GDP data. Eventually, the countries that were assigned GDP data are: UK, France, Italy, Netherlands, Poland, Portugal, Spain, Sweden, Russia, Germany, and Belgium. As GDP data are partial, we assigned to each portrait date the GDP corresponding to the closest available GDP data for its country.

First, was computed a model with perceived facial trustworthiness as dependent variable, GDP as independent variable and controls and weights. To compare the effect of GDP and time, another model was computed, identical to the previous one but including time in addition.

Finally, Bayes factors were also computed. Male gender is negatively correlated with perceived facial trustworthiness (b = -0.36, p < 0.001), and so is age (b = -0.008, p < 0.001).

Results

Descriptive statistics

Quality checks

Both our quality checks are verified. Therefore, we can continue with the other analyses.

Effect of time

To test our main hypothesis that perceived facial trustworthiness increased through time, we computed a mixed effects linear model (lmer function from lme4 package) with perceived facial trustworthiness as dependent variable, painting date as variable of interest and perceived facial dominance, gender, Wikidata item as random effect variable. For ease of reading and interpretation, the time variable (date of the portrait) has been scaled.

Time is significantly positively correlated with perceived facial trustworthiness (b = 0.180, p < 0.001). Our main hypothesis that perceived facial trustworthiness in portraits increased through time is confirmed. (See figure 3).

Effect of GDP

Discussion

This study aimed to assess psychological variations over time, namely social trust over the last six hundred years. To do so, portraits of European monarchs dated from 1371 to 1999 were collected online. Taking as inputs facial muscles activation measured by OpenFace, an algorithm trained by ourselves was used to derive perceived facial trustworthiness scores. These scores were analyzed in several mixed effects linear models with the country as random effect variable. In line with our hypothesis, perceived facial trustworthiness increased over time. A correlation between GDP per capita and perceived facial trustworthiness was also found.

The increase in perceived facial trustworthiness in portraits that we found here is consistent with other similar articles in the literature. First, [START_REF] Martins | The rise of prosociality in fiction preceded democratic revolutions in Early Modern Europe[END_REF] National Portraits Gallery and the Web Gallery of Arts. Moreover, these portraits represented people of all social classes without being able to effectively control for this variable. Indeed, one of the criticisms made of this paper was that the changes in the balance of power between the social classes led to changes in the pictures that were painted, without the people really having changed, they would simply have been replaced. Here we are partly protected from this problem since we have only rulers at the head of their state. However, it should be noted that the status of sovereigns changed over time: the last one still in place have a lot less power than their ancestries. Moreover, our sample includes emperors of the Holy Roman Empire but also rulers like doges of Venice, that certainly did not have the same social status.

Concerning the positive correlation between perceived facial trustworthiness and GDP, our results are also in line with the literature. Again, it confirms the results of Safra et al. (Safra et al., 2020) and Guillou et al. (Guillou et al., 2021). But it is also in line with the literature linking social trust and economic resources. Indeed, much literature supports the fact that economic resources are a positive determinant of social trust [START_REF] Alesina | Who trusts others?[END_REF][START_REF] Brandt | Changes in Income Predict Change in Social Trust: A Longitudinal Analysis: Socioeconomic Status and Trust[END_REF]Hörl et al., 2016;[START_REF] Mell | Early life adversity is associated with diminished social trust in adults[END_REF][START_REF] Stamos | Investigating the effect of childhood socioeconomic background on interpersonal trust: Lower childhood socioeconomic status predicts lower levels of trust[END_REF]. However, it should be noted that we found that time is a better predictor of perceived facial trustworthiness than GDP. Therefore, it cannot be said with these data that the increase in perceived facial trustworthiness over time is due to an increase in GDP. A few elements must be noted concerning GDP: these data are partial and for some countries there is no data (i.e., Russia not before 1950). For other countries the data exist but are partial and very approximate. When we had partial GDP data, we assigned to each portrait date the GDP data dated the closest. This avoids losing too much data, but it makes the GDP data even more imprecise.

Future research could try to replicate these results using another type of algorithm to see if they are robust to a partial change in methods. Moreover, one interesting way we were not able to explore was the history of each country. Indeed, we had not enough portraits per country to study them separately. Focusing on only one country, it would be interesting to link the way of representing sovereigns or more generally aristocrats with political events like revolutions and restorations. One of the drawbacks of this study is that we did not have only good quality portraits because we collected them on the internet and often the good quality ones were not freely available. So future research would do well to test the same variables with really good quality portraits.

The evolution of trustworthiness displays in the portraits of the Versailles collection: A provisional report

Guillou, L. 1 , Safra, L. 2 , Baumard, N. 1 1.

University, Paris, France.

2. Sciences Po, CEVIPOF, CNRS, Paris, France.

Pre-registration: https://osf.io/4fwsg/

Introduction

Our [START_REF] Safra | Tracking historical changes in trustworthiness using machine learning analyses of facial cues in paintings[END_REF] which showed that perceived facial trustworthiness from portraits increased over time [START_REF] Safra | Tracking historical changes in trustworthiness using machine learning analyses of facial cues in paintings[END_REF]. To do so, we tried to find a database of good quality portraits. Indeed, previous studies (see https://osf.io/3bf54/) showed that the quality of the portraits is important to derive statistical analyses from them.

Thanks to a collaboration with the curator of the Château de Versailles, we were able to access 2,291 portraits, from 1483 to 1938, all digitized in very high resolution. These portraits were Chapter Five 95 accompanied by a file containing information such as the names of the painter and the subject, and the date range in which the portrait was painted. The very high quality of the image files and of the information provided are important strength of this database. However, it should be noted that, because the portraits do not necessarily represent well-known personalities, several birth and death dates were not available online.

The hypothesis we tested is that the perceived facial trustworthiness of portraits from the Château de Versailles has increased over time. The techniques used are identical to those used in the previous articles.

Material and methods

Data collection and pre-processing

Thanks to the generosity of its curator, we were able to access the entire digitized collection of the portraits of the Château de Versailles (N = 2,247). In addition, a dataset containing information on these portraits (date, subject, painter, technique, etc.) was provided. When possible, we also calculated the age of the subject of the portrait and identify her/his gender.

Concerning the economic data, we collected GDP per capita estimates from the Maddison project [START_REF] Bolt | Maddison style estimates of the evolution of the world economy[END_REF]. This study was pre-registered (see https://osf.io/cz72y).

Computation of trustworthiness and dominance levels

Levels of perceived trustworthiness and perceived dominance of faces were computed based on the facial action units automatically extracted by Open Face 3.1

(https://github.com/TadasBaltrusaitis/OpenFace) and transformed into perceived trustworthiness and perceived dominance evaluation scores using a random forest algorithm optimized on the avatars controlled for perceived trustworthiness and perceived dominance generated by FaceGen (Oosterhof & Todorov, 2008).

The OpenFace 3.1 software (https://github.com/TadasBaltrusaitis/OpenFace) is not able to process all types of images. Some pictures were excluded because the software did not detect any face on it (due to basic characteristics such as face orientation or insufficient contrast between the background and the face).

Pictures were rated by 3 evaluators. Each of the 3 evaluators (an intern, Nicolas Baumard and Léonard Guillou) independently classified the face as accurately detected or not: 0: badly detected; 1: well detected. The three scores were added to attribute a unique detection score to each picture. These detection scores were used as weights for the statistical analyses. Images that were scored three times 0 were therefore excluded from the analyses.

Statistical analyses

Quality checks

First of all, to assess the quality of our data and the proper capacity of our algorithm to predict scores of perceived facial trustworthiness, we checked if we replicated well-known idiosyncratic biases of participants: namely age and gender effects. Indeed, younger and more feminine faces are on average perceived as more trustworthy than older and more masculine ones.

If these two quality checks would not have been verified, the study would have stopped there.

To check our quality controls, we used linear models (lm function in R):

age effect: perceived facial trustworthiness as dependent variable and age as independent variable.

gender effect: perceived facial trustworthiness as dependent variable and gender as independent variable.

We added time as control in both models.

Taking into account faces with a score higher than 0, 1,399 of them have a gender value and 836 an age value, 764 have both.

Time effect

To test our main hypothesis, we used a linear model (lm function in R) with perceived facial trustworthiness as dependent variable, time in years as independent variables and age, gender, and perceived facial dominance as controls. Detection scores were used as weights. It is important to note, thanks to the density plot (see figure 2), that portraits are not well distributed along the timeline and especially very few of them are dated after 1850. From the portraits with a detection score higher than 0, only 134 portraits are dated 1850 or more recent.

Results

Descriptive statistics

Only 71 of them have age and gender values and are therefore included in the main model. Numbers in parentheses represent standard errors.

Quality checks

Our two quality checks are verified. Male faces are perceived as less trustworthy (b = -0.326, p < 0.001) than female faces. Age is negatively correlated with perceived facial trustworthiness (unstandardized b = -0.005, p = 0.0012). Thus, we can continue with the other analyses. The effect of time is not statistically significant (b = -0.025, p = 0.39).

Effect of time

Discussion

This study fails to replicate the results from Safra et al. [START_REF] Safra | Tracking historical changes in trustworthiness using machine learning analyses of facial cues in paintings[END_REF]. Several hypotheses can explain it.

First, there may be something specific to this dataset that we did not identify, like an unobserved confounding variable.

Second, it might be related to the fact that the number of portraits having values for gender and age is too low. This is quite unlikely. Indeed, we have shown in chapter 4 that samples with sizes bigger than 750 are very likely to yield p-values for the effect of time lower than 0.05.

Moreover, the excellent quality of the digitalization of these portraits might have reduced the noise and therefore allow for the detection of small effects with lower sample sizes.

Third, it can most likely be due to the inhomogeneous distribution of the portraits over time (see figure 2). Indeed, only 71 portraits dated 1850 or more recent can be included in the main model. Moreover, we know from previous studies that the positive effect of time is partially driven by the most recent portraits. Therefore, the lack of recent portraits probably explains the lack of effect of time. The dataset of the Château de Versailles (in red on figure 3) is less homogeneously distributed over time than the three other datasets where an effect of time has been found. It is also the dataset where there are the less very old and very recent portraits. It contains very few portraits dated before 1600 or after 1850, compared to the three other datasets.

This might explain why it is impossible to find a significant effect of time with this dataset. been reviewed and no effect of oxytocin has been found [START_REF] Nave | Does Oxytocin Increase Trust in Humans? A Critical Review of Research[END_REF]. More recently, [START_REF] Kosfeld | Oxytocin increases trust in humans[END_REF] experiment has been replicated and no clear effect of oxytocin has been found [START_REF] Declerck | A registered replication study on oxytocin and trust[END_REF]. Testosterone, on the contrary, is known to enhance competitiveness and dominance in humans and non-human animals [START_REF] Mccall | The animal and human neuroendocrinology of social cognition, motivation and behavior[END_REF].

In line with this fact, a negative effect of testosterone on trust has been found, especially in a context of social threat or competition [START_REF] Boksem | Testosterone Inhibits Trust but Promotes Reciprocity[END_REF][START_REF] Carré | Testosterone responses to competition predict decreased trust ratings of emotionally neutral faces[END_REF][START_REF] Hareli | Emotional versus neutral expressions and perceptions of social dominance and submissiveness[END_REF]. Unfortunately, the impact of testosterone and oxytocin on trust were both assessed with small samples (often less than 100 participants), and their effect is often found only for half of the sample (only in men for example, [START_REF] Carré | Testosterone responses to competition predict decreased trust ratings of emotionally neutral faces[END_REF]). More pre-registered studies including large samples are required to conclude on the biological bases of social trust. Fortunately, a precise understanding of its biological underpinnings is not absolutely necessary to study social trust consequences.

Despite its apparent benefits [START_REF] Balliet | Trust, conflict, and cooperation: A meta-analysis[END_REF]Bjørnskov, 2017;[START_REF] Crystal | Sources of Civic Orientation Among American Youth: Trust, Religiious Valuation, and Attributions of Responsibility[END_REF][START_REF] Guiso | Trusting the Stock Market[END_REF][START_REF] Guiso | Cultural biases in economic exchange[END_REF][START_REF] Knack | Does Social Capital Have an Economic Payoff? A Cross-Country Investigation[END_REF][START_REF] Mannemar Sønderskov | Explaining large-N cooperation: Generalized social trust and the social exchange heuristic[END_REF][START_REF] Rönnerstrand | Social capital and immunisation against the 2009 A(H1N1) pandemic in Sweden[END_REF][START_REF] Rothstein | All for All: Equality, Corruption, and Social Trust[END_REF], we hypothesize that the observed variability in social trust levels is adaptive. The low levels of resources we observe in some populations may be an adaptive response to the environment. Among the characteristics of an environment that could influence the expression of a given phenotype, in our case social trust, is the level of resources available. Indeed, development, reproduction and even each of our behaviors have a certain cost. This is why the notion of trade-offs in the allocation of resources to particular needs is central in evolutionary biology and evolutionary psychology (see e.g., [START_REF] Giudice | Life History Theory and Evolutionary Psychology[END_REF]Sear, 2020)). In our case, it is the question of investment in cooperation, through the acceptance (or not) of the risks associated with it, which can be approached through generalized social trust, that interested us.

Understanding the determinants of social trust

In chapter 2 we discussed the link between social trust and resource availability from a proximal and mechanistic perspective. Our idea was to identify the cognitive mechanism that links perceived socio-economic status to social trust. In line with our hypothesis, we found that temporal discounting partially mediated the relationship between socio-economic status and social trust. Therefore, our study supports the idea that temporal preferences play a role in the perception of the risks and costs associated with trust. Unfortunately, our treatment, designed to causally test this mediation, did not show any effect. We cannot therefore conclude on the actual causal pattern. It is still possible, that both time preferences and social trust are simply independently correlated with socio-economic status, and that their overlapping variance suggests a mediation pattern that does not actually exist.

Moreover, even if we take the mediation as real, only a small proportion (between 12 and 27 %)

of the effect of socio-economic status on social trust is mediated by temporal preferences. This means that the majority of the effect is direct or mediated through another channel. This is not very surprising as we already know that there is more antisocial behavior in deprived environments [START_REF] Nettle | Variation in Cooperative Behaviour within a Single City[END_REF]. Which is reinforced by the human tendency to model our behaviors on what we expect fro [START_REF] Bicchieri | Do the right thing: But only if others do so[END_REF]. As a result, individuals with low resources have likely experienced more deception in past social interactions. This would explain a part of the relationship between SES and social trust that is not necessarily mediated by temporal preferences.

Another thing to note, is the low part of the variance of social trust explained by SES. There are two elements that need to be discussed. First, our models do not aim at making predictions, instead they aim at testing our predictions. When we try to explain the variance in perceived facial trustworthiness by several variables, we only want to test if our variable of interest (for example GDP) is significantly correlated with perceived facial trustworthiness. Quite obviously our goal is not to use this series of variables to predict levels of perceived facial trustworthiness in other situations. Therefore, the R squared of our model is not a very important measure.

Second, there are many other phenomena that we do not take into account and that we cannot control for by design because we are not in lab-controlled conditions. A biologist studying nonhuman animals in the lab, could argue that our models are too low in explanatory power to be of real interest, compared to what her studies produce. Nevertheless, it is important to put the R squared of the different models in perspective and not to make raw comparisons. Humans are a very interesting biological model but also a terrible one when it comes to designing experiments. Indeed, create experimental designs that control for all the potential confounding variables is clearly impossible. Mice can be reared in controlled conditions: same food, same temperature, same light, etc. Obviously, this cannot be the case for humans, and we cannot even gather enough information to control for these variables in our models. Therefore, none should expect to find clear and large effects in psychology, especially concerning complex behaviors like social trust.

One strategy to solve this problem is to design the sample carefully. First, a large sample size allows small effects to be detected. The less control there is for confounding factors in the experimental design, the larger the sample size needs to be in order to detect effects that are buried in noise. Second, having a representative sample of the population avoids the risk of observing only particular individuals that do not represent the whole population. Fortunately, platforms like Prolific Academic, which are designed to conduct human experiments online, provide the opportunity to gather large and representative samples.

Despite all these potential limitations our studies make an important contribution to the very limited literature on this topic. Indeed, the effect of temporal preferences on social trust has only been studied twice [START_REF] Albanese | Trust, risk and time preferences: Evidence from survey data[END_REF][START_REF] Nguyen | Trust and Trustworthiness Under the Prospect Theory: A Field Experiment in Vietnam[END_REF] with mixed results.

A historical perspective

In chapters 3 and 4 and 5, we approached this question from a different angle. We had a very exciting project: to try to study the psychology of people from the past. More specifically, to approximate levels of social trust using cultural items, namely facial representations. Building on the work of [START_REF] Safra | Tracking historical changes in trustworthiness using machine learning analyses of facial cues in paintings[END_REF], we have shown, in several different databases of photographed or painted portraits, that perceived facial trustworthiness, our proxy of social trust, increases over time. This gives strength to the results of [START_REF] Safra | Tracking historical changes in trustworthiness using machine learning analyses of facial cues in paintings[END_REF].

An important method element that is not described in chapters 3, 4 and 5 is how we tested the robustness of our algorithm. We used the same method as in [START_REF] Safra | Tracking historical changes in trustworthiness using machine learning analyses of facial cues in paintings[END_REF] but trained a new algorithm on my own computer. The faces used to train the algorithm are the avatars controlled for perceived facial trustworthiness and dominance, created by Oosterhof and

Todorov (Oosterhof & Todorov, 2008), using the software FaceGen. These faces are known to elicit consensual judgements of trustworthiness and dominance from participants. A part of the sample was used as training sample and the other part as test sample. The chosen algorithm is a random forest algorithm, as Safra et al. [START_REF] Safra | Tracking historical changes in trustworthiness using machine learning analyses of facial cues in paintings[END_REF] found that this technique is better than neural networks, that have the tendency to overfit the training sample in our case, which poses a generalization problem.

After training and selecting the best random forest algorithm, we tested that we were able to replicate the results from Safra et al. [START_REF] Safra | Tracking historical changes in trustworthiness using machine learning analyses of facial cues in paintings[END_REF] on four different databases: Karolinska (Lundqvist et al., 1998), Oslo (Leknes Affective Brain lab, 2015), Chicago (Ma et al., 2015) and Brazil (Thomaz & Giraldi, 2010). This was the case.

Obviously, most of the limitations described in the previous section still apply in these and other chapters. The noisiness of human data is even more pronounced with historical and cultural data, which requires larger datasets. For example, with online portraits, it is impossible to do a proper random sampling of the entire population of portraits that have been painted.

First, some portraits have been lost over the years, and others have never been digitized. Of the digitized portraits, some are not accessible online in good quality, and others lack date information, making them useless for our studies. Given all this, the sampling strategy is necessarily biased by the motivation of the sampler to dig deeper in websites to find more portraits or more information on them. Moreover, the sample is severely limited by the quality of the data available online, which may bias the sample too.

said that they do not reflect historical trends of the society as a whole. However, in a democratic system, politicians have to convince the citizens. The impression they give needs to match the citizens values. Therefore, their facial expressions give a good idea of what citizens value.

Furthermore, it is important to bear in mind that the level of perceived facial trustworthiness we measure is only a proxy for what voters value in their politicians. Valuing trustworthy politicians may mean that pro-social behaviors such as cooperation are themselves valued, which is related to social trust level. Quite obviously, social trust itself is impossible to measure directly with individuals from the past. To test the reliability of our results, it is necessary to approximate levels social trust through other proxies. Further research is therefore needed, bearing in mind that different cultural items may have different temporal relationships with the actual level of social trust. For example, it can be suggested that certain cultural items such as portraits of rulers may not be a proxy of the level of social trust of the society, but only of the highest social classes. As social classes may be subject to different economical dynamics, their social trust levels may vary differently. Theater plays may be more popular and therefore reflect the social trust level of middle class. As a result, finding slightly different temporal variations between the different social trust proxies may be related to the fact that they approximate the social trust levels of different segments of the population.

It should also be noted that variations in facial representations over time may be subject to different effects acting together. First, the intention of the painter to make a particular ith a particular facial expression. And third, further intention of the subject regarding the potential directions he or she may give to the painter. This may especially be the case for official paintings paid by the sovereign.

One could also argue that the variations in social trust we observe are simply the result of fashion in posing and the changes in painting techniques. In fact, this is not a problem. We think ion.

Therefore, the study of fashion changes in the use of facial expressions allows us to study related psychological changes.

The trustworthiness measurements made by our algorithm may be influenced by the technological changes in painting and photography that happened over time. However, our algorithm only measures trustworthiness and dominance based on facial action units. Then it does not take into account elements like color. Furthermore, notably in chapter 3, we controlled for these potential confounding variables in our models. More generally, the use of an algorithm relying on the activation of facial muscles, allows us to ignore many historical effects that are not related to facial expressions changes and that may bias the perception of the subject. It is picture [START_REF] Salem | Analyzing human appearance as a cue for dating images[END_REF] and perception of trustworthiness [START_REF] Brambilla | The influence of visual context on the evaluation of facial trustworthiness[END_REF]. For background, clothing or hairstyle. These cues may trigger modules that are not as universal and conserved as facial expressions detection. This could therefore bias the trustworthiness evaluations. For example, the Hitler moustache was very popular in France in the 1930s, before the Second World War. Nowadays, participants may infer negative or aggressive emotions from this kind of moustache, something that our algorithm does not.

A matter of money

In a correlation there are at least two variables. In our case, we have already discussed the one we wanted to study and understand: social trust. But we have not yet discussed the second member of the equation, namely resources.

It is quite tricky to measure the resources of individuals in a certain environment. At first sight, the amount of money they have on their bank account seems to be a good candidate. But how do we measure it? We cannot have access to bank statements, and we cannot force our participants to tell us exactly how much money they have.

Therefore, in chapter 2, we decided to ask our participants how much money arrived in their bank account each month, taking into account all sources of income and benefits. However, we spending has more to do with their place in the income hierarchy of society. This probably better indicates the amount of resources perceived by their organism, than an absolute amount of money per month.

This issue is even more problematic when studying these phenomena at the societal level and in the past. In chapters 3 and 4, we have mainly used growth domestic product (GDP) per capita which is a widely used measure of the wealth of countries. Moreover, GDP is one of the few variables that has also been estimated in the past, which was necessary for our historical studies.

Nevertheless, it is important to note that there are several limitations to using GDP. First it is a very general measure of wealth, at a large scale. It clearly does not take into account individual differences. In particular, GDP does not reflect inequality. A high GDP may indicate that a country is rich, but it does not take into account how that wealth is distributed, it may bring together very poor and very rich people.

Moreover, as explained in chapter 2, the absolute amount of resources available to an individual may not be the best measure of how much she struggles to make ends meet. Several effects probably intersect and act at the same time. There is probably an effect of the absolute amount of resources, with a kind of threshold beyond which one has the capacity to have other than basic needs. This is not a new idea, as Maslow already suggested it in 1943 [START_REF] Maslow | A theory of human motivation[END_REF]Mcleod, 2007). Furthermore, as it has been suggested by Amartya Sen and Martha Nussbaum, absolute wealth measures do not take into account the actual capabilities of individuals [START_REF] Robeyns | The Capability Approach[END_REF][START_REF] Sen | Commodities and Capabilities[END_REF]. In other words, the capacity for an individual to make certain actions that will enhance her well-being is not only related to her financial capacity to do it but also to « substantive freedoms », such as the ability to live to old age, engage in economic transactions, or participate in political activities. Nevertheless, there is probably another more relative resource effect related to hierarchy.

The effects of hierarchical position are quite complex and have been studied mainly in nonhuman animals. On the one hand, individuals with a high position often have more resources at their disposal, be it food or sexual partners. But on the other hand, they often experience more stress from having to defend their dominant position. The relationship between social rank and cortisol level, a stress related hormone, seems to be sex dependent. It is negative for females and positive for males. This might be related to the fact that males and females do not achieve dominance the same way [START_REF] Beehner | The next step for stress research in primates: To identify relationships between glucocorticoid secretion and fitness[END_REF]. For example, male chimpanzees have to fight for higher rank, whereas females achieve it simply by aging [START_REF] Foerster | Chimpanzee females queue but males compete for social status[END_REF].

As far as humans are concerned, the hierarchical position is difficult to assess precisely, as we have the capacity to occupy different positions in different groups: family, work, society, sport, associations, etc. To overcome this methodological problem, income rank has been used as a proxy of hierarchical position. Wood et al. [START_REF] Wood | An evolutionary based social rank explanation of why low income predicts mental distress: A 17 year cohort study of 30,000 people[END_REF], suggest that the psychological effect of income rank might be more important than the material effect of absolute income in predicting mental distress. They give an evolutionary explanation based on the « involuntary defeat syndrome », which represents a hard-wired response to being of low social rank [START_REF] Taylor | The role of defeat and entrapment in depression, anxiety, and suicide[END_REF]. The idea is that lower-ranking animals undergo a hormonal regulation that leads to an adaptive « no threat » cluster of behaviors. This « no threat » cluster is adaptive as it avoids conflicts with dominant individuals. Interestingly these behaviors are similar to those associated with depression in humans: withdrawal, apathetic behavior, decreased appetite, decreased sexual behavior, and hypervigilance [START_REF] Gilbert | Evolution and depression: Issues and implications[END_REF][START_REF] Gilbert | The role of defeat and entrapment (arrested flight) in depression: An exploration of an evolutionary view[END_REF]. In other words, this theory suggests that there is a cluster of behaviors that has been selected to respond adaptively to a low rank situation, and that is till shared by several animal species, including humans.

Another phenomenon at work, is the fact that each increase of one unit of resources does not have the same effect. Desperate individuals may not be able to get out of the despair zone with a single extra euro [START_REF] De Courson | Why do inequality and deprivation produce high crime and low trust?[END_REF]. Symmetrically, in relation with the idea of diminishing returns [START_REF] Shephard | The law of diminishing returns[END_REF], very wealthy individuals may see no difference from a small increase in wealth, compared to lower middle-class individuals. This has already been studied in relation to happiness that does not increase linearly with income [START_REF] Frey | What Can Economists Learn from Happiness Research[END_REF].

Finally, the overall increase in GDP over time makes its effect difficult to disentangle from the effect of other variables correlated with time. Given enough time points, the only statistical test that can help is time-lag analyses. They can show whether changes in GDP precede changes in social trust or whether the reverse is true. Unfortunately, we did not have enough time points in chapter 3 to perform this type of analysis. Therefore, we have to be cautious about calling for a causal relationship when we do not have the data to test it.

Future directions

All chapters need further studies to confirm their results. Chapter 2 needs further experimental their relative income to see if it has an effect on temporal preferences and social trust. A major limitation of our study that could be easily overcome is that we did not ask our participants whether they believed us when we corrected their relative position in the society in terms of income.

Regarding future directions, chapters 3 and 4 provide more interesting ideas. First, it seems necessary to multiply this type of study with other types of cultural items. Martins and Baumard [START_REF] Martins | The rise of prosociality in fiction preceded democratic revolutions in Early Modern Europe[END_REF] did it with theater plays and found similar results to ours -their proxy for social trust increases with time. To continue with texts, one possibility would be to use legal texts. Indeed, they have been around for quite a long time and are well ordered and kept in archives. Moreover, legal texts are known to often endorse changes in the mentality of the society. Therefore, they could be used to track historical psychological changes.

Concerning portraits, following the study of MPs, it would be interesting to explore the political aspects of facial expressions. For instance, the Cevipof has a database of political leaflets often Another thing that could be very interesting and can be applied to any type of cultural item would be to use their popularity as a proxy of peop the records of theaters (number of theater performances) or publishing houses (number of plays sold or number of different editions) could give a good idea of their popularity. It would then be possible to track the popularity of several theater plays over time to see if certain aspects (e.g., the value placed on prosocial behavior) of these plays could explain why they are more es for certain behaviors, such as pro-social behaviors or more aggressive ones, may be reflected in the cultural elements they like. Something similar could be done for other types of books or for songs. It seems more difficult to do something similar for portraits as their popularity in the past may be less easy to assess. Nevertheless, one could try to find a comprehensive sample of portraits painted in a particular period, in a particular context and look at which ones are still popular today and which ones are not. The selection process that has occurred may reflect how current preferences differ from past preferences. For example, we might predict that regardless of the period chosen, the portraits that are most popular today are those that feature the most trustworthy faces from each period.

Evolutionary questions on cooperation and social trust

The question of the evolutionary origins of cooperation is still an open field of research (see e.g., [START_REF] André | Mechanistic constraints and the unlikely evolution of reciprocal cooperation[END_REF][START_REF] Geoffroy | The emergence of cooperation by evolutionary generalization[END_REF]). We do not take a position in this debate.

However, one might ask whether humans have not been cooperating and therefore trusting each other for tens of thousands of years. The answer is « yes, but ». During most of its evolutionary history, homo sapiens has lived in small communities with a lot of genetic relatedness between most members of each group ((R. B. [START_REF] Lee | Hunter-Gatherers and Human Evolution: New Light on Old Debates[END_REF], but see [START_REF] Singh | Human social organization during the Late Pleistocene: Beyond the nomadic-egalitarian model[END_REF]). As a result, there have been selection pressures to be able to identify reliable cooperation partners, i.e., community still exists. Interestingly, Jachimowicz et al. [START_REF] Jachimowicz | Community trust reduces myopic decisions of low-income individuals[END_REF] show that this type of trust is not correlated with economic variables. Yet, this is not the type of trust we studied in this thesis, namely generalized social trust. How did generalized social trust emerge?

Since we have long lived in small groups with few resources, what were the selection pressures acting on generalized social trust? Why would there be a specific module selected by evolution dedicated to calculating a level of generalized social trust?

Something similar to the mechanisms of leader choice may be at work for generalized social trust. One hypothesis to explain leader choice is that it is a by-product of our ability to identify individuals that should be successful in a given environment [START_REF] Safra | Why would anyone elect a narcissistic untrustworthy leader? A behavioural ecology approach[END_REF]. Similarly, generalized social trust could be a composite trait, not selected per se but resulting from the gathering of several sources of information that may interact with each other: past experiences, temporal preferences, level of resources, etc. This is why it is so important to understand the proximal mechanisms that determine trust, as we did in chapter 2. It would also explain why we rely on any cue, even inaccurate ones such as those related to perceived facial trustworthiness, to update our judgement from our basal level of generalized social trust to an individual level of perceived trustworthiness.

Concluding remarks

The benefits of social trust are well-known [START_REF] Balliet | Trust, conflict, and cooperation: A meta-analysis[END_REF]Bjørnskov, 2017;[START_REF] Crystal | Sources of Civic Orientation Among American Youth: Trust, Religiious Valuation, and Attributions of Responsibility[END_REF][START_REF] Guiso | Trusting the Stock Market[END_REF][START_REF] Guiso | Cultural biases in economic exchange[END_REF][START_REF] Knack | Does Social Capital Have an Economic Payoff? A Cross-Country Investigation[END_REF][START_REF] Mannemar Sønderskov | Explaining large-N cooperation: Generalized social trust and the social exchange heuristic[END_REF][START_REF] Rönnerstrand | Social capital and immunisation against the 2009 A(H1N1) pandemic in Sweden[END_REF][START_REF] Rothstein | All for All: Equality, Corruption, and Social Trust[END_REF]. Therefore, the identification of its determinants is of primary importance for public policy. Whether social trust is only a social construct that can be moved by specific communication campaigns or has natural roots related to the characteristics of the environment is a very important question.

Indeed, if a major determinant of social trust is the environment, and more specifically resources, then investing in communication campaigns might be ineffective as it is somehow the case for teenage pregnancies [START_REF] Henderson | Impact of a theoretically based sex education programme (SHARE) delivered by teachers on NHS registered conceptions and terminations: Final results of cluster randomised trial[END_REF], smoking [START_REF] Labiris | Preliminary evaluation of the school-smoking-prevention policy in Greece[END_REF], or skin cancer [START_REF] Cho | Fear Appeals for Individuals in Different Stages of Change: Intended and Unintended Effects and Implications on Public Health Campaigns[END_REF]. Our results point in the direction of natural roots of social trust.

In this perspective, one take of this PhD is that policy makers should focus on environment rather than trying to directly change them.

for example, yields immediate costs, such as refraining from using a resource that is available, while the majority of the benefits are only felt in the future. Cutting one's individual carbon emissions by limiting the use of one's car or reducing heating at home also implies self-restraint in the present, in order to maintain decent life conditions for future generations, or for oneself in the future.

Given the temporal nature of environmental issues, individual variations in time preferences may have an e↵ect on people's willingness to engage in environmental actions [START_REF] Van Vugt | Naturally green: Harnessing stone age psychological biases to foster environmental behavior[END_REF]. In line with this idea, a meta-analysis by [START_REF] Milfont | Time perspective and environmental engagement: A meta-analysis[END_REF] shows that there is a link between time orientation and pro-environmental attitudes and behaviour, such that individuals with a future-oriented time perspective are on average more engaged in environmental preservation than present-oriented individuals. Identifying the factors that influence time preferences is therefore of central importance to shed light on the variability of pro-environmental attitudes and behaviour.

Several socio-demographic factors play a role in explaining variations in time preferences (Reimers et al., 2009). In particular, multiple studies have now demonstrated that low socioeconomic status (SES) orients individual time preferences towards the present, resulting in a tendency to favour short-term behaviours. Time preferences are typically measured using temporal discounting tasks, in which participants are asked to choose between smaller, more immediate rewards, and larger but more delayed rewards [START_REF] Frye | Measuring Delay Discounting in Humans Using an Adjusting Amount Task[END_REF][START_REF] Green | Discounting of delayed rewards: A life-span comparison[END_REF]. Using these tasks, researchers have shown that people with low incomes and educational levels discount more steeply than people with a higher SES [START_REF] Enzler | Subjective discount rates in the general population and their predictive power for energy saving behavior[END_REF][START_REF] Green | Temporal discounting in choice between delayed rewards: the role of age and income[END_REF]Harrison et al., 2002;[START_REF] Hausman | Individual discount rates and the purchase and utilization of energy-using durables[END_REF][START_REF] Lawrance | Poverty and the rate of time preference: evidence from panel data[END_REF]Reimers et al., 2009). Research documenting increases in impatience due to natural disasters and climate-driven income shocks provides support to the hypothesis that this association between SES and time preferences is causal [START_REF] Cassar | Trust, risk, and time preferences after a natural disaster: experimental evidence from Thailand[END_REF][START_REF] Di Falco | Rain and impatience: Evidence from rural Ethiopia[END_REF]Tanaka et al., 2010). These empirical results are congruent with recent theoretical papers arguing that di↵erences in time preferences play an important role in accounting for socioeconomic gradients in a range of individual behaviours [START_REF] Bickel | A competing neurobehavioral decision systems model of SES-related health and behavioral disparities[END_REF][START_REF] Mani | Poverty impedes cognitive function[END_REF]Pepper and Nettle, 2017;[START_REF] Sheehy-Ske Ngton | How poverty a↵ects People's decision-making processes[END_REF].

Given that socioeconomic conditions have an impact on time preferences on the one hand, and given that future-oriented individuals tend to engage more in proenvironmental actions on the other, SES may have an e↵ect on pro-environmental behaviour via time preferences. In line with this idea, social scientists have long noted an association between SES and pro-environmental attitudes and behaviour in large-scale reviews [START_REF] Diamantopoulos | Can socio-demographics still play a role in profiling green consumers? A review of the evidence and an empirical investigation[END_REF][START_REF] Hines | Analysis and synthesis of research on responsible environmental behavior: A meta-analysis[END_REF]. More recent research confirms that green consumption, recycling, signing petitions, or engaging in environmental organisations are more widespread among high SES individuals [START_REF] Guerin | A multilevel analysis of the determinants of recycling behavior in the European countries[END_REF][START_REF] Kennedy | Eating for Taste and Eating for Change: Ethical Consumption as a High-Status Practice[END_REF][START_REF] Kennedy | Eco-habitus or Ecopowerlessness? Examining Environmental Concern across Social Class[END_REF], and multilevel analyses have shown that this pattern is found in countries around the world [START_REF] Franzen | Environmental Attitudes in Cross-National Perspective: A Multilevel Analysis of the ISSP 1993 and 2000[END_REF][START_REF] Haller | Dispositions to Act in Favor of the Environment: Fatalism and Readiness to Make Sacrifices in a Cross-National Perspective[END_REF][START_REF] Marquart-Pyatt | Are there similar sources of environmental concern? Comparing industrialized countries[END_REF][START_REF] Pisano | Environmental Behavior in Cross-National Perspective: A Multilevel Analysis of 30 Countries[END_REF]. Other studies have found that despite the fact that individuals of lower SES are more worried about the risks associated with environmental hazards, they report being less willing to act for the environment than individuals with higher incomes and educational attainments [START_REF] Lo | National income and environmental concern: Observations from 35 countries[END_REF]Marquart-Pyatt, 2012).

Financial capacities play a direct and rather obvious role in this association, because many pro-environmental behaviours are costly. For example, having more money decreases the relative burden of green taxes and facilitates access to ecofriendly products. Symmetrically, pro-environmental behaviours that are cost-e↵ective, such as energy-saving behaviours and the use of public transport, are more frequent among low SES individuals [START_REF] Blankenberg | On the determinants of pro-environmental behavior -a guide for further investigations[END_REF][START_REF] Trotta | Factors a↵ecting energy-saving behaviours and energy eciency investments in British households[END_REF]. This mechanical e↵ect of income is however limited. Prior research has pointed out that the association between pro-environmentalism and income is quite weak [START_REF] Hines | Analysis and synthesis of research on responsible environmental behavior: A meta-analysis[END_REF], and that education level is a stronger and more systematic predictor [START_REF] Blankenberg | On the determinants of pro-environmental behavior -a guide for further investigations[END_REF][START_REF] Lo | National income and environmental concern: Observations from 35 countries[END_REF]Marquart-Pyatt, 2012;[START_REF] Pearson | Race, Class, Gender and Climate Change Communication[END_REF]. Engagement in pro-environmental behaviour is also influenced by other factors associated with SES. For instance, social scientists and economists have shown that green consumption is also a matter of signalling a liation to a high social class [START_REF] Delgado | The value of environmental status signaling[END_REF][START_REF] Kennedy | Eco-habitus or Ecopowerlessness? Examining Environmental Concern across Social Class[END_REF][START_REF] Van Der Wal | The paradox of 'green to be seen': Green high-status shoppers excessively use (branded) shopping bags[END_REF].

The goal of our paper is to test whether psychological preferences also contribute to the association between SES and pro-environmental behaviour. Recent theoretical frameworks, such as the one proposed by Pepper and Nettle (2017), indeed emphasize the role of time preferences in creating social gradients in a range of real-life decisions that have a strong temporal component, such as investment in education or preventive health. However, these theories have not been applied to environmental behaviour yet, and the idea that the relationship between SES and pro-environmentalism is mediated by time preferences has not been tested.

In this paper, our general hypothesis is that pro-environmental behaviour and willingness to protect the environment are less common among people with a lower SES in part because their temporal discounting is higher. In other words, we hypothesize that the relationship between SES and pro-environmental behaviour is partially mediated by temporal discounting. In our first study, we leverage existing data collected on a French large-scale sample to test the association between SES and pro-environmental attitudes. Our second study tests a mediation model between SES, pro-environmentalism and temporal discounting. Finally, our third study investigates the causal impact of an information shock about relative income on temporal discounting and pro-environmentalism.

Study 1

In study 1, we capitalized on existing data collected by the Center of political research of Sciences Po (CEVIPOF) from a large sample of the French population. We examined whether SES was associated with pro-environmental attitudes. Before analysing the data, our hypotheses, methods, and analysis plan were pre-registered in the Open Science Framework (OSF): https://osf.io/qav9x.

Materials and Methods

Participants

The original dataset contains 17 survey waves, ranging from November 2015 to November 2017. For the purposes of this study, we focused on data regarding respondents' environmental attitudes and merged them with information about respondents' SES. The final analysis includes participants with complete data for en-vironmental and socioeconomic variables up to wave 8 (N = 17,070). Following our pre-registered exclusion criterion, we excluded 1,132 respondents who had answered "I don't know" to the environmental items. 14 participants who had answered "I don't know" to the household income question were also excluded (this exclusion criterion had been forgotten in the pre-registration, but matches the one used for the environmental variables). This resulted in a final dataset of 15,924 participants, with 55.7% women and age range = 16-97 years (M = 47.33, SD = 15.32). Mean monthly income was comprised between €2,250 and €2,999 and 53% of the sample had a higher education degree.

Measures

• Socioeconomic Status

Objective SES was assessed using level of education and monthly net household income, z-transformed and summed (correlation between income and education: r = 0.22, p < .001). Subjective SES was assessed using z-transformed perceived financial ease, measured with a single item asking respondents how they were doing with their household income. Subjective perception of one's SES plays an important role in predicting disparities in many life domains [START_REF] Kraus | A road map for an emerging psychology of social class[END_REF]. For example, several medical studies have shown that the association between subjective SES and health outcomes persists after controlling for education and income [START_REF] Cené | Associations between subjective social status and physical and mental health functioning among patients with hypertension[END_REF][START_REF] Ghaed | Subjective social status, objective socioeconomic status, and cardiovascular risk in women[END_REF]. All the original questions in French, their English translation and the waves during which they were collected are available in the OSF-folder for this study (https://osf.io/9ube8/).

• Environmental attitudes

Pro-environmentalism. Pro-environmentalism was measured with the following survey question: "How important are the following issues to you personally? 1) health insurance, 2) social welfare, 3) pensions, 4) fighting unemployment, 5) purchasing power, 6) crime, 7) the environment, 8) immigration, 9) terrorism, 10) the European Union, 11) the competitiveness of companies established in France". This question was asked in wave 8 and in a couple of later waves. Since attrition grows from one wave to the next, we focused on wave 8, which provides the largest sample for this question. To compute participants' pro-environmentalism, we built a score that captures their interest in environmental issues (as measured by item 7), relative to their overall interest in social and political matters (as measured by all items combined). For each participant, the pro-environmentalism score therefore corresponds to a ratio of the raw score in response to issue 7 over the mean of the scores provided on all issues. This transformation was added because raw responses to item 7 confound participants' general interest for political matters and their specific interest for the environment, which is the construct we ultimately care about.

Willingness to increase green taxes and public spending. We also looked at items measuring people's willingness to increase public spending for the environment, to fight climate change and their willingness to increase taxes on polluting activities (see the OSF-folder: https://osf.io/9ube8/). These items were pre-registered as secondary outcomes, because we reasoned that questions relative to public spending and taxes are more likely to be confounded by political orientation than questions tapping general pro-environmental attitudes.

All items were rated on a five-point scale. They were z-transformed and coded so that the highest scores indicated a pro-environmental position. All three items relative to public spending and taxes were then summed to create a composite score measuring "willingness to increase green taxes and public spending" (↵ = .73, inter-item correlation = 0.47).

Statistical analyses

Our main analyses were pre-registered and carried out in R. We conducted simple linear regressions, with the following specification:

Environmental attitudes i = 0 + 1 SES i + ✏ (1)
The associations between the environmental variables and objective and subjective SES were assessed separately with this same linear model. In addition, to control for the e↵ect of political orientation and other potentially confounding variables, we also conducted supplementary unregistered analyses, with the following specification:

Environmental attitudes i = 2 + 3 SES i + 4 Age i + 5 Gender i + 6 P olitical position i + ✏ (2)
Participants' political position was measured with a question that asked them to indicate where they stood on a scale of 0 to 10, where 0 is left and 10 is right.

Results

Pro-environmentalism was positively and significantly associated with objective SES ( = 0.09, p < .001) and subjective SES ( = 0.07, p < .001, see Table 1). There was also a positive association between SES and willingness to increase green taxes and public spending (objective SES: = 0.06, p < .001; subjective SES: = 0.04, p < .001, see Table 1). Both environmental variables remained positively correlated with SES when controlling for age, gender and political orientation (see Table 3 in the Supplementary Materials).

The results obtained with education and income as separate variables can be found in the correlation matrix in the Supplementary Materials (see Table 2). Both pro-environmentalism and willingness to increase green taxes and public spending were correlated with educational level, but only pro-environmentalism was significantly correlated with income. Further analyses indicated that the di↵erent items that make up the composite willingness score were all positively correlated with education, but that their relationships with income were inconsistent: willingness to increase public spending for the environment was negatively correlated with income (r = 0.024, p = .002) and willingness to increase public spending to fight climate change was not correlated with income (r = 0.006, p = .413). Only willingness to increase taxes on polluting activities was positively correlated with both income (r = 0.042, p < .001) and education (r = 0.083, p < .001). Subjective SES is a rating of perceived financial ease. Pro-environmentalism measures the relative importance of the environment compared to other socio-political issues. Willingness to increase green taxes and public spending is a composite score combining participants' willingness to increase public spending for the environment, to fight climate change and to increase taxes on polluting activities. See detailed definitions for each variable in subsection 2.1.2.

Discussion

In this study, we found that participants' level of pro-environmentalism relative to other socio-political issues was significantly correlated with their SES. There was also a positive association between SES and willingness to increase green taxes and public spending. Globally, these results suggest that willingness to act for the environment is stronger among higher SES individuals. Arguably, a measure of willingness to increase green taxes and public spending is not strictly equivalent to willingness to act for the environment. The positive correlation between SES and willingness to increase green taxes and public spending could be partly due to the fact that richer individuals have a higher acceptance of taxes, simply because they have higher financial capacities. Supporting this view, a recent survey conducted with a representative sample of French adults indicates that low-income individuals are more likely to think that taxes are too high, compared to high-income individuals [START_REF] Forsé | Les Français et la justice fiscale[END_REF].

However, the present study provides evidence that the association between environmental attitudes and SES is not only a matter of willingness to pay and tax acceptance: the positive correlation between SES and pro-environmentalism suggests that, in France at least, higher SES individuals also give higher priority to environmental issues compared to other socio-political issues, such as health insurance or immigration.

On the whole, these analyses provide support for our hypothesis, but they are only a first step, since they are not su cient to test the entire mediation model. We were limited by the dataset, which contained no measure of time preferences. In addition, there was no behavioural data, and the environmental variables were potentially confounded by financial and political factors. In study 2, we test the mediation model using an online discounting task combined with relevant questionnaire data.

Study 2

The goal of Study 2 was to test whether time preferences partially mediate the relationship between SES and pro-environmental attitudes and behaviour. As in Study 1, our hypotheses, methods, and analysis plan were pre-registered (a first pre-registration focused on the replication of the association between SES and temporal discounting was made (https://osf.io/452zr) and a second pre-registration was added to specify our mediation hypotheses: https://osf.io/58rn2). Data, materials, and the R script used to analyse the data are also available in the OSFfolder for this study (https://osf.io/28zkg/files/).

Besides the measurement of time preferences, one important aspect of this second study is the use of a measure of pro-environmental behaviour. Even though environmental attitudes are good predictors of pro-environmental behaviour, important value-action gaps and intention-to-action gaps are widely documented [START_REF] Kollmuss | Mind the Gap: Why do people act environmentally and what are the barriers to pro-environmental behavior?[END_REF][START_REF] Lavergne | Predicting individual di↵erences in the choice of strategy to compensate for attitude-behaviour inconsistencies in the environmental domain[END_REF][START_REF] Maki | Meta-analysis of pro-environmental behaviour spillover[END_REF]. Using direct measures of pro-environmental behaviour is therefore important to better understand the factors that influence actual decision-making [START_REF] Clements | A behavioural measure of environmental decision-making for social surveys[END_REF][START_REF] Oliphant | Social Preferences and Environmental Behavior: A Comparison of Self-Reported and Observed Behaviors[END_REF].

Materials and Methods

Participants

We conducted a power analysis with G*Power (version 3.1.9.3), which determined that a sample of at least 643 participants was su cient to detect an e↵ect size f 2 = 0.0122 with 80% power. This e↵ect size was computed based on the mean of two correlation coe cients reported by Reimers et al. (2009), who found a Spearman correlation of 0.09 between temporal discounting and income, and of 0.13 between temporal discounting and education. Participants were recruited via Prolific. Only British individuals with a minimum approval rate of 90% were eligible for our study. We recruited additional participants to compensate for attrition, resulting in a dataset of 765 participants.

Following our pre-registered exclusion criteria, we removed 9 participants who had failed one or more catch trials in the temporal discounting task. In addition, 49 participants who had responded too fast (< 500 ms) or too slowly (> 2 minutes) to single question screens, and 29 participants who responded too fast (< 3 s) or too slowly (> 5 minutes) to the other survey pages were removed from our analyses. We also excluded 14 participants who did not provide their income. Finally, we added an unregistered exclusion criteria for participants who reported outlier income values (N = 14). Personal monthly incomes above £12,500 were considered as likely reporting mistakes, and correspond to outlier data points (more than 2SD deviation from the mean).

Our final dataset includes 650 participants (70% females) aged between 21 and 77 years (M = 39 years; SD = 12.8). Participants had an average total monthly income of £935, ranging from £0 to £12,000 (SD = 1,187, see Table 2). Participants' educational level was quite high: only 17 participants did not finish high school, while 64% of them had either completed college or obtained a postgraduate degree.

Measures

We presented the questionnaire and discounting task using Qualtrics (https:// www.qualtrics.com). After providing informed consent, participants filled out the socio-demographic questionnaire. Then, they continued with the discounting task, followed by the measures of pro-environmental attitudes and behaviour.

• Socioeconomic status

Our measure of participants' objective SES was a single variable combining level of education and personal monthly income and benefits. We chose to ask respondents about their personal income rather than their household overall income because Micklewright and Schnepf (2010) have shown that questions about household income induce lower response rates and produce lower quality data. Participants were asked to report their personal monthly income in a free-text box, in order to avoid unintentional priming e↵ects that can arise with the use of income brackets (Haisley et al., 2008). We also measured the total monthly amount of government benefits they received with a free-text box. Participants were asked to report their level of education on a 6-point scale. Income, benefits and level of education were ztransformed and summed to create the unique variable of objective SES (correlation between income and education: r = 0.27, p < .001).

As in the previous study, we included a measure of subjective SES, but this time we used a composite variable combining a scale from [START_REF] Griskevicius | When the Economy Falters, Do People Spend or 126 Save? Responses to Resource Scarcity Depend on Childhood Environments[END_REF] and the MacArthur Scale of Subjective Social Status (a 10-rung ladder developed by Adler et al., 2000). [START_REF] Griskevicius | When the Economy Falters, Do People Spend or 126 Save? Responses to Resource Scarcity Depend on Childhood Environments[END_REF] scale includes three items: In the past few years: (a) My family and I have had enough money for things, (b) I have lived in a relatively wealthy neighborhood, (c) I have felt relatively wealthy compared to other people in my neighborhood. A single index was obtained by summing individual scores across the three items (↵ = .68, inter-item correlation = .41). We adapted the MacArthur scale in the following way: "Think of this ladder as representing where people stand in the United Kingdom. At the top of the ladder are the people who are the best o↵, those who have the most money, most education, and best jobs. At the bottom are the people who are the worst o↵, those who have the least money, least education, and worst jobs or no job. Where would you place yourself on the ladder?". Participants' response to this scale and the index based on [START_REF] Griskevicius | When the Economy Falters, Do People Spend or 126 Save? Responses to Resource Scarcity Depend on Childhood Environments[END_REF] scale were z-transformed and summed to create a unique variable of subjective SES. The correlation between these two combined measures was strong (r = 0.59, p < .001). The detailed list of all socioeconomic variables is available in the OSF-folder for this study (https://osf.io/7v3hs/).

• Pro-environmental attitudes Pro-environmental attitudes are multidimensional and there is a large number of measures in the literature. In an e↵ort to synthesize these various scales, Milfont and Duckitt (2010) developed the Environmental Attitudes Inventory, which comprises twelve specific scales that capture the main dimensions highlighted by previous research, such as enjoyment of nature, environmental fragility or support for interventionist conservation policies. The present article focuses on attitudes related to willingness to act for the environment. In Study 2, we assessed these attitudes using two 10-item scales from the Environmental Attitudes Inventory (Environmental movement activism and Personal conservation behaviour). All items are listed in Table 1 in the Supplementary Materials.

• Pro-environmental behaviour A donation question was included at the end of the experiment. We drew on recent works showing that a non-hypothetical donation to an environmental nonprofit organization is a valid measure of pro-environmental behaviour [START_REF] Clements | A behavioural measure of environmental decision-making for social surveys[END_REF][START_REF] Eby | The influence of environmental identity labeling on the uptake of pro-environmental behaviors[END_REF]. Following [START_REF] Ackermann | Save the Planet for Humans' Sake: The Relation between Social and Environmental Value Orientations[END_REF], we o↵ered participants a choice between di↵erent charitable causes rather than specific nonprofit organizations, to avoid biased responses from individuals with strong feelings regarding a particular organization. The six di↵erent causes were presented in a random order to counter order e↵ects. To avoid the bias that lower income participants have less money to give, we told participants that a donation would be made on their behalf to a cause of their choosing (see [START_REF] Eby | The influence of environmental identity labeling on the uptake of pro-environmental behaviors[END_REF]. Participants were informed that we would give 10p to a charity for every person that participated in our study, and that they could choose their two favourite causes out of six options: Reforestation programmes, Food aid for the homeless, Medical cancer research, Care for the elderly, Emergency and disaster relief, Education in developing countries. Answers were transformed into a binary variable (presence of the cause "Reforestation programmes" among the two choices = 1, absence = 0).

• Temporal discounting

The temporal discounting task was based on [START_REF] Frye | Measuring Delay Discounting in Humans Using an Adjusting Amount Task[END_REF]. Participants had to complete three blocks of an intertemporal choice task with varying delays and amounts. Each block consisted of six binary choice trials. The task ended with two catch trials, resulting in a total of 20 trials. In the first block, participants had the choice between a smaller reward in three days, and a larger reward in three weeks. In the second and third blocks, the later delay was set to three months and two years respectively. As in Haushofer et al. (2013), possible serial correlations and order e↵ects in participants' responses were controlled for by randomising the order of trials across blocks. The position of the sooner smaller and larger later alternatives on the screen (top vs. bottom) was also randomised across trials to control for possible position e↵ects.

The monetary choices presented to participants were hypothetical. One concern might be that they may not be su ciently motivated to give thoughtful answers when answering hypothetical questions. However, no clear and systematic di↵erences have been identified when comparing real and hypothetical rewards in discounting tasks [START_REF] Coller | Eliciting individual discount rates[END_REF][START_REF] Johnson | Within-subject comparison of real and hypothetical money rewards in delay discounting[END_REF][START_REF] Kirby | Modeling myopic decisions: Evidence for hyperbolic delay-discounting within subjects and amounts[END_REF]. Moreover, discount rates measured with hypothetical choices in a laboratory setting have been found to correlate with real-world measures of impulsivity such as smoking, overeating, and debt repayment [START_REF] Chabris | Individual laboratory-measured discount rates predict field behavior[END_REF]Hardisty et al., 2013;[START_REF] Meier | Time discounting predicts creditworthiness[END_REF]Reimers et al., 2009).

The later reward was kept constant (at 70£ as in Reimers et al., 2009), while the smaller reward was adjusted according to participants' choices. The adjustment was based on a bisection algorithm, following [START_REF] Frye | Measuring Delay Discounting in Humans Using an Adjusting Amount Task[END_REF]. As recommended by the authors, the adjustment for the upcoming trial was always equal to the maximum amount multiplied by 2 ( n) , where n is the trial number for the current adjustment. Such a procedure allows for the identification of various individual indi↵erence points for each participant. An indi↵erence point corresponds to the magnitude of the smaller-sooner reward at which a participant shows no preference for either the smaller-sooner or later-larger reward [START_REF] Scholten | Behavioral trainings and manipulations to reduce delay discounting: A systematic review[END_REF]. It can be used for the computation of a discount rate.

The method that we chose to compute respondents' discount rates is based on the computation of the area under the curve (AUC) of the empirical discounting function (see [START_REF] Frye | Measuring Delay Discounting in Humans Using an Adjusting Amount Task[END_REF]Myerson et al., 2001). The empirical discounting function is defined by the various indi↵erence points evidenced by the discounting task. The AUC between two points on the curve is calculated as [(x 2 x 1 )[(y 1 + y 2 )/2], where x 1 and x 2 are the successive delays and y 1 and y 2 are the indi↵erence points for those delays. In the present study, the AUC between 3 weeks and 3 months and the one between 3 months and 2 years were summed, resulting in a single value of AUC per participant. Lower values of the AUC indicate a steeper discount rate.

Statistical analyses

Regression analyses were conducted relying on the three step procedure set forth by Baron and Kenny (1986) to detect a mediation e↵ect. The regression models had the following specifications:

Environmentalism = 10 + 11 SES + ✏ (3)
DiscountRate = 12 + 13 SES + ✏ (4)

Environmentalism = 14 + 15 SES + 16 DiscountRate + ✏ (5)
Where Environmentalism refers alternatively to pro-environmental attitudes and behaviour; SES refers alternatively to objective or subjective SES; and Discount Rate corresponds to the area under the curve (AUC). We used an OLS regression model for every equation, except for pro-environmental behaviour, for which we used a Logit regression.

Following Baron and Kenny's (1986) approach, a mediation e↵ect exists if coe cients 11 and 13 in equations ( 3) and ( 4) are significant, and if coe cient 15 in equation ( 5) is not significant: in other words, there is a mediation when the predictor-outcome e↵ect becomes non-significant once the mediator is added to the model. Partial mediation is established when the e↵ect of the predictor on the outcome only weakens once the mediator is added. However, this approach does not formally test the significance of the mediating e↵ect and does not assess its size. Researchers have recommended that other methods, such as nonparametric testing procedures, should be used for this purpose [START_REF] Adler | Relationship of subjective and objective social status with psychological and physiological functioning: Preliminary data in healthy, White women[END_REF][START_REF] Holmbeck | Post-hoc probing of significant moderational and mediational e↵ects in studies of pediatric populations[END_REF][START_REF] Preacher | SPSS and SAS procedures for estimating indirect e↵ects in simple mediation models[END_REF]. We derived percentile-based confidence intervals with the bootstrap, relying on R mediation package (see [START_REF] Tingley | mediation: R Package for Causal Mediation Analysis[END_REF].

Results

Descriptive statistics for key variables are reported in Table 2. Tables 3 and4 provide the regression and bootstrap estimates for the full model.

Mediation analysis

For the first step of the mediation analysis, environmental variables were regressed on socioeconomic variables. We found a positive and significant relationship between self-reported pro-environmental attitudes and SES (objective SES: = 0.09, p = .026; subjective SES: = 0.08, p = .035, see Table 3). However, our measure of pro-environmental behaviour was not correlated with either objective or subjective SES (see Table 4 in the Supplementary Materials).

For the second step of the mediation analysis, we found the expected association between SES and temporal discounting: the area under the discounting curve was positively correlated with objective ( = 0.15, p < .001) and subjective SES ( = 0.17, p < .001, see Table 3). Since lower values of the area under the curve indicate steeper discounting, this means that participants with lower SES tended to discount more future rewards. Figure 1 illustrates these results for the objective variable: it represents the mean indi↵erence points as a function of time for low-SES participants and high-SES participants (using a median split). The area under the discounting curve is smaller for low-SES participants, which reflects a steeper discount rate. The third step of the mediation analysis was consistent with our hypotheses, as far as pro-environmental attitudes are concerned: the positive association with objective SES disappeared when adding the temporal discounting variable to the regression model ( = 0.07, p = .095, see Table 3). We observed a similar pattern with subjective SES: its positive correlation with pro-environmental attitudes disappeared when the discounting variable was added to the model ( = 0.05, p = .141, see Table 3), which is compatible with the hypothesis of a mediation. Controlling for gender and age, all of our linear models with SES and environmental variables yielded similar results, indicating that the associations that we observed were not driven by these other variables (see Table 5 in the Supplementary Materials).

Size of the mediating e↵ect

To estimate the size of the hypothesized mediating e↵ect and the direct e↵ect, we computed nonparametric bootstrap confidence intervals with the percentile method, using R mediation package [START_REF] Tingley | mediation: R Package for Causal Mediation Analysis[END_REF]. Concerning objective SES and pro-environmental attitudes, the average mediating e↵ect was positive and significant ( = 0.031, p < .001) but the direct e↵ect was stronger, albeit still small ( = 0.093, p = .040, see Table 4). As could be expected from the regression analyses, we found no significant direct or mediated e↵ect of objective and subjective SES on the behavioural measure using the bootstrap. Finally, the relationship between subjective SES and pro-environmental attitudes was significantly mediated by temporal discounting ( = 0.034, p < .001). There was also a direct e↵ect but it was not significant ( = 0.081, p = 0.114, see Table 4).

Discussion

In line with our hypothesis, we find that pro-environmental attitudes are associated both with objective and subjective SES and that this relationship is partially mediated by temporal discounting. However, this result does not translate to the pro-environmental behaviour measure. In addition, analyses with revenue and education as separate variables suggest that the mediation pattern obtained with the objective SES composite score was mostly driven by the education variable. This could mean either that educational attainment and the subjective perception of one's socioeconomic position are stronger determinants of environmentalism than wealth, or that the income and benefits measures we have used are less reliable.

Null results concerning the pro-environmental behaviour variable could be due either to a gap between attitudes and behaviour, or to certain shortcomings of the donation question that we used to measure behaviour. The low stakes (a donation of 10p) might have contributed to diminish the validity of this measure. It could also be due to some specificities of the answer options we o↵ered: the donation question asked participants to evaluate the importance of an environmental cause with respect to others, which may depend on the participants' evaluations of the other causes as much as the environmental one. It is also possible that the actions we included were too specific or that participants were influenced by the fact that some of the actions could be located in the UK, while others were relevant for other countries (e.g. "Education in developing countries"). This measure should be modified so as to decide if its validity can be improved.

In addition, a major limitation of studies 1 and 2 is the use of cross-sectional data, with which we cannot establish causal processes definitively. Even though our significant results are encouraging, other causal models could underlie the correlations that were found. Experimental studies are therefore needed before we can claim that there is a causal e↵ect of SES on pro-environmental attitudes which is mediated by temporal discounting.

Study 3

The goal of this third study is to go beyond correlations and test the causal influence of socioeconomic resources on people's temporal discounting and proenvironmentalism. The only way to act on people's objective SES is to change their income, educational level or occupation, which is obviously di cult in an experimental setting. However, brief psychological interventions such as priming can be used to alter perceptions of one's socioeconomic condition: for instance, [START_REF] Griskevicius | When the Economy Falters, Do People Spend or 126 Save? Responses to Resource Scarcity Depend on Childhood Environments[END_REF] used recession cues to prime their participants with resource scarcity in a laboratory experiment. Another possibility is economic games, which allow experimenters to manipulate participants' perception of their economic resources through income shocks: for example, Haushofer et al. (2013) found that negative income shocks lead to an increase in temporal discounting, and that positive income shocks weakly decrease discount rates.

Other kinds of research have shown that it is possible to use information about income as an experimental treatment, so as to alter the perception of one's socioeconomic condition (Card et al., 2012;[START_REF] Cruces | Biased perceptions of income distribution and preferences for redistribution: Evidence from a survey experiment[END_REF][START_REF] Hvidberg | Social Position and Fairness Views[END_REF][START_REF] Karadja | Richer (and Holier) than Thou? The E↵ect of Relative Income Improvements on Demand for Redistribution[END_REF][START_REF] Mijs | How Information about Inequality Impacts Belief in Meritocracy: Evidence from a Randomized Survey Experiment in Australia, Indonesia and Mexico[END_REF]. Capitalizing on works demonstrating that faulty beliefs about one's own position in the national income distribution are common (see [START_REF] Fehr | Your Place in the World: The Demand for National and Global Redistribution[END_REF]Norton and Ariely, 2011;[START_REF] Norton | The Not-So-Common-Wealth of Australia: Evidence for a Cross-Cultural Desire for a More Equal Distribution of Wealth: The Not-So-Common-Wealth of Australia[END_REF], economists and psychologists have shown that informing people of their true position in the income distribution can have an impact on political attitudes. For instance, [START_REF] Cruces | Biased perceptions of income distribution and preferences for redistribution: Evidence from a survey experiment[END_REF] found that Argentinian respondents who believed themselves to be relatively richer than they actually were demanded more redistribution, when provided with correct information. [START_REF] Karadja | Richer (and Holier) than Thou? The E↵ect of Relative Income Improvements on Demand for Redistribution[END_REF] found that a vast majority of Swedes believed that they were poorer than they actually were relative to others, and that correcting this misperception had an e↵ect on participants' attitudes toward redistribution.

In this study, we use a treatment similar to Karadja et al.'s (2015): in order to induce variations in the perception of one's SES, we draw on this work by using an informational treatment, which corrects misperceptions about one's position in the national income distribution. Thus, instead of an income shock in an economic game, we made use of an information shock about relative income. For this experiment, we selected British individuals who believed themselves to be poorer than they actually were (relative to others), and provided them with information about their actual position in the national income distribution. The experiment only targeted participants who held a wrong negative bias about their socioeconomic position in society. For ethical reasons, participants who believed that they were richer than they actually were did not receive any correction.

We reasoned that this positive information shock would be the psychological equivalent of an income shock. Our main hypothesis for this study is that a positive information shock about relative income decreases temporal discounting. As an exploratory hypothesis, we also predicted that the magnitude of the negative bias, and thus the intensity of the treatment, would moderate the e↵ect of the information shock on temporal discounting (i.e. the bigger the negative bias, the stronger the e↵ect of the treatment), in line with results from [START_REF] Karadja | Richer (and Holier) than Thou? The E↵ect of Relative Income Improvements on Demand for Redistribution[END_REF], who found an interaction between the magnitude of the bias and the e↵ect of their treatment. In addition, we made cross-sectional hypotheses concerning the relationships between pro-environmental attitudes and behaviour, temporal discounting and SES, in order to replicate Study 2's results. All theses hypotheses, as well as our methods and analysis plan were pre-registered (https://osf.io/bf8jv).

Materials and Methods

Participants

Participants were recruited via Prolific, relying on pre-screening criteria to filter the participants by nationality and approval rate: only British participants with a minimum approval rate of 90% were recruited. We also excluded students because their reported income may not reflect their actual living standards: students may earn no income but receive parental support. For the power analysis, we used measures of temporal discounting from Study 2. G*Power indicated that 807 participants were needed to be able to detect a minimum e↵ect of 0.2 with a power of 80%. Adding 10% to compensate for attrition, we thus aimed to include at least 888 participants. 910 eligible participants (i.e. with a negative bias) took part in the study. Our pre-registered exclusion criteria were similar to those used in Study 2. We excluded 21 participants who responded too fast (< 500 ms) or too slowly (> 2 minutes) to single question screens, and 16 participants who responded too fast (< 3 s) or too slowly (> 5 minutes) to the other survey pages. 11 participants who failed one or more catch trials in the temporal discounting task were removed from the analyses. In addition, we removed 7 participants who reported a personal monthly income above £12,500, in order to avoid outliers or potential reporting mistakes. This resulted in a total of 855 participants in the final dataset (52% females), aged between 23 and 74 years (M = 42 years; SD = 10.5). Participants' SES was higher than in the previous study: their average total monthly income was £2,736, ranging from £0 to £12,000 (SD = 1,258, see Table 5). Only 2 participants did not finish high school and 80% of them had either completed college or obtained a postgraduate degree.

Procedure

After providing informed consent, participants first answered questions about their SES and demographic characteristics. These answers were then used to calculate whether and by how much participants underestimated or overestimated their relative position in the British income distribution. The survey stopped there for participants who estimated their position accurately or overestimated it (null or positive bias). Those who underestimated their position (negative bias) were randomly assigned to the treatment or control condition. In the treatment condition, participants were presented with a correction of their position in the British society. After this information treatment, the rest of the survey was identical for both groups, where all participants completed the temporal discounting task followed by the environmental attitudes scales. In addition, questions about social trust were added to provide data for another project (Guillou et al., 2021). The measure of proenvironmental behaviour came last. Randomisation checks demonstrated that the two groups were balanced on gender ratio, age, income, educational level, subjective SES and bias (see Table 9 in the Supplementary Materials).

Measures

To measure pro-environmental attitudes and temporal discounting, we used the same environmental scales (see Table 1 in the Supplementary Materials) and discounting task as in Study 2 (see subsection 3.1.2). Materials, as well as the data and the R scripts used to analyse the data can also be found in the OSF (https://osf.io/5sbmr/files/).

• Socioeconomic Status

Concerning SES, we used similar questions as in Study 2, with minor modifications to increase their accuracy and reliability. As for income, we used the same question but we added another one aimed at decreasing reporting error: participants' annual income was calculated based on reported monthly income and they were then asked to confirm if it corresponded to their actual earnings. As for benefits, participants were asked to specify which amount they received for each type of benefit (instead of simply indicating a global amount, as in Study 2). Personal monthly income and benefits were used both for the calculation of objective SES (ztransformed and combined with education) and to evaluate our participants' actual position in the British income distribution.

Subjective SES was again calculated by combining the three items from Griskevicius et al. ( 2013) (↵ = .65, inter-item correlation = .38) and participants' response to the MacArthur Sc ale. We used a slightly modified version of the MacArthur scale focusing on income rather than education and jobs: "Think of this scale as representing where people stand in the United Kingdom. At the top of the scale [START_REF] Cogsdill | Inferring Character From Faces: A Developmental Study[END_REF] are the people who are the best o↵ in terms of overall income. At the bottom (1) are the people who are the worst o↵ in terms of overall income. Where would you place yourself on this scale?". The correlation between [START_REF] Griskevicius | When the Economy Falters, Do People Spend or 126 Save? Responses to Resource Scarcity Depend on Childhood Environments[END_REF] scale and the MacArthur Scale was similar to that found in Study 2 (r = 0.55, p < .001). The detailed list of all socioeconomic variables is available in the OSF-folder for this study (https://osf.io/yztx9/).

• Treatment: information shock

For all participants, the sum of their reported income and taxable benefits was calculated to determine which decile they belonged to. These calculations were based on statistics from the British government's Personal Income statistics release, which provides percentiles of total annual income (comprising taxable benefits).

This decile was compared to their answer to the MacArthur scale, so as to estimate the extent to which participants had a biased perception of where in the income distribution they were located. We define the bias of a participant as the di↵erence between their perceived and actual income decile. Participants who underestimated their relative income by 1 decile point or more were categorized as having a negative bias. Participants who overestimated their relative income by 1 decile point or more were categorized as having a positive bias. The remaining participants were defined as having no bias. For those who show no bias or a positive bias, the questionnaire came to an end, and only those with a negative bias were randomly assigned to the treatment or control condition. Participants in the treatment condition were presented with a correction of the previous answer they gave, thus receiving a subjective positive income shock, based on their reported income and taxable benefits.

• Pro-environmental behaviour

In Study 2, measuring pro-environmental behaviour with a donation question did not yield significant results. In this third study, we used a modified version of the question to improve its validity. Participants were told that we were going to give 10p to a research foundation for every person that participates in our study, and that they could choose the two causes they preferred. We presented them with the following options: research for climate change mitigation, medical cancer research, research in education sciences, space exploration research, research on Alzheimer disease, or research in economic policies. We only included causes that are relevant for British participants and we presented the various options in general terms (e.g., climate change mitigation as opposed to reforestation programmes), in an e↵ort to reduce noise. Answers were transformed into a binary pro-environmental variable (presence of the cause "Research for climate change mitigation" among the two choices = 1, absence = 0). Based on participants' choices, we e↵ectively donated the corresponding amount to relevant organizations afterwards.

Statistical analyses

We used an independent t-test to test our main hypothesis that mean discount rate is di↵erent between the control and the treatment groups. The exploratory hypotheses were then tested with regression analyses. To test our hypothesis about the moderating e↵ect of the magnitude of the negative bias, the following preregistered model was used:

DiscountRate = 10 + 11 T reatment + 12 Bias + 13 T reatment ⇤ Bias + ✏ (6)
Where Treatment corresponds to a binary variable indicating whether a participant is in the control group or the treatment group, Bias is a continuous variable referring to the di↵erence between perceived and actual income decile, and Discount Rate corresponds to the area under the curve calculated with the discounting task. In addition, following our mediation model we expected that a treatment impacting time preferences would have a downstream e↵ect on pro-environmentalism. Therefore we also predicted a moderated e↵ect of the treatment on pro-environmental attitudes and behaviour:

Environmentalism = 14 + 15 T reatment + 16 Bias + 17 T reatment ⇤ Bias + ✏ (7)
Finally, since we aimed to replicate our previous findings, we conducted the same cross-sectional analyses as in Study 2. All variables were calculated as in Study 2 and the regression models had the same specifications:

Environmentalism = 10 + 11 SES + ✏ (8) DiscountRate = 12 + 13 SES + ✏ (9) Environmentalism = 14 + 15 SES + 16 DiscountRate + ✏ (10)
The significance of the indirect pathway was again tested with bootstrapping, using R mediation package [START_REF] Tingley | mediation: R Package for Causal Mediation Analysis[END_REF].

Results

Descriptive statistics for key variables are reported in Table 5, while Table 6 provides the regression estimates for the exploratory models. Figures 1 and2 in the Supplementary Materials display the distribution of bias respectively in the sample of British candidates who were screened for eligibility to the study, and in the final sample of negatively biased participants who were retained (N = 855). As opposed to Karadja et al.'s (2015) study in which bias distribution was substantially skewed to the right (indicating that a majority of their Swedish respondents underestimated their position), the distribution in Figure 1 is normal. Although this sample is not representative, this suggests that Britons might not underestimate their relative wealth as much as the Swedes. Participants were not homogeneously distributed across the various levels of bias, with a majority displaying a bias lower than 2 (i.e. underestimating their relative income by less than 2 deciles). Therefore, we pooled participants in broader categories: participants who underestimated their relative income by 1 decile were classified as "very low bias", those who underestimated their relative income by 2 were classified as "low bias", those who underestimated their relative income by 3 were classified as "medium bias" and those above 4 were classified as "high bias".

Main and exploratory analyses

Contrary to our hypotheses, the information shock had no impact on temporal discount rates, which were not di↵erent in the control group and the treatment group (t = 0.15938, p = .87); and there was no moderated e↵ect of the treatment on temporal discounting depending on the magnitude of the bias (see Table 6).

However, additional analyses revealed that the impact of the treatment on proenvironmental behaviour was a↵ected by the magnitude of the bias: although we observed no main e↵ect of the treatment on pro-environmental behaviour in the full sample ( = 0.02, p = .569), participants in the medium bias and high bias groups responded more to the treatment than participants in the very low bias group (medium bias: = 0.21, p = .026; high bias: = 0.21, p = .049, see Table 6). Similar results were found for pro-environmental attitudes: participants with a high bias responded more to the treatment than those with a very low bias ( = 0.42, p = .047, see Table 6). These interactive e↵ects are represented in Figure 2. The fact that, in contrast to Study 2, the mediated association between subjective SES and pro-environmental behaviour became significant in Study 3 can be attributed to changes in the donation question, that were aimed at improving its validity. This seems to bring additional support for our mediation model. However, it should be noted that this can also be attributed to di↵erences in the samples. Study 3 is not a perfect replication of the previous one, as we only retained participants who had a negative perception bias concerning their position in the income distribution. This a↵ected the composition of the sample: in particular, participants in Study 3 had in average higher incomes, educational levels and AUCs than in Study 2 (see Tables 2 and5).

Finally, we also examined the separate e↵ects of revenue and education levels. As in the previous study, we found that education had a stronger mediated e↵ect ( = 0.010, p = .03) than revenue ( = 0.006, p = .04), and that revenue had no direct e↵ect on pro-environmental attitudes ( = 0.010, p = .77, see Table 11 in the Supplementary Materials).

Discussion

Prior research has shown that time preferences can shift in response to an experimental shock, for example by exposing participants to positive or negative income shocks in economic games (Haushofer et al., 2013), or by using narratives about a sudden change to one's future income (an experimental treatment also known as episodic future thinking, see [START_REF] Bickel | Stuck in Time: Negative Income Shock Constricts the Temporal Window of Valuation Spanning the Future and the Past[END_REF]Liu et al., 2013;[START_REF] Mellis | Less is more: Negative income shock increases immediate preference in cross commodity discounting and food demand[END_REF][START_REF] Sze | Bleak Present, Bright Future: Online Episodic Future Thinking, Scarcity, Delay Discounting, and Food Demand[END_REF]. In our experiment however, providing participants with positive information about their relative income did not have an e↵ect on temporal discounting. But this treatment had positive interactive e↵ects on environmentalism, such that participants who were the most biased about their relative income responded to the treatment with more positive pro-environmental behaviour and attitudes. The fact that the e↵ects are not mediated by temporal discounting was not anticipated and our data do not allow us to identify the causal channel of change.

One possibility is that, rather than activating considerations about the future, our experimental treatment activated considerations about fairness, which may have had a downstream e↵ect on participants' pro-environmental behaviour. There is indeed a wealth of studies showing that correcting false beliefs about socioeconomic position heightens concerns about inequalities for people who learn that they are poorer than they thought relative to others, while those who learn they are relatively richer put less priority on fairness and redistribution [START_REF] Cruces | Biased perceptions of income distribution and preferences for redistribution: Evidence from a survey experiment[END_REF][START_REF] Hvidberg | Social Position and Fairness Views[END_REF][START_REF] Karadja | Richer (and Holier) than Thou? The E↵ect of Relative Income Improvements on Demand for Redistribution[END_REF][START_REF] Mijs | How Information about Inequality Impacts Belief in Meritocracy: Evidence from a Randomized Survey Experiment in Australia, Indonesia and Mexico[END_REF][START_REF] Nair | Misperceptions of relative a✏uence and support for international redistribution[END_REF]. On the other hand, there is evidence that the importance that people place on fairness a↵ects their willingness to protect the environment: studies show that people are reluctant to support environmental public policies such as the carbon tax mainly because they believe it to be unfair [START_REF] Douenne | Yellow vests, carbon tax aversion, and biased beliefs[END_REF][START_REF] Sommer | Supporting carbon taxes: The role of fairness[END_REF]. Another example of the importance of fairness concerns can be found in an annual French survey about social representations of climate change: when respondents are asked on what condition they are willing to make significant lifestyle changes to reduce their emissions, their priority since 2015 has systematically been that e↵orts be shared fairly among all members of society (ADEME, 2020). In our study, participants in the treatment group might have been less concerned by fairness and more willing to accept environmental measures and sacrifices that are generally deemed unfair. But this interpretation remains speculatory since we did not measure participants' attitudes about fairness in our study.

In addition, this last study globally replicated the correlational results of the previous study: as in Study 2, we found that the association between SES and proenvironmental attitudes was mediated by temporal discounting. We also observed a mediated e↵ect of subjective SES on pro-environmental behaviour. Thus, Study 3 brings additional support for our mediation hypothesis, although further research with experimental data is needed to ascertain the robustness of this causal model.

General discussion

The present investigation is related to a vast literature on the relationships between SES and pro-environmental attitudes and behaviour. Relying on psychological theories of poverty, we aimed to gain a better understanding of the mechanisms underlying this relationship. We hypothesized that temporal discounting is one of the factors accounting for socioeconomic di↵erences in pro-environmentalism. Our three studies provide support for this mediation hypothesis. Our results across all studies are compatible with the idea that the correlation between SES and environmental attitudes is partially mediated by time preferences. In Study 3, we found that providing participants with positive information about their relative income had a heterogeneous e↵ect on pro-environmental attitudes and behaviour, but contrary to our hypothesis, the treatment had no impact on temporal discounting.

This research makes several contributions to the literature. First, our studies are the first to test the hypothesis that time preferences mediate the e↵ect of SES on environmentalism. Social scientists have long been interested in the influence of SES on environmental attitudes [START_REF] Buttel | Social class and mass environmental beliefs: A reconsideration[END_REF][START_REF] Diamantopoulos | Can socio-demographics still play a role in profiling green consumers? A review of the evidence and an empirical investigation[END_REF][START_REF] Harry | Conservation: An upper-middle class social movement[END_REF], but few studies have explored the psychological mechanisms that underpin this relationship [START_REF] Eom | Social class, control, and action: Socioeconomic status di↵erences in antecedents of support for proenvironmental action[END_REF].

Second, surveys have often focused on objective markers of SES such as profession, income and education, rather than using self-report scales measuring individuals' subjective experience. Our studies rely on measures of SES that include both subjective and objective indicators. This allowed us to detect that subjective measures are correlated with pro-environmental attitudes in the same way as objective markers of SES. However, subjective measures appear to have a more consistent e↵ect than income. Results from Study 1 for instance, indicate that education and subjective SES are associated with several types of pro-environmental attitudes, whereas income was correlated positively with pro-environmentalism but not with willingness to increase green taxes and public spending. This is coherent with previous research showing that education is a stronger predictor of pro-environmentalism than income, which can have opposite e↵ects on di↵erent types of pro-environmental attitudes [START_REF] Blankenberg | On the determinants of pro-environmental behavior -a guide for further investigations[END_REF][START_REF] Lo | National income and environmental concern: Observations from 35 countries[END_REF]Marquart-Pyatt, 2012;[START_REF] Pearson | Race, Class, Gender and Climate Change Communication[END_REF]. Thus, although the relationship between socio-demographic factors and environmental attitudes has been much studied, Study 1 contributes to the literature on this topic by using more comprehensive measures of SES, while relying on a very large French sample representing a broad range of the population's diversity.

Third, by highlighting the role of time preferences as a potential mediator of the relationship between SES and pro-environmentalism, studies 2 and 3 bring together two streams of research about pro-environmental attitudes and behaviours that have rarely converged into a common approach: one which focuses on socio-demographic determinants and the other on social psychological determinants [START_REF] Dietz | Social structural and social psychological bases of environmental concern[END_REF].

This potential role of time preferences may be of particular interest for policymakers. Having a better understanding of the factors that drive pro-environmental attitudes and behaviour is indeed crucial for decision-makers who seek to implement environmental policies. A growing literature in psychology has started to address these issues (see [START_REF] Nielsen | How psychology can help limit climate change[END_REF]. For instance, in a recent article, [START_REF] Eom | Social class, control, and action: Socioeconomic status di↵erences in antecedents of support for proenvironmental action[END_REF] examined the influence of social class, beliefs in climate change and sense of control on pro-environmental action. Their studies suggest two types of strategies for promoting green behaviour: e↵orts should focus on changing the beliefs of high SES individuals who do not believe in climate change, and on giving a greater sense of control to low SES individuals who do believe in climate change.

Our mediation model could suggest a similar two-way strategy to promote proenvironmental behaviour, depending on SES: since higher SES individuals tend to be more future-oriented, communication focused on the future costs of unmitigated climate change or biodiversity loss might be more convincing for them than for lower SES individuals. For the latter, highlighting the proximal consequences of environmental issues and bringing them psychologically closer could be more e cient. However, despite this being a frequent suggestion to increase individuals' willingness to act for the environment, empirical research testing this proximizing approach has not consistently revealed the expected positive e↵ects on climate-friendly behaviour so far [START_REF] Brügger | Psychological responses to the proximity of climate change[END_REF][START_REF] Spence | The psychological distance of climate change[END_REF]. Mixed results suggest that the effects of proximizing are more complex than is commonly assumed. Further research on how it a↵ects the willingness to act of di↵erent segments of the population is needed before large-scale interventions relying on proximizing are implemented.

The experimental results of Study 3 also suggest other directions for future research on the relationship between SES, temporal discounting and environmentalism. Since providing information about relative income does not seem to impact discount rates, future research aiming to study the causal impact of SES on time preferences and environmentalism should rather focus on narratives and informational treatments which highlight changes in absolute levels of income. For this purpose, the use of episodic future thinking about income shocks, which consists in projecting the self into the future to pre-experience a positive or negative income shock, has proven its e cacy to shift temporal discounting [START_REF] Bickel | Stuck in Time: Negative Income Shock Constricts the Temporal Window of Valuation Spanning the Future and the Past[END_REF][START_REF] Sze | Bleak Present, Bright Future: Online Episodic Future Thinking, Scarcity, Delay Discounting, and Food Demand[END_REF]. Similarly, some studies have shown that engaging in episodic future thinking about climate change-related risks and climate change mitigation leads to acting pro-environmentally [START_REF] Ho | Envisaging Mitigation Action Can Induce Lower Discounting toward Future Environmental Gains and Promote Pro-Environmental Behavior[END_REF][START_REF] Lee | Using episodic future thinking to preexperience climate change increases pro-environmental behavior[END_REF]. Future research could assess whether episodic future thinking about income shocks has a similar e↵ect on pro-environmental behaviour, and whether this e↵ect is partially mediated by temporal discounting. Another possibility would be to test the impact of influencing beliefs about relative education level, rather than income. Since we found that education was the main driver of the mediated e↵ect of objective SES on pro-environmentalism, this might be a more e↵ective experimental treatment than providing information about income.

In addition, the interactive e↵ects of the subjective positive income shock on proenvironmental attitudes and behaviour evidenced in Study 3 suggest new avenues for research and interventions targeting pro-environmental behaviour. Additional surveys are needed to understand the psychological mechanisms behind this ob-served treatment e↵ect. Since previous research has repeatedly shown that such a treatment has an e↵ect on attitudes about fairness and redistribution, future studies could explore how socioeconomic di↵erences in pro-environmentalism might be related to attitudes about fairness and redistribution.

Future research should also examine the extent to which the results generalize to other countries. Our hypotheses are not country-specific because several multicountry studies have found systematic patterns of relationship between income, proenvironmental attitudes and behaviours on the one hand [START_REF] Lo | National income and environmental concern: Observations from 35 countries[END_REF]Marquart-Pyatt, 2012;[START_REF] Pisano | Environmental Behavior in Cross-National Perspective: A Multilevel Analysis of 30 Countries[END_REF] and between temporal discounting and income on the other [START_REF] Wang | How time preferences di↵er: Evidence from 53 countries[END_REF]. However, it will be important to validate this model empirically. Even within the same country, there could also be variations according to individuals' ethnicity. Including participants' ethnicity as a control variable may be especially relevant, since it is generally strongly correlated with SES.

There are several limitations to this research, the first being the absence of experimental evidence for our partial mediation model. Thus, di↵erent theories about how time preferences relate to pro-environmentalism and SES should be considered. Another possible limitation of our last two studies concerns the measurement of proenvironmental behaviour: unlike environmental attitudes, there is no standardized measure of pro-environmental behaviour for online studies [START_REF] Lange | Measuring pro-environmental behavior: Review and recommendations[END_REF]. While Study 2 appears to validate our mediation model with respect to environmental attitudes, no significant mediated relationship was found for behaviour. It remains unclear whether we did not find the same results because of an essential di↵erence between pro-environmental behaviour and attitudes, or because of a lack of validity of our behavioural measure. In Study 2, pro-environmental behaviour measured by a donation to reforestation programmes showed only a low to medium correlation with a validated pro-environmental attitudes scale [START_REF] Milfont | The environmental attitudes inventory: A valid and reliable measure to assess the structure of environmental attitudes[END_REF]. Many studies have evidenced a value-action gap in the environmental domain (see [START_REF] Kollmuss | Mind the Gap: Why do people act environmentally and what are the barriers to pro-environmental behavior?[END_REF], therefore this discrepancy between attitudes and behaviour does not come as a surprise. However, the magnitude of this discrepancy is surprising, as the correlation is still lower than could be expected. Other considerations related to the alternative donation choices o↵ered to participants may also call into question the validity of the behavioural measure that we used. In Study 3, a transformation of the variable to measure donation to a more global environmental action, and the use of di↵erent alternative choices led to a higher correlation with attitudes, which suggests a higher validity of the measure. In addition, there was a mediated e↵ect of subjective SES on pro-environmental behaviour which was similar to that observed for pro-environmental attitudes. But these variations from Study 2 to Study 3 could also be attributed to di↵erences in the samples. Further research could be conducted with other measures of proenvironmental behaviour to test the robustness of our mediation model with respect to behaviour.

Another limitation might be that the individual discount rates inferred from a monetary discounting task cannot be considered as pure measures of time preferences: responses can be influenced by participants' budgetary constraints and the interest rates of the markets to which they have access (see [START_REF] Frederick | Time discounting and time preference: A critical review[END_REF][START_REF] Wang | How time preferences di↵er: Evidence from 53 countries[END_REF]. There has been considerable discussion in the literature as to whether observed socioeconomic di↵erences in temporal discounting actually reflect di↵erences in preferences, or whether they may instead reflect actual or perceived liquidity constraints in conditions of poverty (Haushofer et al., 2013). Thus, it would be interesting to see whether our results replicate using non-monetary rewards in the discounting task, such as environmental gains. However, it is worth noting that according to previous research, individuals discount environmental outcomes in a similar way to monetary outcomes [START_REF] Hardisty | Discounting future green: money versus the environment[END_REF].

Finally, it should be emphasized that temporal discounting seem to explain only a small part of the association between SES and pro-environmental attitudes: correlational results from Studies 2 and 3 highlight very small e↵ect sizes, and the mediated e↵ect of objective SES was significantly weaker than the direct e↵ect. Other social psychological factors, such as sense of control, may be stronger mediators of the relationship between SES and pro-environmentalism. Studies which evaluate the relative contribution of di↵erent factors would thus be welcome. These weak e↵ect sizes also suggest that the association between SES and pro-environmentalism is complex and may depend on types of behaviours or contexts. In addition, there are other factors that we did not incorporate in our mediation model and that are known to moderate the relationship between SES and pro-environmentalism, such as political orientation and climate change beliefs (see [START_REF] Ballew | Does socioeconomic status moderate the political divide on climate change? The roles of education, income, and individualism[END_REF][START_REF] Ehret | Support for environmental protection: an integration of ideological-consistency and information-deficit models[END_REF][START_REF] Eom | Social class, control, and action: Socioeconomic status di↵erences in antecedents of support for proenvironmental action[END_REF].

Despite its limitations, the present research contributes to a scarce literature that examines the psychological mechanisms underlying the relationship between socio-demographic variables and pro-environmental attitudes and behaviour. Our three studies give weight to the hypothesis that people's preference for the future is a partial mediator of this relationship. Study 3 also shows that information about relative SES impacts pro-environmentalism via other paths. Since having a fine-grain understanding of the antecedents of pro-environmental behaviour can be consequential from an environmental policy perspective, it would be fruitful to continue exploring these lines of research.

Partial replication on Singaporean MPs

This replication is subject to a separate pre-registration (https://osf.io/h9qrk).

In order to evaluate the robustness and generalizability of our results, we decided to replicate the same type of analysis on Asian MPs. The idea was to use non-Caucasian faces (even if there are some in the French MPs, they are not the majority). We also wanted them to come from a country with a different history than France.

We first tried to obtain the photographic portraits of the Chinese, South Korean and Japanese deputies but without success. Either they were not available on the internet or their quality was too poor. We even tried to contact directly these assemblies by phone and email but without success. Fortunately, we finally found the website of the Singaporean parliament (https://www.parliament.gov.sg) where pictures of the MPs from 1959 to today are available. From the website of the Parliament of Singapore, composite pictures (one per parliament) composed of the pictures of all MPs were downloaded (https://www.parliament.gov.sg/mps/composite-photos). This exhaustive sample, composed of the 14 available composite pictures, corresponding to the 14 successive parliaments lead to 1,234 faces detected by OpenFace. GDP data were collected from World Inequality Database (https://wid.world) and where then scaled to make its estimate in the models interpretable.

The same methods as those used for the French MPs photographs were used on the Singaporean have access to the dates of birth. Therefore, the only quality check concerned the gender effect. For the analysis of the effect of time, age could not be added as a control variable.

In a linear model with time and perceived facial dominance as control variables, male gender is negatively associated with facial perceived trustworthiness (b = -0.35, p = 0.001), (see table 7, model 1). As this quality check is verified, we were able to continue with the analyses.

Our main hypothesis, that perceived facial trustworthiness evaluations were correlated with time, was tested using a linear model. In a model with facial perceived trustworthiness as dependent variable, time as variable of interest and gender and perceived facial dominance as control variables, time is positively correlated with perceived facial trustworthiness (unstandardized b = 0.011, p < 0.001), such that, as time increases, perceived facial trustworthiness increases (see table 7, model 1). Our hypothesis that perceived facial trustworthiness levels increased with time is confirmed.

Our second hypothesis that the increase in GDP is a potential cause for the increase in perceived facial trustworthiness over time was tested using a second linear model including GDP per capita instead of time as variable of interest (see table 7, model 2). In this model, GDP per capita is positively correlated with perceived facial trustworthiness (b = 0.233, p < 0.001). In a last model including both time and GDP per capita, we saw that GDP per capita is still positively correlated with facial perceived trustworthiness (b = 0.893, p < 0.001).

Table 7. Replication on Singaporean MPs.

Finally, to know if it is the GDP or the time that is the best predictor of the perceived facial trustworthiness, we computed Bayes Factors. Comparing the model with GDP and controls (table 7, model 2) and the one with time and controls (table 7, model 1), we found that the model with GDP better explains the data than the one with time (BF = 57.3). Similarly, we found that the model including both time and GDP better explains the data than the model including only time (BF = 291.0).

To conclude, we found that perceived facial trustworthiness of Singaporean MPs increased over time and that this increase is better explained by GDP than by time. Therefore our results on French MPs are confirmed on non-Caucasian faces from non-European country. This gives them a lot of strength as they are cross-cultural and therefore more generalizable.

Figure 2 .

 2 Figure 2. Examples of polyphenism.

Figure 3 .

 3 Figure 3. Examples of reaction norms.

Figure 1 .

 1 Figure 1. Medians of temporal discounting curves of the control (blue) and treated (red) groups.The upper bound of error bars is the third quartile, the lower bound is the first quartile. The distance between them is the interquartile range. Within each plot, the three medians are significantly different (all p < .001 for paired Wilcoxon tests). This type of visualization was chosen because indifference points are not normally distributed.

figure 1

 1 figure1for examples). Pictures were collected by taking high quality photographs of every book page(from 1906 to 2002). Concerning the three most recent legislatures(2007, 2012 and 2017) pictures were directly digitized and were given by the French NationalAssembly. 1914 and 1936 are the only other legislatures information (birth date, death date, department of election, political party, etc) was given by the Archives.

  Figure 1 2017).

Figure 2 .

 2 Figure 2. A well detected face (on the left) and a badly detected one (on the right) from our series.

Figure 3 .

 3 Figure 3. Perceived facial trustworthiness across time. Points represent the mean of each legislature and the bars the 95 % Confidence Intervals (CI). The blue line corresponds to the approximated mean and the grey area represents the 95 % CI.

  with perceived facial trustworthiness (from Clio infra: b = 0.53, p < 0.001, from WID: b = 0.47, p < 0.001). Regional GDP is also significantly positively correlated with facial perceived trustworthiness (b = 0.086, p < 0.001) as well as mean national income from INSEE and WID (respectively b = 0.38, p < 0.001 and b = 0.47, p < 0.001), regional income (b = 0.29, p < 0.001) and per adult national wealth (b = 0.65, p < 0.001). In line with our second hypothesis, economic development, represented here by GDP per capita, seems to explain the increase over time in perceived facial trustworthiness.On the opposite, national, regional and departmental unemployment are significantly negatively correlated with perceived facial trustworthiness (respectively b = -0.081, p < 0.001, b = -0.040, p < 0.001, b = -0.026, p < 0.001) as well as national poverty (b = -0.37, p < 0.001) and top 1 percent shares of income and wealth (respectively b = -0.27, p < 0.001, b = -0.31, p < 0.001).

  oAU12. Which explains why they capture a high part of the variance of perceived facial trustworthiness. No qualitative change was observed in the time effect (b = 0.008, p < 0.001), national GDP effect (b = 0.27, p < 0.001) or national unemployment (b = -0.072, p < 0.001) (see table 4 in supplementary materials in the general appendix).

Figure 1 .

 1 Figure 1. Power analysis using data from the National Portraits Gallery (Safra et al., 2020).

Figure 2 .

 2 Figure 2. Examples of well-detected faces with landmarks from OpenFace. On the left: Christian VIII of Denmark (1786-1848), in 1831. On the right: Maria Anna of Austria (1683-1754), queen consort of Portugal, in 1729.

Figure 3 .

 3 Figure 3. Variations of mean perceived facial trustworthiness through time.

  measured the occurrence of cooperation-related and dominance-related words in French and British theater plays. They showed that pro-sociality increased over time in both France and Great Britain between the early 17th and late 19th centuries. Our results are also in line with the article Safra et al. 2020 and the one of Guillou et al. in prep. These last two articles showed with methods similar to ours but on quite different data that perceived facial trustworthiness had increased over time. Guillou et al. showed this on photographs of 20th century French deputies. Safra et al. showed it on data much more similar to those used in this paper: portraits painted between the 15th and 20th centuries. The major difference in terms of data with this paper is that the portraits of Safra et al. were taken from only 2 databases: the

Figure 1 .

 1 Figure 1. Examples of well-detected faces with landmarks from OpenFace. On the left: Laurent-Jean-François, comte Truguet, amiral de France (1772-1842) by Jean-Baptiste-Paulin Guérin in 1835. On the right: Anne de Rohan-Chabot, princesse de Soubise, by an anonymous painter, between 1600 and 1700.

Figure 3 .

 3 Figure 3. Comparison of the densities. Green: Web Gallery of Art. Dark green: National Portraits Gallery. Blue: European sovereigns. Red: Versailles.

  //archive.org/details/archiveselectoralesducevipof). It would be possible to compare what is said in the text with the facial expressions of the face on the leaflet. Several technical problems made it impossible to use them during my thesis, but, as the Cevipof is working on making this database easier to use for researchers, it could be used by future students of the team.

Figure 1 :

 1 Figure 1: Area under the curve of high and low SES participants This figure displays the mean indi↵erence points as a function of time for high and low SES participants. Three indi↵erence points are plotted on the graph, as the questionnaire contained three di↵erent delays. Error bars represent standard error of the mean.

Figure 2 :

 2 Figure 2: Interactive e↵ects of the treatment and the magnitude of the bias on proenvironmental attitudes and behaviour

Figure 1 .

 1 Figure 1. Examples of avatars from FaceGen. On the left, a face perceived as highly trustworthy and low dominant, on the right, a face perceived as low trustworthy and highly dominant.

Olivier Morin, Pierre Jacquet, Niels Letinga, Paul Ecoffet, Julien Lie-Panis, Mélusine Boon-Falleur, Léo Fitouchi, Aurore Grandin, Edgar Dubourg

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	mon stage de M2 se passe le mieux possible et que je réussisse. Je pense que tu es une
	encadrante exceptionnelle.
	chance de te croiser sur leur route.

de ma précédente plus importante rencontre scientifique. Michel Raymond alors que je désespérais de trouver un stage de césure en paléontologie et que je ne savais pas quel master choisir. À la fin de notre première entrevue, Michel demandé un petit fossile . Dans la p . Malgré cette petite erreur Michel a vite accepté de me prendre en stage de césure puis en stage de M2. Je ne peux pas dire à quel point je lui suis s dans équipe de Biologie Évolutive scientifiquement, techniquement, socialement, sur tous les plans. Pour en revenir à cette thèse, je voudrais remercier chaleureusement Lou Safra. Merci été aussi disponible, surtout sur la fin de ma thèse en prenant de plus en plus part à mon encadrement. Ta bonne humeur et ta justesse technique m ! un petit mot de remerciement pour Maria Giavazzi qui a été une excellente mentor, toujours disponible pour discuter, (Dark !!!), Laudine Carbuccia, Mathilde Mus, Amine Sijilmassi, Charles de Dampierre et Anne-Sophie Hacquin (d gang des agros . Et plus généralement au DEC, merci à Hualin Xiao et Antoine Marie. : Aurore Grandin t plus besoin de mon aide sur qualtrics), Faniriana Rakoto Endor, Alexis Garsmeur, Saeyeon Kwon et Marius Mercier. Vous avez tous été des bouffées ! Merci à ma famille et plus particulièrement à mes parents, Alain et Isabelle professionnelle, même la dernière en date qui devrait suivre cette thèse. permis de recouvrir le balcon de plantes, au point de ne plus pouvo nourri ma curiosité à coup de documentaires animaliers et de livres sur les dinosaures. Et merci aussi à mes amis : Jules, David et Hugo ; Adam, Guillaume, Robin et Nils ; Kévin ; Mélina ; Éloïse, et les autres Évidemment, je ne peux clôturer cette séquence de remerciements sans parler de Mathilde. Ta qu que dans ta tête ça va trop vite pour moi).

  ). r-selection is the result, for instance, of variable and unpredictable climate, population size and competition. It favors,

among other things, rapid development, early reproduction and small body size. On the opposite K-selection, related, for instance, to a constant or predictable climate and a population at the equilibrium, favors among other things, a slow development, delayed reproduction and larger

Table 1 .

 1 Descriptive statistics.

	Statistic	Mean St. Dev. Min Max
	Age (years)	39.8	10.5	21	72
	Education score (1-6)	4.1	0.9	1	5
	McArthur ladder (1-10) perceived 5.2	1.5	1	9
	McArthur ladder correct	7.5	1.8	2	10
	Total income	2,736.3 1,257.6 1,200 12,000
	AUC	20,501.4 12,934.1 709.0 49,275.5
	Total social trust	-0.0	2.5	-5.4	5.7
	Griskevicius score	12.7	3.3	3	21

Table 2 .

 2 Baron and Kenny (1986) steps procedure, Study 1.

				Dependent variable:
		Total Trust	AUC	Total Trust
			(1)	(2)	(3)
	Intercept		0.000	-0.000	0.000
			(0.034)	(0.034)	(0.034)
	Objective SES		0.168 ***	0.135 ***	0.153 ***
			(0.034)	(0.034)	(0.034)
	AUC				0.111 **
					(0.034)
	Observations		855	855	855
	R 2		0.028	0.018	0.040
	Adjusted R 2		0.027	0.017	0.038
	Residual Std. Error	0.986 (df = 853)	0.991 (df = 853)	0.981 (df = 852)
	F Statistic 24.767 Intercept	0.000	-0.000	0.000
			(0.033)	(0.034)	(0.033)
	Subjective SES		0.264 ***	0.197 ***	0.248 ***
			(0.033)	(0.034)	(0.034)
	AUC				0.083 *
					(0.034)
	Observations		855	855	855
	R 2		0.070	0.039	0.076
	Adjusted R 2		0.069	0.038	0.074
	Residual Std. Error	0.965 (df = 853)	0.981 (df = 853)	0.962 (df = 852)
	F Statistic	63.821 *** (df = 1; 853) 34.569 *** (df = 1; 853) 35.116 *** (df = 2; 852)
	Notes:	* p<0.05 ** p<0.01 *** p<0.001
		Numbers in parentheses represent standard errors.

*** (df = 1; 853) 15.807 *** (df = 1; 853) 17.867 *** (df = 2; 852)

Table 3 .

 3 Mediation analysis Study 1.

	Type of SES	Statistic	Estimate	95 % CI Lower	95 % CI Upper
		ACME	0.016 **	0.005	0.03
	Objective	ADE	0.168 ***	0.097	0.24
		Total Effect	0.185 ***	0.112	0.26
		Prop. Mediated	0.090 **	0.028	0.19
		ACME	0.023 *	0.005	0.04
		ADE	0.347 ***	0.257	0.44
	Subjective	Total Effect	0.369 ***	0.275	0.46
		Prop. Mediated	0.062 *	0.012	0.13
	Notes:

* p<0.05 ** p<0.01 *** p<0.001 ACME: Average Causal Mediating Effect; ADE: Average Direct Effect.

Table 4 .

 4 Mediation analysis with Treatment.

	Type of SE Statistic	Estimate	95 % CI Lower	95 % CI Upper
		ACME	0.001	-0.017	0.02
	Objective	ADE	-0.060	-0.191	0.07
		Total Effect	-0.058	-0.058	0.08
		Prop. Mediated	-0.025	-1.210	1.16
	Notes:

* p<0.05 ** p<0.01 *** p<0.001 ACME: Average Causal Mediating Effect; ADE: Average Direct Effect.

Table 5 .

 5 Descriptive statistics.

	Statistic	Mean St. Dev. Min Max
	Age (years)	45.6	15.3	19	87
	Education score (1-6)	3.7	1.1	1	5
	McArthur ladder (1-10) perceived 4.8	1.7	1	10
	McArthur ladder correct	3.2	3.0	1	10
	Total income	1,157.8 1,380.4 0 10,000
	AUC	20,387.9 12,666.7 709.0 49,275.5
	Total social trust	-0.0	2.5	-5.9	5.2
	Griskevicius score	12.0	3.6	3	21

Table 6 .

 6 Baron and Kenny (1986) steps procedure, Study 2.

				Dependent variable:
		Total Trust	AUC	Total Trust
			(1)	(2)	(3)
	Intercept		0.000	0.002	-0.0003
			(0.030)	(0.029)	(0.029)
	Objective SES		0.096 **	0.136 ***	0.069 *
			(0.030)	(0.030)	(0.029)
	AUC				0.194 ***
					(0.029)
	Observations		1,130	1,130	1,130
	R 2		0.009	0.019	0.046
	Adjusted R 2		0.008	0.018	0.044
	Residual Std. Error	0.996 (df = 1128)	0.992 (df = 1128)	0.978 (df = 1127)
	F Statistic 10.383 Intercept	0.000	0.002	-0.0003
			(0.029)	(0.029)	(0.028)
	Subjective SES		0.260 ***	0.209 ***	0.227 ***
			(0.029)	(0.029)	(0.029)
	AUC				0.156 ***
					(0.029)
	Observations		1,130	1,130	1,130
	R 2		0.067	0.043	0.091
	Adjusted R 2		0.067	0.043	0.089
	Residual Std. Error	0.966 (df = 1128)	0.979 (df = 1128)	0.954 (df = 1127)
	F Statistic	81.445 *** (df = 1; 1128) 51.233 *** (df = 1; 1128) 56.197 *** (df = 2; 1127)
	Notes:	* p<0.05 ** p<0.01 *** p<0.001
		Numbers in parentheses represent standard errors.

** (df = 1; 1128) 21.394 *** (df = 1; 1128) 27.230 *** (df = 2; 1127)

Table 7 .

 7 Mediation analysis Study 2.

	Type of SES	Statistic	Estimate	95 % CI Lower	95 % CI Upper
			ACME	0.037 ***	0.020	0.06
	Objective		ADE	0.097 *	0.016	0.18
			Total Effect	0.134 **	0.053	0.22
			Prop. Mediated	0.277 **	0.128	0.71
			ACME	0.047 ***	0.027	0.07
			ADE	0.325 ***	0.235	0.41
	Subjective		Total Effect	0.371 ***	0.283	0.46
			Prop. Mediated	0.126 ***	0.070	0.20
	Notes:	* p<0.05 ** p<0.01 *** p<0.001

ACME: Average Causal Mediating Effect; ADE: Average Direct Effect.

Table 1 .

 1 Descriptive statistics. The sample is composed of 877 females and 11,951 males.

	Statistic	Mean St. Dev. Min Max
	Facial perceived Trustworthiness 0.5	1.2	-2.7 2.7
	Facial perceived Dominance	0.4	1.0	-2.6 2.8
	Age (years)	50.0	10.3	23	89
	Party (far right 1-7 far left)	4.1	1.8	1.0 7.0
	National GDP	15,497 10,959 3,337 36,773

Table 2 .

 2 Models to assess quality checks.

		Dependent variable:
		Perceived Facial Trustworthiness
		(1)	(2)
	Intercept	-21.676 ***	-23.481 ***
		(0.768)	(0.733)
	Gender Male	-0.143 **
		(0.048)
	Age		-0.070 ***
			(0.011)
	Date	0.011 ***	0.012 ***
		(0.0004)	(0.0004)
	Observations	12,828	12,828
	Log Likelihood	-20,190.440	-20,176.250
	Akaike Inf. Crit.	40,390.870	40,362.500
	Bayesian Inf. Crit.	40,428.170	40,399.800
	Notes:

* p<0.05 ** p<0.01 *** p<0.001 Numbers in parentheses represent standard errors Then, the correlation between our proxy of social trust (perceived facial trustworthiness evaluations) and the actual social trust levels in France from the five waves of the European Values Study (EVS) was assessed. Mean facial perceived trustworthiness and generalized social trust from EVS are moderately but significantly correlated (rho = 13.4 %, p < 0.001). In a mixed-effects model with facial perceived trustworthiness as dependent variable, mean national generalized social trust as variable of interest and the controls described above, mean national generalized social trust is positively associated with perceived facial trustworthiness (b = 1.04, p < 0.001) (see table 1 in supplementary materials in the general appendix).

Table 3 .

 3 Outputs and Bayes Factors from models containing time or economic variables.

Table 4 .

 4 Bayes factors of models against the model with GDP and controls.

	Variable of interest in the model	Bayes factor
	National Unemployment	6.30e-41
	National Income	5.22e+13
	Poverty	2.30e-41
	Income Inequality	1.77e-54
	Wealth Inequality	9.36e-46

Table 1 .

 1 Descriptive statistics.

	Statistic	Mean St. Dev. Min Pctl(25) Pctl(75) Max
	Facial perceived Trustworthiness 0.85	1.37	-2.61 -0.29	1.99	2.77
	Facial perceived Dominance	-0.73	1.08	-2.55 -1.65	0.01	2.65
	Age (years)	34.45 17.35	1.00 21.62 46.38 96.50
	Painting Date	1703.39 113.74 1371.00 1612.50 1785.00 1999.00

The sample is composed of 329 female faces and 637 male faces. The distribution of the images between the different countries or regions is as follows: Benelux 5 portraits, France 86, Holy Roman Empire 59, Italy 265, Netherlands 63, Poland 35, Portugal 50, Russia 27, Scandinavia 163, Spain 23, United Kingdom 190.

Table 2 .

 2 Models to assess quality checks.

		Dependent variable:
		Perceived Facial Trustworthiness
		(1)	(2)
	Intercept	-1.999 ***	-2.286 ***
		(0.515)	(0.512)
	Perceived facial Dominance	-0.766 ***	-0.774 ***
		(0.032)	(0.033)
	Gender Male	-0.361 ***
		(0.072)
	Age		-0.008 ***
			(0.002)
	Date	0.001 ***	0.002 ***
		(0.0003)	(0.0003)
	Observations	966	966
	R 2	0.419	0.413
	Adjusted R 2	0.418	0.411
	Residual Std. Error (df = 962)	1.687	1.696
	F Statistic (df = 3; 962)	231.560 ***	225.622 ***
	Notes:	* p<0.05; ** p<0.01; *** p<0.001
		Numbers in parentheses represent standard errors.

Table 3 .

 3 Model assessing the effect of time.

		Dependent variable:
		Perceived Facial Trustworthiness
	Intercept	0.745 ***
		(0.100)
	Perceived facial Dominance	-0.744 ***
		(0.033)
	Gender Male	-0.364 ***
		(0.075)
	Age	-0.006 **
		(0.002)
	Date	0.181 ***
		(0.035)
	Observations	966
	Log Likelihood	-1,433.544
	Akaike Inf. Crit.	2,881.088
	Bayesian Inf. Crit.	2,915.200
	Notes:	* p<0.05; ** p<0.01; *** p<0.001
		Numbers in parentheses represent standard errors.

Table 4 .

 4 Models assessing the effect of GDP.

			Dependent variable:
		Perceived Facial Trustworthiness
		(1)	(2)	(3)
	Intercept	0.760 ***	0.712 ***	0.736 ***
		(0.105)	(0.111)	(0.109)
	Perceived facial Dominance	-0.745 ***	-0.757 ***	-0.756 ***
		(0.034)	(0.034)	(0.034)
	Gender Male	-0.364 ***	-0.330 ***	-0.327 ***
		(0.079)	(0.081)	(0.081)
	Age	-0.006 **	-0.006 **	-0.006 **
		(0.002)	(0.002)	(0.002)
	Date	0.160 ***		0.103 *
		(0.037)		(0.041)
	GDP per capita		0.180 ***	0.132 **
			(0.038)	(0.041)
	Observations	915	915	915
	Log Likelihood	-1,360.400	-1,358.635	-1,357.820
	Akaike Inf. Crit.	2,734.799	2,731.269	2,731.640
	Bayesian Inf. Crit.	2,768.532	2,765.002	2,770.192
	Notes:	* p<0.05; ** p<0.01; *** p<0.001
		Numbers in parentheses represent standard errors.
	For ease of reading and interpretation, the time (date of the portrait) and GDP per capita
	variables have been scaled. GDP per capita is positively correlated with perceived facial
	trustworthiness (b = 0.180, p < 0.001) (table 4). However, when time is added in the model, the
	effect of GDP is lowered (b = 0.132, p = 0.0014). To see which of GDP or time is a better
	predictor of perceived facial trustworthiness, Bayes factors have been computed. Their results
	are mixed. When comparing the model with GDP against the model with time (i.e., model 2
	against model 1, see table 4), the model with GDP performs slightly better (BF = 5.84). But
	comparing the model with GDP and time against the model with time alone (i.e., model 3

against model 1, see table 4), the model with GDP and time performs worse than the model with time alone (BF = 0.44). It is not clear that GDP is a better predictor of perceived facial trustworthiness than time.

Table 1 .

 1 Descriptive statistics.

	Statistic	Mean St. Dev. Min Pctl(25) Pctl(75) Max
	Facial perceived Trustworthiness 0.86	1.19	-2.59 -0.10	1.83	2.63
	Facial perceived Dominance	-0.54	1.09	-2.51 -1.49	0.21	2.62
	Age (years)	42.59 19.09	0.50 26.88 58.50 91.50
	Painting Date	1776.58 76.93 1483.00 1737.75 1837.00 1950.50

Table 2 .

 2 Quality checks

	0.015
	0.010
	Density
	0.005
	0.000
	1500	1600	1700	1800	1900
			Date

Table 3 .

 3 Model to assess the effect of time.

		Dependent variable:
		Perceived Facial Trustworthiness
	Intercept	1.307 *
		(0.654)
	Perceived facial Dominance	-0.760 ***
		(0.028)
	Gender Male	-0.001
		(0.002)
	Age	-0.344 ***
		(0.066)
	Date	-0.025
		(0.029)
	Observations	764
	R 2	0.589
	Adjusted R 2	0.587
	Residual Std. Error	1.246 (df = 759)
	F Statistic	271.973 *** (df = 4; 759)
	Notes:	* p<0.05; ** p<0.01; *** p<0.001
		Numbers in parentheses represent standard errors.

Table 1 :

 1 Relationships between environmental attitudes and socioeconomic status ⇤⇤⇤ p<0.001. This table estimates the association between SES and two environmental outcome variables. Objective SES is a composite score combining education and income.

		Dependent variable:
		Pro-environmentalism	Willingness to increase green
			taxes and spending
		(1)	(2)
	Objective SES	0.09 ⇤⇤⇤	0.06 ⇤⇤⇤
		(0.08,0.11)	(0.04,0.07)
	Intercept	0.00	0.00
		(-0.02,0.02)	(-0.02,0.02)
	Observations	15,924	15,924
	Adjusted R 2	0.01	0.003
	RSE (df = 15922)	1.00	1.00
	F Statistic (df = 1; 15922)	135.19 ⇤⇤⇤	52.96 ⇤⇤⇤
	Subjective SES	0.07 ⇤⇤⇤	0.04 ⇤⇤⇤
		(0.05,0.08)	(0.03,0.06)
	Intercept	0.00	0.00
		(-0.02,0.02)	(-0.02,0.02)
	Observations	15,924	15,924
	Adjusted R 2	0.005	0.002
	RSE (df = 15922)	1.00	1.00
	F Statistic (df = 1; 15922)	74.31 ⇤⇤⇤	31.60 ⇤⇤⇤
	Note: OLS regression coe cients. Confidence intervals in parentheses. † p<0.1; ⇤ p<0.05;
	⇤⇤ p<0.01;

Table 2 :

 2 Descriptive statistics

	Statistic	N	Mean	SD	Min	Max
	Education score	650	3.67	1.07	1	5
	Income (£)	650	935	1,187	0	12,000
	Benefits (£)	650	113	256	0	1,595
	Griskevicius items	650	12.04	3.61	3	21
	MacArthur Scale	650	5.43	1.56	1	10
	AUC (temporal discounting)	650	19,033	11,497	709	49,276
	Activism score	650	4.45	1.11	1	6.8
	Conservation score	650	5.17	0.95	2	7
	Pro-environmental behaviour	650	0.27	0.45	0	1
	Note: Activism score and Conservation score refer to Milfont and Duckitt's (2010) scales
	from the Environmental Attitudes Inventory, which were used to calculate participants' pro-
	environmental attitudes. See detailed definition of each variable in subsection 3.1.2.

Table 4 :

 4 Correlational mediation analysis based on bootstrapping

		Dependent variable: Pro-environmental attitudes
	Type of SES	Statistic	Estimate 95% CI Lower 95% CI Upper
		Average mediated e↵ect 0.031 ⇤⇤⇤	0.012	0.05
	Objective	Average direct e↵ect	0.093 ⇤	0.002	0.19
		Total e↵ect	0.123 ⇤	0.034	0.22
		Proportion mediated	0.249 ⇤	0.083	0.89
		Average mediated e↵ect 0.034 ⇤⇤⇤	0.013	0.06
	Subjective	Average direct e↵ect	0.081	-0.021	0.18
		Total e↵ect	0.115 ⇤	0.018	0.22
		Proportion mediated	0.298 ⇤	0.084	1.61
		Dependent variable: Pro-environmental behaviour
	Type of SES	Statistic	Estimate 95% CI Lower 95% CI Upper
		Average mediated e↵ect	0.006	0.002	0.02
	Objective	Average direct e↵ect	0.037	0.083	0.01
		Total e↵ect	0.031	0.077	0.02
		Proportion mediated	0.194	2.224	1.43
		Average mediated e↵ect	0.006	0.002	0.02
	Subjective	Average direct e↵ect	0.018	0.063	0.03
		Total e↵ect	0.012	0.058	0.04
		Proportion mediated	0.535	4.555	3.11
	Note: correlational mediation analyses testing for the e↵ect of objective and subjective
	SES on pro-environmental attitudes and behaviour via temporal discounting. Bootstrap-
	based coe cients and confidence intervals. N = 650. Sampling iterations = 1000. †
	p<0.1; ⇤ p<0.05; ⇤⇤ p<0.01; ⇤⇤⇤ p<0.001.

Table 5 :

 5 Descriptive statisticsNote: Activism score and Conservation score refer to[START_REF] Milfont | The environmental attitudes inventory: A valid and reliable measure to assess the structure of environmental attitudes[END_REF] scales from the Environmental Attitudes Inventory, which were used to calculate participants' proenvironmental attitudes. See detailed definition of each variable in subsections3.1.2 and 4.1.3.

	Statistic	N	Mean	St. Dev.	Min	Max
	Education score	855	4.1	0.9	1	5
	Total income (including benefits)	855	2,736	1,258	1,200	12,000
	Griskevicius items	855	12.7	3.3	3	21
	MacArthur Scale	855	5.2	1.5	1	9
	AUC (temporal discounting)	855	20,501	12,934	709	49,276
	Conservation score	855	5.2	1	1	7
	Activism score	855	4.2	1.2	1	7
	Pro-environmental behaviour	855	0.4	0.5	0	1
	Treatment	855	0.5	0.5	0	1
	Bias	855	2.2	1.2	7	1

Table 1

 1 Comparison with generalized social trust levels from European Values Study.

Table 2 .

 2 Outputs and Bayes Factors from models containing time and economic variables.

Table 3 .

 3 Main models controlling for political variables.

Table 4 .

 4 Main models controlling for smile.

Table 5 .

 5 Models outputs. Subsamples, clockwise: only male MPs, only black and white pictures, only pictures scanned by ourselves.

Table 6 .

 6 Model output. Effect of political affiliation.

Acknowledgments

Pendant une bonne

Acknowledgments

We thank Rita Abdel Sater for her help during the preparation of the pre-registration, especially for the power analysis of study 1. We would like to thank two anonymous reviewers for their insightful comments on our manuscript.

Acknowledgments

We would like to thank the archivists of the French National Assembly who opened us the doors of the archives for several dozen hours of photography. We also thank an intern: Faniriana Rakoto Endor for his help in rating the detection of the faces.

Acknowledgments

We thank three interns: Alexis Garsmeur, Saeyeon Kwon and Marius Mercier who helped us collect and process the portraits.

Acknowledgments

We thank an intern: Liza Sarde who helped us collect and process the portraits.

Funding

This research was supported by the Agence Nationale de la Recherche (EUR FrontCog ANR-17-EURE-0017*).

Funding

This research was supported by the Agence Nationale de la Recherche (EUR FrontCog ANR-17-EURE-0017*).

://osf.io/xucgw/

Data accessibility

All data and R scripts are available at https://osf.io/xucgw/.

much do you agree with each of the below statements on a scale from 1 (strongly disagree) to 7 (strongly agree)? My family and I have had enough money for things. I have lived in a relatively wealthy neighborhood. I have felt relatively wealthy compared to other people in my Temporal discounting. Prior research has demonstrated that hypothetical delay-discounting tasks reliably predict actual temporal preferences (Hardisty et al., 2013), and correlate with realworld measures of impulsivity such as smoking, overeating, and credit card debt [START_REF] Chabris | Individual laboratory-measured discount rates predict field behavior[END_REF][START_REF] Meier | Present-Biased Preferences and Credit Card Borrowing[END_REF]Reimers et al., 2009). Our temporal discounting task was based on the one designed by Frye et al. [START_REF] Frye | Measuring Delay Discounting in Humans Using an Adjusting Amount Task[END_REF]. In this task, participants had to complete three blocks of an intertemporal choice task with varying delays (three weeks, three months and two years) and varying amounts. All values displayed to participants were rounded to the nearest 50 pence (£0.50). Each block consisted of 6 binary choice trials. The task ended participants read carefully, resulting in a total of 20 trials. In the first block, participants had the choice between a smaller reward in three days, and a larger reward in three weeks. In the second and third blocks, the later delay was set to three months and two years respectively. The choices were hypothetical, which means that participants did not actually receive the money corresponding to their choices. As in Haushofer et al.'s paper (Haushofer et al., 2013), possible randomizing the order of trials across blocks. In order to control for possible unintended effects, the position of the sooner-smaller and later-larger alternatives on the screen (top vs. bottom) was also randomized across trials. The later reward was kept constant at £70 (as in (Reimers et al., 2009) adjustment was based on a bisection algorithm, following [START_REF] Frye | Measuring Delay Discounting in Humans Using an Adjusting Amount Task[END_REF]. As recommended by the authors, the adjustment for the upcoming trial was always equal to the maximum amount multiplied by

, where n was the trial number for the current adjustment.

Such a procedure allowed us to identify three individual indifference points for each participant, which can be used as the basis for the computation of discount rates. In our case, the indifference point for a particular delay is the amount of money to which a participant is indifferent between receiving it in three days and receiving £70 with the particular delay. Given that we only have 3 indifference points per participant, we were not able to fit curvilinear regression models (like Mazur's [START_REF] Mazur | An adjusting procedure for studying delayed reinforcement. The Effect of Delay and of Intervening Events on Reinforcement Value: Quantitative Analyses of Behavior[END_REF]), Green's [START_REF] Green | Temporal discounting in choice between delayed rewards: The role of age and income[END_REF] or Rachlin's [START_REF] Rachlin | Notes on discounting[END_REF]) and focused

Correlational Mediation model

To test our first main hypothesis (H1) we conducted regression analyses relying on the causalsteps procedure set forth by Baron and Kenny (Baron & Kenny, 1986) to detect a mediation effect. The results suggest a partial mediation: the effect size of both objective and subjective (see table 2) socioeconomic status is lowered by the addition of temporal discounting in the model. For both mediation pathways (Objective vs. Subjective socioeconomic status), the positive effect of socioeconomic status on social trust is partially mediated by temporal discounting (Objective socioeconomic status: Average Causal Mediating Effect of temporal discounting: b = 0.02, p = .001, Proportion of the total effect that is mediated: 9.0 %, p = .001;

Subjective socioeconomic status: Average Causal Mediating Effect of temporal discounting: b = 0.02, p = .01, Proportion of the total effect that is mediated: 6.2 %, p = .01). These values are calculated for control.value = -0.6 (first quartile) and treat.value = 0.5 (third quartile) for

Objective socioeconomic status and control.value = -0.7 (first quartile) and treat.value = 0.7 (third quartile) (see table 3.). The Proportion of the total effect that is mediated and all the pvalues do not vary if other control.value and treat.value are chosen, only b values, change.

Ethics

Évaluation

Éthique pour les recherches en sant -CERES no. 201659) and is in accordance with the Declaration of Helsinki (World Medical Association, 2008). Moreover, before beginning our online experiments, participants had to give their informed consent.

Competing interests

We declare we have no competing interests.

General Discussion

In this thesis, we sought to better understand the link between a psychological trait and a major environmental characteristic, namely social trust and resources availability. To do so, we have placed ourselves within the framework of evolutionary psychology (see e.g., [START_REF] Buss | The Handbook of Evolutionary Psychology[END_REF][START_REF] Giudice | Life History Theory and Evolutionary Psychology[END_REF]). This framework is based on the idea that humans, like all living organisms, are the product of a long evolutionary process. Evolution is based on two mechanisms, mutation and selection. Mutation is the phenomenon whereby errors occur in DNA replication, potentially leading to new viable alleles. Selection then leads to the invasion (/disappearance) of the mutant in the population, depending on the advantage (/disadvantage)

given by the phenotype resulting from the new allele. In this case, the notion of advantage can be of various types (survival, fertility, attractiveness, etc.) but must be linked to an increased capacity of transmission of the alleles through the generations. In other words, individuals in a population carrying an allele that gives them an advantage in terms of the transmission of their genetic heritage will, over the generations, invade the population. As a result, after several generations this allele becomes the majority. In reality, this process is much more complex and other mechanisms like recombination can interact. Moreover, the strength of selection pressures is not equal on every trait, therefore, some might be more subject to genetic drive and other more strongly selected.

Starting from this principle, it is possible to see the traits of a given species, e.g., humans, in a new light, by seeking to understand their ultimate causes and not just their proximal ones.

Evolutionary psychologists see the brain exactly like other organs (heart, lungs, liver, etc.) Chapter Six

104 which have a structure encoded in the genome that has been and is still under selection. More specifically, as the brain is involved in numerous functions, many different functional structures have been selected and now cope with adaptive problems (e.g., finding food, choosing a partner, taking care of children, etc.). As a result, we see the brain as a collection of modules that fulfill specific functions and therefore may undergo different selection pressures. Many questions can then be asked: Which traits result from selected modules? Which behaviors are adaptive, and which are maladaptive? For example, is disgust an adaptation? To what problem? disease risk?

These are questions that have been studied for long by evolutionary psychologists [START_REF] Tooby | Conceptual Foundations of Evolutionary Psychology[END_REF].

Then more complex questions can be asked. Is the same phenotype advantageous in all environments? Will the alleles that determine it be selected in the same way everywhere?

Knowing that gene expression can be modulated, are phenotypic changes observed when the environment changes, even over a period of time too short for natural selection to act? Which characteristics of an environment influence the expression of a given phenotype? By what mechanisms? All the questions can be addressed using the concept of phenotypic plasticity [START_REF] Pfennig | Phenotypic Plasticity & Evolution: Causes, Consequences, Controversies[END_REF][START_REF] Woltereck | Weitere experimentelle Unter-suchungen uber Artveranderung, speziell uber das Wesen quantitativer Artunterschiede bei Daphniden[END_REF].

Among the traits that are interesting, we decided to focus on social trust. There are also other types of trust that are more specific. For example, community trust is the trust in one's own community, that is the people one lives close to. In some cases, this type of trust can interact with generalized social trust, for instance in buffering the effect of low income [START_REF] Jachimowicz | Community trust reduces myopic decisions of low-income individuals[END_REF]. Another type of trust that can be of interest is the trust in institutions, like media, religious or political institutions [START_REF] Twenge | Declines in Trust in Others and Psychological Science[END_REF]. In the times of a pandemic, the extent to which the citizens trust their government is particularly important [START_REF] Devine | Trust and the Coronavirus Pandemic: What are the Consequences of and for Trust? An Early Review of the Literature[END_REF], for instance in the acceptance of restrictive measures (Bavel et al., 2020). Interestingly, institutional and social trust interact. At least in Sweden during the covid pandemic, it seems like institutional trust fed social trust [START_REF] Esaiasson | How the coronavirus crisis[END_REF]. In this PhD, we focus on the most general type of trust, generalized social trust.

Social trust has been linked to two specific hormones that have antagonist effects: oxytocin promoting trust and testosterone decreasing it [START_REF] Krueger | Toward a Model of Interpersonal Trust Drawn from Neuroscience, Psychology, and Economics[END_REF], but mixed results have been found. In line with its positive impact on prosocial behaviors in non-human animals [START_REF] Carter | Neuroendocrine perspectives on social attachment and love[END_REF][START_REF] Pedersen | Oxytocin Control of Maternal Behavior Regulation by Sex Steroids and Offspring Stimuli[END_REF], oxytocin has been seen as potentially playing a role in the biological control of social trust [START_REF] Kosfeld | Oxytocin increases trust in humans[END_REF]. However this body of research has

General appendix

Appendix to Chapter Two Socioeconomic status, time preferences and pro-environmentalism Grandin A., Guillou L., Abdel Sater R., Foucault M. and Chevallier C.

September 2021

Keywords

Socioeconomic status; Pro-environmental behaviour; Environmental attitudes; Temporal discounting; Perceived relative a✏uence.

Abstract

Future-oriented individuals tend to display more pro-environmental attitudes and behaviours, compared to those who are present-oriented. Investigating the determinants of time preferences could therefore shed light on factors that also influence environmentalism. A key factor that impacts time preferences is socioeconomic status (SES). Importantly, SES is also positively correlated with willingness to act for the environment. In this paper, we test whether time preferences partially mediate the relationship between SES and pro-environmentalism in three studies. In the first study, we tested the assumption that pro-environmental attitudes are positively correlated with SES on a large cross-sectional French sample (N = 15,924). We found expected results both with an objective and a subjective measure of SES. Then, we conducted an online study including a temporal discounting task, which allowed us to fully test the mediation hypothesis on British participants (N = 650). Our results suggest that the positive association between SES and pro-environmental attitudes is partially mediated by temporal discounting, but no significant mediated relationship was found for pro-environmental behaviour. Finally, we conducted a third study with an experimental setting, for which we recruited British participants who underestimated their position in the income distribution (N = 855). In the treatment group, participants received a correction of their misperception, in order to increase their perceived relative income. Although the expected shift towards increased preferences for the future was not observed, we found a moderated e↵ect of the treatment on pro-environmentalism.

Introduction

Environmental issues are emblematic cases of intertemporal problems: they involve resource dilemmas where a short-term cost is incurred for a benefit that only comes in the future [START_REF] Kortenkamp | Time, Uncertainty, and Individual Di↵erences in Decisions to Cooperate in Resource Dilemmas[END_REF]. Preserving forests or fresh water Baron and Kenny's (1986) procedure, aimed at testing the partially mediated e↵ect of SES on pro-environmental attitudes via temporal discounting (AUC). See detailed definitions for each variable in subsection 3.1.2.

In addition, we conducted unregistered analyses aimed at testing our mediation model with education and revenue (a variable combining personal income and benefits) as separate variables. The results can be found in Table 6 in the Supplementary Materials. There was both a direct and a mediated e↵ect of education on pro-environmental attitudes (direct: = 0.084, p = .020; mediated: = 0.021, p < .001). As for revenue, there was a smaller mediated e↵ect ( = 0.017, p = .028) and no significant direct e↵ect ( = 0.020, p = .724).

Replication of the correlational mediation model

As was pre-registered, additional mediation analyses were conducted to replicate the correlational mediation model found in the previous study. Following Baron and Kenny's (1986) approach, we replicated the overall patterns found in the previous study (we do not include the detail of these analyses here, but they are available in Tables 7 and8 in the Supplementary Materials). The only discrepancy with Study 2 was that subjective SES was not significantly associated with pro-environmental attitudes ( = 0.01, p = .80).

The analyses based on bootstrapping also yielded very similar results to those from Study 2: we observed again a positive mediated e↵ect of objective SES on pro-environmental attitudes via temporal discounting ( = 0.012, p = .01), as well as a direct e↵ect ( = 0.085, p = .03, see Table 10 in the Supplementary Materials). As in Study 2, we found no significant direct or mediated e↵ect of objective SES on pro-environmental behaviour. The bootstrapping procedure also indicated that the relationship between subjective SES and pro-environmental attitudes was significantly mediated by temporal discounting ( = 0.026, p = .01), while there was no direct e↵ect ( = 0.014, p = .77). One di↵erence with Study 2 was that this time we found a significant mediated association between subjective SES and pro-environmental behaviour ( = 0.010, p = .04).

Appendix to Chapter Three

Supplementary materials to chapter three

Pictures renaming

The next step was to match the names identified on the pictures with MPs' names in the MPs' dataset (using Matching_MPs.R). The first time, the matching was made between a pictures dataset (containing pictures names, perceived trustworthiness and dominance scores, etc) combining several legislatures and the original deputies dataset. The first merging was made with the variable MatchName (combination of first name and family name without spacing). This created the first dataset that was used later on for the other matchings. Then, several matches were made (for instance: with the first name, with the second name, with or without "de", exact and inexact matches allowing one letter of difference). Eventually, the several pictures that did not match are manually matched. Afterwards, newly obtained legislatures were matched with the dataset resulting from these matchings. The same sequence of matchings was applied: first with MatchName and then several different ones.

Robustness checks with subsets

First, we re-ran the analyses (removing gender from the controls), excluding women. The dataset is reduced to 11951 observations. Almost no quantitative change was observed in the results. In separate models as described above, facial perceived trustworthiness is significantly positively associated with time (unstandardized b = 0.015, p < 0.001), significantly positively associated with national GDP (b = 0.53, p < 0.001) and significantly negatively associated with mean political affiliation (b = -0.072, p < 0.001). Second, we re-ran the main analyses, removing color pictures (i.e., pictures since 1997). The dataset is reduced to 11951 observations. Almost no quantitative change was observed in the results. In separate models as described above, facial perceived trustworthiness is significantly positively associated with time (unstandardized b = 0.015, p < 0.001), significantly positively associated with national GDP (b = 0.50, p < 0.001) and significantly negatively associated with mean political affiliation (b = -0.071, p < 0.001). Last, we re-ran the main analyses, removing the pictures that were not scanned by ourselves (i.e. 1914, 1936, 2007, 2012, 2017). The dataset is reduced to 10166 observations. Almost no quantitative change was observed in the results. In separate models as described above, facial perceived trustworthiness is significantly positively associated with time (unstandardized b = 0.015, p < 0.001), significantly positively associated with national GDP (b = 0.52, p < 0.001) and significantly negatively associated with mean political affiliation (b = -0.084, p < 0.001).

Appendix to Chapter Four

Project working title: Trustworthiness of European Monarchs' faces across History. Authors: Léonard Guillou, Lou Safra, Nicolas Baumard. Affiliation: Institut Jean Nicod, École Normale Supérieure Paris, PSL Université.

Social trust is linked to a host of positive societal outcomes, including improved economic performance, lower crime rates and more inclusive institutions. Yet, the origins of trust remain elusive, partly because social trust is difficult to document in time. Building on recent advances in social cognition, we will use an algorithm designed to automatically generate trustworthiness evaluations from the facial action units (smile, eye brows, etc.) of monarchs' portraits. We will only use portraits painted during each monarch's lifetime. In order to be sure that each monarch had an influence on how he has been represented on these portraits. So this study will give us information on how the monarchs wanted to be represented. Like a "cognitive fossil" of a part of their mind.

A. Hypotheses

Description of essential elements Describe the (numbered) hypotheses in terms of directional relationships between your (manipulated or measured) variables.

Hypothesis: In line with historical work showing a rise of trust in European societies since the middle ages, it has already been observed that trustworthiness displays have risen over time in British and European portraits. Our main hypothesis is that the trustworthiness displays in monarchs' portraits followed the same variation. More specifically, according to ecological approaches to social and political preferences, trustworthiness displays in monarchs' portraits should correlate with economical variations. (i.e., in harsh periods, people prefer stronger leaders with less trustworthy faces than in non harsh periods, and therefore, leaders should prefer to be represented as less trustworthy in harsh periods.)

For interaction effects, describe the expected shape of the interactions. NA If you are manipulating a variable, make predictions for successful check variables or explain why no manipulation check is included. NA

Recommended elements A figure or table may be helpful to describe complex interactions; this facilitates correct specification of the ordering of all group means.

For original research, add rationales or theoretical frameworks for why a certain hypothesis is tested.

If multiple predictions can be made for the same IV-DV combination, describe what outcome would be predicted by which theory.

B. Methods

Description of essential elements Design List, based on your hypotheses from section A:

Independent variables with all their levels: GDP per capita, urbanization, book titles and democratization. Website: https://clio-infra.eu/ Dependent variables, or variables in a correlational design: Level of trustworthiness display computed based on the facial action units automatically extracted by Open Face 3.1 (https://github.com/TadasBaltrusaitis/OpenFace) and transformed into trustworthiness evaluation scores using a random forest algorithm optimized on the avatars controlled for trustworthiness generated by FaceGen (Oosterhof and Todorov, 2008).

Control variables.

Time, information about each portrait: age of the monarch at the date of the portrait, type of artwork (oil painting, engraving, etc.), face's level of dominance displays computed with a similar algorithm as for trustworthiness displays.

Planned sample If applicable, describe pre-selection rules.

We decided to study only European portraits to avoid cultural bias. Indeed, we used the avatars controlled for trustworthiness generated by Oosterhof and Todorov (2008) based on American participants' evaluations to optimize our trustworthiness evaluation algorithm. We will only use paintings that were painted during each monarch's lifespan. We exclude caricatures as they do not reflect the way a specific monarch wants to be presented. As GDP is our critical variable of interest due to its direct link with economic wealth but also to its available for extended periods of time, a "country" is selected when there are GDP data available (https://clio-infra.eu/) for an area corresponding to the borders of this country (http://geacron.com/home-en/) and there is a monarch ruling it.

Indicate where, from whom and how the data will be collected. Monarchs' lists are attached. Way of collecting these lists, for each country: -finding Wikipedia pages (like « list of French monarchs ») -looking at the Wikidata pages of these monarchs to find a way of picking them all (like « position held » : « King of France and Navarre ») -making a Wikidata query (https://query.wikidata.org/) to pick these monarchs' names, dates of birth and death, beginning and end of their reign -exporting it as a csv file To make our list as complete as possible, this method was applied in different languages: English, German, French, Spanish, Italian and the country's local language. Then, these csv files in different languages were compiled in a unique list and the list was checked, and if necessary completed, using the Wikipedia list page (some monarchs are not well referenced and many of them do not have beginning and ending reign dates in Wikidata).

Socio-economic variables come from: https://clio-infra.eu/ and [START_REF] Alfani | Growing apart in early modern Europe? A comparison of inequality trends in Italy and the Low Countries, 1500-1800[END_REF].

Justify planned sample size (if applicable, you can upload a file related to your power analysis here (e.g., a protocol of power analyses from G*Power, a script, a screen shot, etc.). We will use the maximum of European monarchs and portraits of them available within the set of countries decided.

Describe data collection termination rule.

When as much as possible monarchs' portraits will have been collected within the set of countries decided.

Exclusion criteria Describe anticipated specific data exclusion criteria.

The software OpenFace 3.1 (https://github.com/TadasBaltrusaitis/OpenFace) is not able to treat every kind of portrait. Some portraits will be excluded as the software does not detect any face in it (because of basic features such as face orientation or insufficient contrast between the background and the face). Some portraits will be excluded by two evaluators after a double check to find all the faces badly recognized (with points at wrong places). Each of the two evaluators who will check will independently classify the face as accurately detected or not: 0: badly detected ; 1: moderately well detected ; 2: well detected. The two scores will then be added to attribute a unique detection score to each portrait. These detection scores will be used as weights for the statistical analyses.

Procedure (Recommended element, in the online form see next page) Set fail-safe levels of exclusion at

which the whole study needs to be stopped, altered, and restarted. If applicable, you can upload any files related to your methods and procedure here (e.g., a paper describing a scale you are using, experimenter instructions, etc.). In order to test if our method can be successfully applied to monarchs' portraits, we will first assess if some well documented results on trustworthiness and dominance displays are replicated (Oosterhof & Todorov, 2008): -gender effect: males appear less trustworthy and more dominant than women -age effect: older adults appear less trustworthy and more dominant than younger ones.

-positive correlation between the level of trustworthiness and the level of dominance. If we don't replicate these effects we will stop the study. Describe all manipulations, measures, materials and procedures including the order of presentation and the method of randomization and blinding (e.g., single or double blind), as in a published Methods section. Besides everything described above, the two face-detection evaluators will code the type of artwork (1: oil painting, 0: other kind of technique). Moreover, we sometimes have several different digitization processes for a single portrait, we will keep all of them when faces are well detected. These duplicates will be identified during the double-check step. We will run our models with and without these duplicates and we will give the percentage of repeated portraits. Portraits are not always precisely dated. Therefore, some date recoding has been done. Here I present the most common texts recoded. Wikimedia has many different contributors. Therefore, there are numerous different ways of writing the dates (some are very specific and not written here). In the analyses, we will use the rounded mean of DATE1 and DATE2. We keep these 2 dates to know which portrait is precisely dated and which one is not and to have a confidence interval.

C. Analysis plan

Confirmatory analyses

Describe the analyses that will test each main prediction from the hypotheses section. For each one, include: the relevant variables and how they are calculated; Same variables as in section B.

the statistical technique;

We will design linear mixed models with monarchs' ID as a random effect to control for the fact that there are several portraits of each monarch.

To investigate the dynamics of these historical changes we could run time-lag analyses on trustworthiness displays and GDP per capita (and urbanization and number of book titles, etc.). (the online form asks the above for the first, second, third, fourth and further predictions separately)

Recommended elements

All statistical analyses will be carried out in R 3.4.4 (https://www.r-project.org/).

Specify contingencies and assumptions, such as: Method of correction for multiple tests. NA

The method of missing data handling (e.g., pairwise or listwise deletion, imputation, interpolation). We will use listwise deletion when data about the painting are missing.

Reliability criteria for item inclusion in scale.

Anticipated data transformations.

Assumptions of analyses, and plans for alternative/corrected analyses if each assumption is violated.

Optionally, upload any files here that are related to your analyses (e.g., syntaxes, scripts, etc.).

Answer the following final questions:

Has data collection begun for this project? Yes, data collection (portraits downloading) is complete.

If data collection has begun, have you looked at the data?

We have looked at the monarchs' lists and paintings found to clean datasets but we did not do any statistics even descriptive statistics.

The (estimated) start and end dates for this project are (optional):

Start date: 1st of October 2018 End date:

Any additional comments before I pre-register this project (optional): NA

Add to the pre-registration

We failed to find the pre-registered gender effect (women are supposed to be more trustworthy than men) in our dataset, which is one of our quality checks (p-value = 0.5848, see the model at the end of this page). This reflects a high level of noise in our dataset, probably due to many bad digitizations on Wikimedia (pictures taken with a camera for instance).

To rule out the possibility that this effect is actually due to a important gender imbalance in our dataset (761 portraits of men for 92 portraits of women), Lou Safra has done 1000 simulations on an other sample of portraits from the high quality scans of the Web Gallery of Art she used for an other study. The simulations of sub samples of 761 portraits of men and 92 portraits of women (same proportion of our sample of 853 portraits) all showed a significant effect of gender (all p-s < .005). These results further suggests that the absence of a gender effect in our dataset is due to the poor quality of the pictures.

In order to improve the quality of our dataset, we decided to clean our sample by removing the portraits that have a non-official source (we did not do any analysis so far). List of official sources is attached.

Other possible actions we have already done:

recode the dates of the portraits that are badly dated (range >= 100 years). By looking at the information on the source website (hypothesized to be more accurate than the one provided on Wikimedia)

control in our models for the type of artwork, coded as: 1: oil painting, 0: other type of artwork.

(this code was already mentioned in the first pre-registration)

remove the portraits which have a mean date of creation later than the death date of the monarch control in our models for the source

Other possible action we have not done yet but will do soon:

remove the portraits that are in double: we will keep the primary source (in general, a museum website)

After all this cleaning work we will see if we find the gender effect (which is a quality check) with all the countries and also in samples by country. As our model is trained on adult faces only, we will also conduct separate analyses on the portraits for which the monarch's age is above 15 years old.

If we still do not find our quality checks effects after these cleanings we will stop the study and submit an online open-access short report on OSF presenting our null results.

Model to check for the gender effect:

Formula: Trustworthiness ~ gender + (

Case study

As we said in the document « Add to the pre-registration », we used several filters in order to improve the quality of our dataset. We could not find a gender effect (one of our quality check) even in our cleaned sample. One hypothesis is that it is due to the quality of the images extracted from Wikimedia. To test this hypothesis we compared 15 portraits which had the National Portrait Gallery (NPG) as source, with their original version on the NPG (a dataset on which we were able to find a gender effect in a previous study).

Here are the results for this comparison: mean and standard error to the mean of the absolute difference between Wikimedia and NPG images for several indicators. We can conclude that there are clear quantitative differences between the images from NPG and the one from Wikimedia, on these different indicators.

We also did a quick analysis to test how these differences can explain the differences in Trustworthiness and Dominance measures: Linear model explaining the absolute difference between trustworthiness displays in the Wikimedia and in the NPG databases:

Estimate Std. Error t value Pr(>|t|) (Intercept) 0.22107 0.28721 0.77 0.456 Absolute difference in the contrast of the images from Wikimedia and from the NPG database 0.05776 0.02085 2.77 0.017 *

The difference in Trustworthiness level of a portrait from the NPG and the same one from Wikimedia is partially explained by the difference in contrast between these two images.

Conclusion:

It appears that difference in quality can explain why we do not find a gender effect in our dataset.

That is why we decided to stop our analysis on this dataset. Cela explique en partie comment les ressources économiques peuvent influencer le niveau de confiance sociale abilité de la confiance au cours de l'histoire. Malgré de terribles inégalités encore persistantes, les sociétés humaines se sont fortement enrichies avec le temps, et ce, tout particulièrement depuis le début de la révolution industrielle. Serait-il alors dans les niveaux de confiance sociale au cours du temps ? Pour répondre à cette question, je me suis appuyé sur ce qui nous est parvenu des personnes ayant vécu dans le passé et qui est un produit de leur psychologie : les items culturels. Ces items culturels peuvent être considérés comme des "fossiles cognitifs", une sorte faciales, à quel point les photographies ou portraits peints représentent des visages qui inspirent confiance ou perçue dégagée par les visages augmentait au cours du temps et que cette augmentation était bien expliquée par les variations économiques. À la lumière de ces analyses, il y a de bonnes raisons de penser que les changements de mentalités liés à la confiance qui ont eu lieu par le passé et vont probablement advenir dans le futur sont, au moins en partie, déterminés par des changements environnementaux, au premier rang desquels le niveau de ressources des individus.

ABSTRACT

Social or interpersonal trust is the belief that people are on average trustworthy, that they cooperate in good faith, without trying to cheat and exploit others. Among other things, it allows unrelated individuals, who may not have aligned interests, to cooperate. It therefore has many benefits at both the individual and societal levels. However, despite its undeniable advantages, social trust is highly variable not only between countries but also between individuals. How can this variability be explained? In this thesis, I explore the idea that some of the variability in interpersonal trust can be explained by differences in resource levels. From an evolutionary perspective, social trust does indeed come with several risks and costs. One of these risks is that of mistaking a "cheater" for a trustworthy partner. In this situation, the cheater may exploit you without benefit to you. If your resources are low, the cost of this cheating is more salient than if they are abundant. It is therefore not optimal to have a very high level of social trust when you have low resources. In my thesis, I first investigated how the level of resources might impact social trust. I tested the hypothesis that it is temporal preferences that mediate the relationship between resource level and social trust (Chapter 2). In other words, part of the positive effect of the level of resources on social trust is due to the fact that higher resources lead to a more long-term orientation. Social trust, because of its direct link to reciprocal cooperation, is itself a long-term oriented trait. This partly explains how economic resources can influence the level of social trust of individuals. I then turn to the variability of trust over history. Despite terrible persisting inequalities, human societies have become much richer over time, especially since the beginning of the industrial revolution.

Would it then be possible to observe variations in the levels of social trust over time? To answer this question, I have relied on what has come down to us from people who lived in the past and which is a product of their psychology: cultural items. These cultural items can be considered as "cognitive fossils", a kind of partial imprint of their psychology at that time. More precisely, I was interested in facial representations, to what extent photographs or painted portraits represent faces that inspire confidence or not (Chapters 3, 4 and 5). I showed that for most of the datasets collected, the level of perceived trust in faces increased over time and that this increase was well explained by economic variations. Considering these analyses, there is good reason to believe that the changes in trust-related attitudes that have occurred in the past and are likely to occur in the future are, at least in part, determined by environmental changes, foremost among which is the level of resources of individuals.

KEYWORDS

Social trust, economic resources, behavioral plasticity, temporal preferences, culture.