Cyril Forestier

Philippe Sarrazin

Benoit Allenet

Aurélie Gauchet

Jean-Philippe Heuzé

Aïna Chalabaev

Benoît Allenet

"ARE YOU IN FULL POSSESSION OF YOUR CAPACITY?" 1 "Are you in full possession of your capacity?". A mechanistic self-control approach at trait and state levels to predict different health behaviors

Keywords: Trait self-control capacity, state self-control capacity, self-control mechanisms, healthy behaviors, unhealthy behaviors

come     "Are you in full possession of your capacity?". A mechanistic self-control approach at trait and state levels to predict different health behaviors

Introduction

Despite accumulating evidence that behaviors play a central role in health, most people have difficulties to adopt a healthy lifestyle (e.g., [START_REF] Ford | Low-risk lifestyle behaviors and all-cause mortality: Findings from the National Health and Nutrition Examination Survey III mortality study[END_REF]. Understanding the determinants of behavior change has thus become a hot topic in psychology, notably within socio-cognitive models (e.g., theory of planned behavior, [START_REF] Ajzen | The theory of planned behavior[END_REF]. Although these models have been successful in identifying key determinants of behavioral intention (e.g., self-efficacy, attitudes), intentions do not systematically translate into behavior change, a phenomenon known as the "intention-behavior gap". Self-control, which reflects the ability of the self to exert control over the self [START_REF] Muraven | Self-regulation and depletion of limited resources: Does self-control resemble a muscle?[END_REF], represents a promising concept to address this limitation (e.g., Sniehotta, Presseau, & Araujo-Soares, 2014). Indeed, individual differences in self-control have consistently been shown to predict health behaviors. For example, people with high self-control trait report less use of tobacco and less eating disorders than people with low self-control trait (for a review see [START_REF] De Ridder | Taking stock of self-control[END_REF]. However, the mechanisms through which self-control affects behaviors remain unclear.

Traditionally, self-control is conceived as the ability to inhibit desires or habits (e.g., [START_REF] Hagger | Ego depletion and the strength model of self-control: A meta-analysis[END_REF]. The more people are able to resist desires that conflict with their long-term goals, the more likely self-control success is to occur (e.g., [START_REF] Kotabe | On integrating the components of self-control[END_REF]. Yet, recent research shows instead that high trait self-control is associated with reduced desire for problematic behaviors, reduced conflict between desires and long-term goals, and less resistance to conflict (e.g., [START_REF] Hofmann | Dieting and the self-control of eating in everyday environments: An experience sampling study[END_REF][START_REF] Hofmann | Everyday temptations: An experience sampling study of desire, conflict, and self-control[END_REF][START_REF] Luehring-Jones | The effect of self-control on drinking outcomes is mediated by automatic appetitive responses to alcohol[END_REF]. This is in line with a broader conceptualization of self-control as a general process by which people prioritize distal goals over proximal motives [START_REF] Fujita | On conceptualizing self-control as more than the effortful inhibition of impulses[END_REF]. Here, effortful inhibition is one mechanism by which people control their behaviors, but more "proactive" strategies exist, such as avoiding temptations or developing healthy habits.

The goal of this study was to extend this line of research by investigating self-control at the dispositional and state levels. We examined if trait and state self-control independently predict health behaviors, as well as the mechanisms of these relationships (desire, conflict, and resistance). This questioning lies on evidence that although individual differences in self-control exist, this ability may also vary on a daily-life basis (e.g., [START_REF] Buyukcan-Tetik | Within-person variations and between-person differences in self-control and wellbeing[END_REF] and Hofmann et al., (2012). This suggests that people's available resources at a particular point in time may differ from the resources they have most of the time. If this is the case, are self-regulation behaviors affected by both trait and state self-control, and to what extent? On the one hand, we can consider based on the strength model of self-control (e.g., [START_REF] Muraven | Self-regulation and depletion of limited resources: Does self-control resemble a muscle?[END_REF] that behaviors are primarily determined by current self-control resources. On the other hand, a recent study on well-being showed that both trait and state selfcontrol contributed to this outcome, and that trait self-control was more predictive than state selfcontrol [START_REF] Buyukcan-Tetik | Within-person variations and between-person differences in self-control and wellbeing[END_REF]. This suggests that these two aspects of self-control should both be considered.

We examined the role of trait and state self-control on different types of health behaviors (unhealthy and healthy). Most research assumes that self-control effects on unhealthy behavior (e.g., eating junk food) and healthy behaviors (e.g., eating healthy) rely on the same processes, but empirical evidence of this assumption is lacking [START_REF] De Ridder | Taking stock of self-control[END_REF]. Yet, such evidence is necessary because these two types of behaviors differ in an important way. Indeed, unhealthy behaviors may be attractive in the short term and need therefore to be inhibited; healthy behaviors may instead be unattractive in the short term and need therefore to be actively initiated (e.g., de Boer, Van Hooft, & Bakker, 2011). The literature on approach and avoidance motivation suggests the relevance of distinguishing these two types of behaviors (e.g., achievement goals theory, [START_REF] Elliot | A conceptual history of the achievement goal construct[END_REF]; regulatory focus theory, [START_REF] Higgins | Promotion and prevention: Regulatory focus as a motivational principle[END_REF]. For example, the regulatory focus theory considers that people may pursue different types of goals: they are promotion-oriented when they pursue desirable outcomes (e.g., gains), and prevention-oriented when they strive to avoid undesirable outcomes (e.g., losses). Importantly, these goals lead to different emotions, cognitions and behaviors (Higgins, 2018). This suggests that self-control mechanisms could differ according to the nature of the behavior.

In sum, the current study investigated the mechanisms through which both trait and state self-control predict unhealthy and healthy behaviors, from a broad self-theoretical perspective.

This questioning was examined in the domains of diet (eating balanced food and avoiding eating unbalanced food), physical activity (being physically active and avoiding being sedentary), and smoking (avoiding smoking). We hypothesized that trait and state self-control would predict behaviors independently from one another (e.g., Buyukcan-Telik et al., 2018). We also hypothesized that the self-control -unhealthy behaviors relationships would be mediated by decreased desire for these behaviors, reduced conflict between desires and long-term goals, and less resistance to conflict (e.g., Hofmann, Baumeister, et al., 2012;[START_REF] Luehring-Jones | The effect of self-control on drinking outcomes is mediated by automatic appetitive responses to alcohol[END_REF].

Material and methods

Participants and procedure

College students from five French universities were contacted through mailing lists to participate in an online study examining the relationships between their personal beliefs and health behaviors. They were further informed that their participation was anonymous and that their responses would be kept confidential. Sample size was determined before any data analysis, following [START_REF] Boomsma | Nonconvergence, improper solutions, and starting values in Lisrel maximum likelihood estimation[END_REF] recommendation to include a minimum of 200 participants when conducting structural equation modeling (we did not continue to collect data after data analysis).

After having confirmed their consent to participate in this study, a sample of 422 voluntary individuals completed the online questionnaire (hosted on SurveyMonkey). The study was conducted in accordance with APA ethical principles in the conduct of research with human participants.

Seventy-nine participants were not considered in further analyses because they completed only the first page of the questionnaire (less than 15% of the items). In addition, data beyond three standard deviations from the mean of the variables of interest were removed, resulting in exclusion of eighteen more participants. The final sample was composed of 325 participants (188 women; M age = 20.90, SD age = 4.62). All measures, manipulations, and exclusions in the studies are disclosed.

Measures

Trait self-control capacity.

The Brief Self-Control Scale (BSCS) (Tangney, Baumeister, & Boone, 2004) is a largely used scale to assess trait self-control. The BSCS is composed of 13 items (e.g., "I am good at resisting temptations", "I have a hard time breaking bad habits"). Participants responded on a seven-item Likert scale ranging from 1 (Completely disagree) to 7 (Completely agree). The scale presented good reliability in the present study (α = .77).

State self-control capacity.

State self-control was indexed by subjective vitality. This operationalization was chosen because subjective vitality corresponds closely to the definition of self-control capacity as one's perception of the mental resources or energy available to the self [START_REF] Clarkson | Perceived mental fatigue and selfcontrol[END_REF]. Indeed, subjective vitality is defined as the energy available to the self [START_REF] Ryan | From ego depletion to vitality: Theory and findings concerning the facilitation of energy available to the self[END_REF]. This construct may not be reduced to energy because other energy-related constructs such as anger, anxiety, or arousal, are unrelated or negatively related to it [START_REF] Ryan | On energy, personality, and health: Subjective vitality as a dynamic reflection of well-being[END_REF]. Instead, subjective vitality represents energy that one can harness or regulate for purposive actions [START_REF] Ryan | From ego depletion to vitality: Theory and findings concerning the facilitation of energy available to the self[END_REF], and is therefore similar to the definition of selfcontrol capacity.

In support to this idea, past research within the ego depletion literature has shown that exerting self-control can reduce subjective vitality (e.g., [START_REF] Hagger | Ego depletion and the strength model of self-control: A meta-analysis[END_REF][START_REF] Muraven | Helpful self-control: Autonomy support, vitality, and depletion[END_REF][START_REF] Rouse | Effects of motivation and depletion on the ability to resist the temptation to avoid physical activity[END_REF]. For instance, [START_REF] Muraven | Helpful self-control: Autonomy support, vitality, and depletion[END_REF] observed that decreased self-control performance after a depleting task was mediated by decreased subjective vitality. This suggests that subjective vitality and behavioral assessments of ego depletion tap into the same resources, with subjective vitality having the advantage of being a highly accessible, phenomenologically based variable [START_REF] Ryan | From ego depletion to vitality: Theory and findings concerning the facilitation of energy available to the self[END_REF].

More particularly, participants completed the Subjective Vitality Scale [START_REF] Ryan | On energy, personality, and health: Subjective vitality as a dynamic reflection of well-being[END_REF]. This scale began with the stem: "Indicate to what extent each of the following sentence reflects the general feelings you had during the past two days". It was composed of five items (e.g. "I felt alive and vital") and responses ranged on a seven-item Likert scale from 1 "ARE YOU IN FULL POSSESSION OF YOUR CAPACITY?" 8 (Completely disagree) to 7 (Completely agree). The scale presented good reliability (α = .70).

Past research showed that this measure may fluctuate over time (e.g., Emile, d'Arripe-Longueville, Cheval, Amato, & Chalabaev, 2015;[START_REF] Smolders | Daytime light exposure and feelings of vitality: Results of a field study during regular weekdays[END_REF]. For example, [START_REF] Smolders | Daytime light exposure and feelings of vitality: Results of a field study during regular weekdays[END_REF] observed that more than 65% of the variance in feelings of vitality occurred within individuals, at the level of time of day. This suggests that this measure may be appropriate to capture state of self-control capacity.

Self-control mechanisms.

Desire, desire-intention conflict, and resistance were assessed with the items used in (Hofmann et al., 2012). Items were preceded by the stem: "Among the following behaviors, what are those you have desired doing and to what extent, during the past two days?". The stem was followed by five items focusing on the three unhealthy behaviors of interest (i.e., sedentary activity, unhealthy diet, tobacco consumption). More particularly, desire frequency and strength1 were measured by the items: "With which frequency have you experienced a desire for sedentary activities?", and "How strong was the desire for sedentary activities?" (words in italic differed depending on the target behavior). Participants responded on a 8-item scale ranging from 0 (No desire at all) to 7 (Irresistible desire). Desire-intention conflict was measured by the item: "To what extent did this desire conflict with your intention to avoid sedentary activities?".

Participants responded on a 8-item scale ranging from 0 (No conflict at all) to 7 (Very high conflict). Finally, resistance (i.e., self-control effort) was assessed with the item: "To what extent have you attempted to resist the desire for sedentary activities?". Participants responded on a 8item scale ranging from 0 (No resistance at all) to 7 (Very high resistance).

Health behaviors.

Physical and sedentary activities were measured with the French version of the International Physical Activity Questionnaire [START_REF] Craig | International physical activity questionnaire: 12-country reliability and validity[END_REF], which assesses moderate and vigorous physical activity. For each of these intensities, participants were asked to indicate the number of days during which they did at least 10 min sessions of physical activity, and how many minutes, during the last week. We then summed the scores on the moderate and vigorous physical activity items, to obtain the Moderate to Vigorous Physical Activity score (MVPA score). The IPAQ also assesses walking time in minutes during the last week, and sedentary time, defined as the time in minutes spent in a sitting position during the last week.

Healthy and unhealthy diet were measured with the Healthy Eating Behaviour Scale [START_REF] Pelletier | Why do you regulate what you eat? Relationships between forms of regulation, eating behaviors, sustained dietary behavior change, and psychological adjustment[END_REF]. This 8-item scale is composed of two subscales assessing diet behavior during the last week. Four items measure healthy diet: "I eat vegetables, fruits and grain products", "I eat a variety of foods which balance the four alimentary groups (fruits and vegetables, dairy products, starches, meat/fish/eggs)", "I eat foods that are low in fat, saturated fat, and cholesterol", and "I drink water". The other four items measure unhealthy diet: "I eat food such as chips, chocolate, and candies", "I eat fried food", "I use white sugar", and "I use salt". Participants indicated their consumption frequency on a seven-item scale ranging from 1 (Once or twice per month) to 7 (More than three times per day). In contrast to [START_REF] Pelletier | Why do you regulate what you eat? Relationships between forms of regulation, eating behaviors, sustained dietary behavior change, and psychological adjustment[END_REF], who averaged the items of each subscale, we decided instead to sum the responses of each subscale to obtain the Healthy Diet Score (ranging from 4 to 28) and the Unhealthy Diet Score (ranging from 4 to 28). Indeed, following [START_REF] Steca | Changes in dietary behavior among coronary and hypertensive patients: A longitudinal investigation Using the Health Action Process Approach[END_REF]'s reasoning, "ARE YOU IN FULL POSSESSION OF YOUR CAPACITY?" 10 we considered that eating one particular type of unhealthy food (e.g., white sugar) may be unrelated to the consumption of other types of unhealthy food (e.g., fried food). It seems therefore conceptually irrelevant to aggregate items that may be independent from each other. A sum may instead better quantify the consumption of unhealthy or healthy food. Finally, tobacco consumption was measured with a single-item "How many cigarettes do you smoke per week?".

Results

Preliminary analyses

All data is available upon request to the first author. We checked that variables were normally distributed by examining their skewness and kurtosis. Distribution of all variables were normal, except MVPA score, sedentary time, and number of cigarettes smoked. A square root transformation was therefore applied to these variables. Means, standard deviations, and Cronbach's alpha coefficients are presented in the supplementary materials, as well as Pearson correlations between the variables of interest. Results showed that MVPA was not significantly related to sedentary time, r(324) = -.03, p = .62, and unhealthy diet was not significantly correlated with healthy diet, r(324) = .01, p = .89. This indicates that healthy and unhealthy behaviors within the same domain are independent from one another.

Primary analyses

Analytic strategy.

We ran path analyses to test our predictions using AMOS 23.0 with maximum likelihood estimation. The same path analytic models were tested for each behavior domain (physical activity, diet, and smoking). The baseline model included all variables (i.e., trait self-control capacity, desire, conflict, and resistance) except state self-control, which was added in the final model. This two-step method allowed us to examine whether state self-control added explained variance in health behaviors. Chi-square differences tests of the baseline versus final models were also conducted to evaluate if these increases in explained variance were significant.

The models' fit was assessed by examining the minimum discrepancy (CMIN/DF), the probability level (p-value), the Bentler-Bonett normed fit index (NFI), the comparative fit index (CFI), the Tucker-Lewis-Index (TLI), and the root-mean-square error of approximation (RMSEA). A satisfactory model fit is indicated by a CMIN/DF ratio below 2.00 (Byrne, 2012), a p-value over .05 (Arbuckle & Wothke, 2001), a NFI over .95 [START_REF] Schumacker | A Beginner's guide to structural equation modeling[END_REF], a TLI over .90 [START_REF] Tabachnick | Using multivariate statistics[END_REF], a CFI over .93 [START_REF] Byrne | Structural equation modeling with EQS and EQS/Windows: Basic concepts, applications, and programming[END_REF], and a RMSEA below .05 (Stieger et al., 1990). All non-significant paths were deleted according to the methods described by [START_REF] Maccallum | Specification searches in covariance structure modeling[END_REF].

In addition, the cross-sectional nature of the design prevents from drawing causal inferences. We therefore tested for each behavior a reverse model in which behavior was the predictor and self-control capacity the outcome, in order to rule out the alternative hypothesis that adopting health behaviors predicts self-control capacity. Indeed, given that health behaviors adoption is related to well-being [START_REF] Cheng | Personality traits and health behaviors as predictors of subjective wellbeing among a multiethnic sample of university-attending emerging young adults[END_REF], and that subjective vitality is a component of well-being [START_REF] Ryan | On energy, personality, and health: Subjective vitality as a dynamic reflection of well-being[END_REF], one could predict the reverse relationships to the ones we predicted. Indices of these reverse models were not adequate, ruling out this alternative hypothesis (CMIN/DF = 4.00, p < .001, NFI = .72, TLI = .41, CFI = .75, RMSEA = .10, for the physical and sedentary activity model; CMIN/DF = 3.24, p = .00, NFI = .87, TLI = .78 , CFI = .90, RMSEA = .08, for the healthy and unhealthy diet model, and CMIN/DF = 8.47, p = .00, NFI = .87, TLI = .73, CFI = .88, RMSEA = .15 for the smoking model).

Path analyses.

Self-control components predicting physical and sedentary activity.

Results of the baseline and final models are presented in Figure 2. Both models provided good fit to the data (CMIN/DF = 1.49, p = .16, NFI = .90, TLI = .89, CFI = .96, RMSEA = .04, for the baseline model; CMIN/DF = 1.07, p = .38, NFI = .93, TLI = .99, CFI = .99, RMSEA = .02, for the final model). Desire positively predicted conflict, which positively predicted resistance, which in turn negatively predicted sedentary activity. In other words, the more participants reported a strong desire for sedentary activity, the more they experienced conflict between this desire and their intention to be physically active, and the more they resisted this desire; in turn, the more they resisted it, the less they spent time being sedentary. A direct positive relationship between desire and sedentary activity was also observed. Results concerning self-control capacity showed that both trait and state self-control negatively predicted desire for sedentary activity. Additional results showed that state self-control directly and positively predicted physical activity, and that trait self-control negatively predicted conflict.

Finally, inclusion of state self-control added explained variance in physical activity, which increased from R 2 = .03 to R 2 = .07. However, the chi-square difference test revealed that the final model did not significantly differ from the baseline model (Δχ² = .91, Δdf = 4, p > .05).

[Insert Figure 2 here]

Self-control components predicting healthy and unhealthy diet.

Results of the baseline and final models are presented in Figure 3. Both models provided good fit to the data (CMIN/DF = 1.05, p = .39, NFI = .96, TLI = .99, CFI = .96, RMSEA = .01, for the baseline model; CMIN/DF = 1.58, p = .08, NFI = .93, TLI = .94, CFI = .97, RMSEA = .04, for the final model). Again, desire positively predicted conflict, which positively predicted resistance. In other words, the more participants reported a strong desire for unhealthy food, the more they experienced conflict between this desire and their intention to eat healthy, and the more they resisted this desire. However, resistance did not significantly predict unhealthy food consumption. A direct positive relationship between desire and unhealthy food consumption was nevertheless observed. Results concerning self-control capacity showed that both trait and state self-control directly and positively predicted consumption of healthy food, while trait self-control also negatively predicted consumption of unhealthy food. Moreover, inclusion of state selfcontrol added explained variance in healthy food consumption, which increased from R 2 = .08 to R 2 = .13. Finally, the chi-square difference test showed that the final model significantly differed from the baseline model (Δχ² = 11.05, Δdf = 4, p < .05).

[Insert Figure 3 here]

Self-control components predicting smoking.

Results of the baseline and final models are presented in Figure 4. The final model provided better fit to the data (CMIN/DF = 3.66, p = .00, NFI = .97, TLI = .93, CFI = .98, RMSEA = .09, for the baseline model; CMIN/DF = 2.78, p = .01, NFI = .96, TLI = .98, CFI = .98, RMSEA = .07, for the final model). Similar to physical activity behaviors, desire positively predicted conflict, which positively predicted resistance, which in turn negatively predicted cigarette consumption. In other words, the more participants reported a strong desire for smoking, the more they experienced conflict between this desire and their intention not to smoke, and the more they resisted this desire; in turn, the more they resisted it, the less they smoked. A direct positive relationship between desire and smoking was also observed. Results concerning self-control capacity showed that neither trait nor state self-control directly predicted smoking. However, trait self-control negatively predicted desire while state self-control negatively predicted conflict. Finally, inclusion of state self-control did not explain more variance in cigarette consumption. This observation was confirmed by the chi-square difference test, which indicated that the final model did not significantly differ from the baseline model (Δχ² = 3.78, Δdf = 3, p > .05).

[Insert Figure 4 here]

Discussion

Role of trait self-control capacity

When considering self-control at the trait level only, results were in line with recent research [START_REF] Hofmann | Dieting and the self-control of eating in everyday environments: An experience sampling study[END_REF]Hofmann, Baumeister, et al., 2012;[START_REF] Luehring-Jones | The effect of self-control on drinking outcomes is mediated by automatic appetitive responses to alcohol[END_REF]: the more individuals had high trait self-control, the less they experienced desires for unhealthy behaviors (sedentary activities, unhealthy food, and smoking). In turn, less desire was generally associated with less conflict, less resistance, and less behavior enactment. Interestingly, results showed that trait self-control affected both unhealthy and healthy behaviors, whereas these behaviors differ in an important way: the former need to be inhibited and the latter need to be initiated. Taken together, these findings are in line with a broad conceptualization of self-control, in which inhibition represents only one manner to control behavior among other manners (e.g., avoiding temptations, developing healthy habits) (e.g., [START_REF] De Ridder | Not doing bad things is not equivalent to doing the right thing: Distinguishing between inhibitory and initiatory self-control[END_REF][START_REF] Fujita | On conceptualizing self-control as more than the effortful inhibition of impulses[END_REF][START_REF] Galla | More than resisting temptation: Beneficial habits mediate the relationship between self-control and positive life outcomes[END_REF]. This interpretation was reinforced by the result that trait self-control still predicted behaviors when controlling for state self-control (except physical activity). This indicates that people's current available resources are not sufficient to explain self-control success or failure, and that their general tendency to prioritize distal goals over proximal motives uniquely predicts behaviors. This result corroborates the idea that high trait self-controllers use self-control strategies that enable them to be successful in different situations and extended periods of time, and notably effortless strategies (e.g., avoiding temptations) (e.g., Gillebaart & de Ridder, 2015).

Role of state self-control capacity

Concerning state self-control, its role was more manifest with regard to healthy behaviors. Indeed, including state self-control to the baseline model doubled the explained variance in physical activity and healthy diet. This suggests that such behaviors may more particularly necessitate available self-control resources or energy available to the self, in order to be actively initiated. However, this increase in explained variance was significant for diet but not for physical activity, which could be due to the low level of explained variance for the physical activity model. This suggests that the role of state self-control capacity differ according to the behavior domain. In addition, these findings corroborate the idea that self-control mechanisms may differ for healthy and unhealthy behaviors. This is in line with the literature on approach and avoidance motivation, which shows that pursuing something desirable and striving to avoid something undesirable are associated with different psychological and behavioral outcomes (e.g., [START_REF] Higgins | Promotion and prevention: Regulatory focus as a motivational principle[END_REF].

In sum, this study shows the relevance of considering self-control capacity both at the trait and state levels. While research on these two facets has developed separately, our results showed that they both predicted behavior independently from each other. Taking into account these two aspects of self-control may therefore be useful to better understand the role of selfcontrol in health behaviors (e.g., [START_REF] Buyukcan-Tetik | Within-person variations and between-person differences in self-control and wellbeing[END_REF]. Overall, our findings raise the question of what processes drive the direct relationships between self-control capacity and healthy behaviors.

Self-control mechanisms are behavior-dependent

Finally, some differences were observed in self-control processes depending on behaviors. While desire directly predicted all unhealthy behaviors, this relationship was much stronger for smoking than for sedentary activity and unhealthy diet. This may be explained by the fact that smoking is addictive, and therefore particularly susceptible to desire strength. In addition, resistance was found to significantly predict sedentary activity and smoking, but not unhealthy diet. These findings suggest that the causes of self-control failures may differ according to the behavior domain, with desire playing a major role especially in smoking. This is in line with the idea that theories that are applicable to one particular behavior may not be applicable to another one, and that identifying the underlying processes of health behaviors requires to accurately define the target behavior [START_REF] Michie | Making psychological theory useful for implementing evidence based practice: A consensus approach[END_REF]. This reasoning is further reinforced by the differences observed in the variance in behaviors explained by the final models (69% in cigarette consumption, 13% in healthy diet, 8% in unhealthy diet, 7% in physical activity, and 3% in sedentary activity). Finally, results for the physical activity model showed a direct relationship between trait self-control and conflict after controlling for desire. This suggests that high self-controllers use other strategies than temptation avoidance, for example, they may be better able to draw their attention away from the problematic desire (e.g., when they are tempted by a sedentary behavior, they may focus on another activity, reducing in turn the experienced conflict).

Limits and future directions

This study is not without limitations, notably due to the cross-sectional nature of the design, which presents limits that are specific to the study of psychological states fluctuating over time. Indeed, even if participants were asked to rate their subjective vitality within a short time frame (two days), there was only one measurement occasion and fluctuations were not assessed. Although this study represents an initial step in the identification of the distinctive role of trait and state of self-control capacity, future studies should adopt a longitudinal methodology such as experience sampling method (e.g., Hofmann et al., 2012), or rely on a latent state-trait approach (Steyer et al., 1999), which is particularly relevant to assess and distinguish precisely states variations from trait of a psychological construct.

Another promising avenue of research would be to articulate research on self-control with socio-cognitive models of behavior change, such as the Health Action Process Approach [START_REF] Schwarzer | Modeling health behavior change: How to predict and modify the adoption and maintenance of health behaviors[END_REF]. This model identifies action planning (defining when, where, and how to act) and coping planning (defining strategies to prioritize long-term goals over habitual responses when obstacles are faced) as key self-regulatory mechanisms to translate health goals into action.

These strategies appear as likely candidates to explain the self-control capacity -behavior relationship, and future research should examine this possibility.

Conclusion

Results highlight that self-control mechanisms differ according to the health behavior considered, and suggest the importance of distinguishing trait from state self-control capacity in these mechanisms. Future research may help to better understand these processes by articulating them with more traditional socio-cognitive models of behavior change, and by adopting a longitudinal design such as the experience sampling method. Tangney, J., Baumeister, R., & Boone, A. (2004). High self-control predicts good adjustment, less pathology, better grades, and interpersonal success. Journal of Personality, 72 (2), 271-324. http://dx.doi.org/10.1111/j.0022-3506.2004.00263.x

Figure 1

 1 Figure 1 summarizes our hypotheses.

Figure 1 .Figure 2 .

 12 Figure 1. Hypothetical model of self-control correlates of unhealthy and healthy behaviors

  Figure 1.

Figure

  Figure(s)

  Figure 3.

  Figure 4.

Table 1 .

 1 Physical and sedentary activity models. All coefficients are unstandardized. Significant coefficients are bolded. * p < .05, ** p < .01, *** p < .001. Numbers into brackets represent 95% confidence intervals.

	Indirect effects

Given that results were similar for desire strength and desire frequency, we present here only results for desire strength, followingHofmann, Baumeister, et al. (2012).

Acknowledgments 1

Authors wish to express their gratitude to University Grenoble Alpes for their funding. 2

Acknowledgements

This research was supported by a 'Alpes Grenoble Innovation Recherche' funding from Univ. Grenoble Alpes (grant number G7H-AG16BC86).


A Tianyi Zhang

B Olivier Le Meur

HOW OLD DO YOU LOOK? INFERRING YOUR AGE FROM YOUR GAZE

Keywords: age inference, scanpath, deep network

The visual exploration of a scene, represented by a visual scanpath, depends on a number of factors. Among them, the age of the observer plays a significant role. For instance, young kids are making shorter saccades and longer fixations than adults. In the light of these observations, we propose a new method for inferring the age of the observer from its scanpath. The proposed method is based on a 1D CNN network which is trained by real eye tracking data collected on five age groups. In order to boost the performance, the training dataset is augmented by predicting a high number of scanpaths thanks to the use of an age-dependent computational saccadic model. The proposed method brings a new momentum in this field not only by significantly outperforming existing method but also by being robust to noise and data erasure.

INTRODUCTION

Since we cannot process all information of our visual field, our brain uses a mechanism of selection aiming to prioritize visual chunks of the input stimulus. This mechanism, called selective visual attention, allows us to focus our visual processing resources on relevant visual information. The covert attention is a particular form of visual attention, that allows us to pay attention toward a specific location of our visual field without moving the eyes [START_REF] Michael | Orienting of attention[END_REF]. In contrast to covert attention, overt attention relies on eye movements to move our focus of attention (i.e. overt orienting). Overt attention, through eye movements, is an exogenous manifestation of the underlying cognitive processes that may or may not occur.

Eye movements are mainly composed by fixations and saccades. Fixations aim to bring objects of interest onto the fovea, where the visual acuity is maximum. Saccades are ballistic changes in eye position, allowing to jump from one position to another. Visual information extraction essentially takes place during the fixation period. The sequence of fixations and saccades is called a visual scanpath [START_REF] Privitera | The scanpath theory: its definition and later developments[END_REF]. When looking at natural scenes, saccades of small amplitudes are far more numerous than long saccades [START_REF] Le | Saccadic model of eye movements for free-viewing condition[END_REF]. In addition, horizontal saccades (leftwards or rightwards) are more frequent than vertical ones, which are much more frequent than oblique ones. These viewing biases, indicating how our gaze moves within a scene, are not systematic. They depend on a number of factors such as the scene content [START_REF] Le | Introducing contextdependent and spatially-variant viewing biases in saccadic models[END_REF][START_REF] Bannier | Deepcomics: Saliency estimation for comics[END_REF], our age [START_REF] Le Meur | Visual attention saccadic models learn to emulate gaze patterns from childhood to adulthood[END_REF], our gender [START_REF] Coutrot | Face exploration dynamics differentiate men and women[END_REF], and whether we suffer from a visual disease or not [START_REF] Itti | New eye-tracking techniques may revolutionize mental health screening[END_REF][START_REF] Jiang | Learning visual attention to identify people with autism spectrum disorder[END_REF]. Regarding the age influence of gaze deployment, recent studies give evidence that there exists age-related differences in viewing patterns while free-viewing visual scene on screen [START_REF] Helo | The maturation of eye movement behavior: Scene viewing characteristics in children and adults[END_REF][START_REF] Heather | Anticipatory eye movements while watching continuous action across shots in video sequences: A developmental study[END_REF]. For instance, fixation durations decrease and saccade amplitudes increase with age. These behavioral differences are due to several factors, such as the eye metamorphosis, the cognition, etc.

In this study, we designed a new method for inferring the age of an observer by only considering his visual scanpath (i.e. without considering the visual content). Five age groups are considered: 2 y.o., 4-6 y.o., 6-8 y.o., 8-10 y.o. and adults [START_REF] Helo | The maturation of eye movement behavior: Scene viewing characteristics in children and adults[END_REF]. As far as we know, there exist very few methods for inferring the age of an observer from his scanpaths. Recently, Le Meur et al. [START_REF] Le Meur | Your gaze betrays your age[END_REF] presented evidence that simple scanpaths-based features, such as the fixation duration and saccade length can be used to infer their age. They proposed to learn a direct non-linear mapping between simple features extracted from the ordered sequence of fixations and saccades and observer's age. They used the multi-class Gentle AdaBoost algorithm [START_REF] Freund | A desicion-theoretic generalization of on-line learning and an application to boosting[END_REF] to perform the classification. Good performances were reported for a binary classification involving 2 classes, i.e. 2 y.-o. versus adult groups.

The contributions of the proposed study are twofold. First, rather than extracting handcrafted gaze-based features, a 1D convolution neural network is trained to predict observer's age. Second contribution concerns the training dataset. As the amount of data for the classification is relatively small, we use a generative model for producing visual scanpaths for the different age groups. Although that these scanpaths are estimated and do not reflect exactly the actual gaze deployment of observers, it turns out that the classification performance dramatically increases, outperforming existing method in a significant manner.

This paper is organized as follows. Section 2 elaborates on the proposed scanpath-based age inference approach. Section 3 presents the performance of the proposed method. Section 4 concludes the paper.

DEEP INFERENCE OF OBSERVER' AGE

In this section, we present the proposed approach. Similarly to [START_REF] Le Meur | Your gaze betrays your age[END_REF], the method aims to infer the age of an observer from his visual scanpath, and regardless of the visual content. Five age groups are defined, i.e. 2 y.o., 4-6 y.o., 6-8 y.o., 8-10 y.o. and adults.

Proposed model

Figure 1 presents the architecture of the proposed method, called AgeNet. AgeNet is based on a one-dimension convolutional neural network (1D CNN). It aims to learn deep features of scanpath for inferring observers' age. 1D CNN has been widely used in the field of speech recognition and sound classification [START_REF] Van Den Oord | Wavenet: A generative model for raw audio[END_REF][START_REF] Aytar | Soundnet: Learning sound representations from unlabeled video[END_REF]. Like audio signals, a scanpath is an ordered time-stamped sequence. The temporal order of scanpath is fundamental. Just after the stimulus onset, the gaze deployment is mainly stimulus-driven when topdown influences are the weakest. These influences tend to increase with the viewing time, and is likely dependent on observers' cognition [START_REF] Parkhurst | Modeling the role of salience in the allocation of overt visual attention[END_REF][START_REF] Tatler | Visual correlates of fixation selection: effects of scale and time[END_REF][START_REF] Le Meur | A coherent computational approach to model the bottom-up visual attention[END_REF]. However, unlike audio signal which often contains thousands of features in one sequence, we are considering in this study a rather short scanpath, that would be composed of less than twenty fixation points. Considering the temporal order and the length of scanpath, AgeNet uses 4 causal convolutional layers, as proposed in [START_REF] Van Den Oord | Wavenet: A generative model for raw audio[END_REF]. They aim to extract deep features from the input scanpath. The causal convolutional layers make sure the model cannot violate the temporal order of scanpath. That is to say, the prediction generated by the model at time t cannot be affected by any of the subsequent gaze data. Table 1 summarizes the configuration of the proposed network. The first convolutional layer applies causal convolutions with the kernel size of 7 × 2 (7 represents the number of data taken into account by the convolution whereas 2 indicates that we are using the x and y coordinates of the visual fixation). Compared with Wavenet [START_REF] Van Den Oord | Wavenet: A generative model for raw audio[END_REF] and Soundnet [START_REF] Aytar | Soundnet: Learning sound representations from unlabeled video[END_REF], the convolutional kernel in the first layer of our network is designed to be small. Early convolutional layers aim to extract local information of scanpath while the deeper layers merge local information to get a more general description of the scanpaths. For the second, third and fourth convolutional layers, we apply causal convolutions with the kernel size of 5 × 4, 3 × 5 and 3 × 16, respectively. Again, the sizes of kernel are rather small for taking into account the rather small scanpath length. In addition, the use of small kernels allows us to get wider range of local clues. We did not deeper our network further since the performance did not get better by adding more layers. The max pooling layer between the first and the second convolution is set to decrease the dimension of feature maps for further convolution. After 4 layers of convolution, a GlobalMax pooling layer is added to generate a 32-dimensional feature vector. At last, the feature vector is fed to two fully-connected layers with 128 neurons each followed by an output layer composed by 5 neurons. The dropout rate for the first fully-connected layer is set to be 0.2 to avoid over-fitting.

Training

For training the proposed model, we used the eye tracking dataset of [START_REF] Helo | The maturation of eye movement behavior: Scene viewing characteristics in children and adults[END_REF]. 101 subjects, including 23 adults and 78 children, participated in the experiments. These subjects were divided into 5 groups: 2 year-old group (18 participants, M = 2.16 (M = M ean), SD = 0.22 (SD = StandardDeviation)), 4-6 year-old group (22 participants, M = 4.2, SD = 0.42), 6-8 year-old (18 participants, M = 6.6, SD = 0.47), 8-10 year-old group (20 participants, M = 8.55, SD = 0.67) and adult group (22 participants, M = 28, SD = 4.48). Participants were instructed to explore 30 color pictures taken from children's books for 10 seconds. More details can be found in [START_REF] Helo | The maturation of eye movement behavior: Scene viewing characteristics in children and adults[END_REF].

Unfortunately, this dataset is too small to perform a reliable training. In order to increase in a significant manner the performance of the CNN, we proceed with data augmentation. In a number of studies, the training dataset can be increased by using a combination of affine transformations. However, these transformations may not be suitable to the proposed study. Indeed, we have to take care of the ordered sequences of fixation, as well as the discrepancy between gaze deployment of the different age groups.

Our data augmentation strategy consists in artificially generating fake scanpaths for each age group. For this purpose, we use the age-dependent saccadic model recently proposed by [START_REF] Le Meur | Visual attention saccadic models learn to emulate gaze patterns from childhood to adulthood[END_REF]. Saccadic models aim to predict not only where observers look at but also how they move their eyes to explore the scene. Le Meur et al. [START_REF] Le Meur | Visual attention saccadic models learn to emulate gaze patterns from childhood to adulthood[END_REF] demonstrated that it is possible to tailor the saccadic model in order to capture differences in gaze behaviour between age groups. Generated scanpaths share the same statistics (e.g. distribution of saccade amplitudes, distribution of saccade orientations) as the actual ones. Thanks to this generative model, we compute, for each age group, 20.000 scanpaths, each composed of 150 fixations. Per age group, we then generate 3 million fixations.

The CNN was trained using RMSProp with a batch size of 1000 scanpaths for pre-training, and 256 for fine-tuning. During the training phase, we monitor the accuracy and the loss on the validation set to avoid over-fitting problem. The learning rate was set to α = 0.001 over the training course. Figure 2 shows learning curve of pre-training and fine-tuning for fake and actual scanpaths, respectively. The training with fake scanpaths has a slow convergence speed. However, we do not need to get very high performance for the pre-training process: the pre-trained model gets an accuracy of 62%. Unlike fake scanpaths, the length of real scanpaths may vary significantly from one observer to the other. To overcome this problem, we pad the real scanpaths with zeros in order to get (150 × 2) values as the fake scanpaths. From Figure 2, we can see the fine-tuning process converged rapidly after only a few epochs and did not cause over-fitting problems at all. We conclude that the proposed network can effectively train the scanpaths and has a good performance of generalization.

PERFORMANCE

In this section, we evaluate our network from different perspectives. Firstly, we use 10-fold cross validation approach to estimate the accuracy and loss of our model. Secondly, we compare different strategies of training using different methods of data augmentation. Then, we evaluate the robustness of the proposed method against noise and loss. At last, we compare our method with [START_REF] Le Meur | Your gaze betrays your age[END_REF].

Cross validation

To evaluate the performance of our model, we cross-checked the network with 10-fold cross validation approach. The original data set was randomly partitioned into 10 equal sized subsets for training and testing. A single subset was used for testing while the remaining 9 subsets were used for training. The training and testing processes are repeated 10 times (i.e f 1,. . . ,f 10) with each of the 10 subsets used exactly once as testing set. The purpose of this validation is to evaluate the generalization error of our model. Figure 3 presents the result of the 10-fold cross validation. Our model (pre-trained with fake scanpaths and fine-tuned with actual scanpaths) achieves 98.27% and 0.095 for average accuracy and loss respectively, similar to the accuracy (98.40%) and loss (0.071) obtained when we randomly choose 90% of the data for training and 10% for testing (see the right-hand side of Figure 4). This result demonstrates that the accuracy and loss of our model do not rely on the selection of training data, which shows a very good generalization performance of the network.

Strategies of training

In this section, we compared different strategies of training using different methods of data augmentation.

Original data. We trained the network through the original data. Since the length of real scanpaths may vary significantly from one observer to the other, while the duration of them is roughly strict to 10 seconds, we sampled each scanpath every 10 milliseconds to unify their length to be (1000 × 2). As shown by Figure 4 (Left), the accuracy achieves 62.6%. We observed that over-fitting problem occurs after about 100 epochs. As a result of this, the accuracy could not been increased by further training.

Simple data augmentation. To overcome the aforementioned problem, we flipped and mirrored the sampled data to increase training set. Suppose a given scanpath sp = {(x k , y k )} k={1,...,1000} , where (x k , y k ) represents the spatial coordinates of the k th visual fixations in the current image, we calculate the flipped scanpaths sp f as well as the mirrored scanpaths spm by flipping and mirroring the spatial coordinates of visual fixations, while keeping the same temporal order. The procedure is described below:

sp f = {(x k , H -y k )} k={1,...,1000} spm = {(W -x k , y k )} k={1,...,1000} (1)
where [W, H] = [1024, 768] is the width and the height of the tested images. By flipping and mirroring the scanpaths, the training set is 3-times larger than original one.

Thanks to this augmented dataset, we trained and fine-tuned our network by the augmented and original data, respectively. The accuracy was promoted to 68.6% (see Figure 4 (center)). Missclassification occurs for the groups of 6-8 y.o. and 8-10 y.o.

Pre-trained the network with fake scanpaths. As explained previously, we used an age-dependent saccadic model to generate fake scanpaths for each age group. The pre-training of the network with this new dataset and its fine-tuning by actual eye tracking data significantly increases the performance of the proposed model: 98.4% of the predictions are correct and only 1.6% are wrong classification. These promising results suggest that our network can extract abundant deep features from fake scanpaths and greatly improved the accuracy of classification by fine-tuning the network with real scanpaths.

Performance with noise and loss

We evaluated the robustness of the proposed method to corrupted data, that may arise from failure or malfunctioning of the eye tracker device. For this purpose, we add artificial noise to the spatial coordinates of the visual fixations and we remove some visual fixations When we simulate the noisy data, f % fixation points in p% scanpaths were chosen and replaced by random values ranging from (0,0) to (1024,768). Similarly, the loss data is generated by randomly dropout f % fixation points in p% scanpaths. In our experiment, the percentage of noisy fixations and loss fixations varies between 10%, . . . , 30%, and 10%, . . . , 50%, respectively. As shown in Figure 5, when the amount of corruption is the most important, i.e. 30% of fixation points are corrupted for 50% of the scanpaths, our network still has an accuracy up to 60%. We observe that the accuracy decreases significantly when the percentage of fixation points in one scanpath are noised or missed. This is due to the corruption that breaks the temporal order of the visual fixations.

Overall, the results are promising. Indeed, in spite of noise and losses, our method performs quite well, suggesting that it could be used for predicting the observer's age when low-cost eye tracker tracks the observer's gaze.

Comparison with [12]

In this section, we compared the proposed method with [START_REF] Le Meur | Your gaze betrays your age[END_REF], which predicts the age of an observer by using handcrafted features (i.e. 70 scanpaths-based features) and Random Forest. Table 2 indicates that the results of our deep learning method dramatically outperforms Le Meur et al.'s method [START_REF] Le Meur | Your gaze betrays your age[END_REF], whatever the configuration. The highest improvement is observed for 4-class classification. The better performance can be explained at least by two reasons. Firstly, the deep features extracted by the network are more robust, more relevant and more numerous than the handcrafted features proposed in [START_REF] Le Meur | Your gaze betrays your age[END_REF]. Secondly, the generated fake scanpaths provide abundant data for training, leading to an efficient training.

CONCLUSION

The proposed deep network predicts the age of an observer by scrutinizing his visual scanpath. Compared to previous method, this method increases dramatically the performance creating a new momentum in the field. Beyond its ability to predict successfully age, the method is robust to noise and losses. Although a comprehensive analysis is required to confirm these properties, they may play an important role when eye tracking data are collected in an unconstrained experimental environment and/or with low-cost eye tracker.

In future works, we want to push forward the limits of the proposed method by considering new age categories. This kind of finegrained classification is extremely challenging because of the large intra-category variations and small inter-category variations.

Fig. 1 .

 1 Fig. 1. Architecture of the proposed 1D CNN

Fig. 2 .

 2 Fig. 2. Learning curves (accuracy and loss) for the proposed network trained with fake scanpaths (top) and by fine-tuning it with actual scanpaths (bottom).

Fig. 3 .

 3 Fig. 3. Accuracy (top) and loss (bottom) of the proposed method for 10-fold cross validation.

Fig. 4 .

 4 Fig. 4. Confusion matrix for the classification. (a) Trained with original data after sampling. (b) Pre-trained by data with flipping and mirroring, and fine-tuned with original data after sampling. (c) Pre-trained by fake scanpaths and fine-tuned by original data after zero padding.

Fig. 5 .

 5 Fig. 5. Performance of the performance when input scanpaths are corrupted with noise and loss.

2

  binary classifications between children (< 10 y.o.) and adult groups.

3

  four-class classification for 2, 4-6, 6-10 y.o. and adult groups. 4 five-class classification for 2, 4-6, 6-8, 8-10 y.o. and adult groups.

Table 1 .

 1 Architecture of the proposed AgeNet network

	Layer	Type	#C Kernel Drop	Output
	input	/	1	/	/	(150, 2)
	conv1	Causal	4	(7, 2)	/	(150, 4)
	pool1	Max	/	(3, 2)	/	(50, 4)
	conv2	Causal	5	(5, 4)	/	(50, 5)
	conv3	Causal	16	(3, 5)	/	(50, 16)
	conv4	Causal	32	(3, 16)	/	(50, 32)
	pool2 GlobalMax	/	/	/	32
	fc1	/	/	/	0.2	128
	fc2	/	/	/	/	128
	output	Softmax	/	/	/	5

Table 2 .

 2 Accuracy of the proposed method (%) compared to[START_REF] Le Meur | Your gaze betrays your age[END_REF].

		Le Meur et al. [12] Proposed
	2-class-1 1	91.3	99.2
	2-class-2 2	75.6	99.0
	4-class 3	52.2	98.1
	5-class 4	-	98.4

1 binary classifications between 2 y.o. and adult groups.


DeepComics: Saliency estimation for comics

Kévin Bannier, Eakta Jain, Olivier Le Meur Abstract-A key requirement for training deep learning saliency models is large training eye tracking datasets. Despite the fact that the accessibility of eye tracking technology has greatly increased, collecting eye tracking data on a large scale for very specific content types is cumbersome, such as comic images, which are different from natural images such as photographs because text and pictorial content is integrated. In this paper, we show that a deep network trained on visual categories where the gaze deployment is similar to comics outperforms existing models and models trained with visual categories for which the gaze deployment is dramatically different from comics. Further, we find that it is better to use a computationally generated dataset on visual category close to comics one than real eye tracking data of a visual category that has different gaze deployment. These findings hold implications for the transference of deep networks to different domains.

I. Introduction

Comics are a compelling medium for visual storytelling that are experiencing a resurgence as a result of the digital revolution [START_REF] Fenty | Webcomics: The influence and continuation of the comix revolution[END_REF]. New forms of comic art, such as mobile comics and web comics, reach a wider audience and creates new consumption methods. Powered by digital tools and open publishing platforms, comics as a form of expression has become accessible to individual content creators beyond established publishing houses.

The common characteristic across all the diverse applications of comics [START_REF] Green | Graphic medicine: use of comics in medical education and patient care[END_REF], [START_REF] Farthing | graphic medicineas a mental health information resource: Insights from comics producers[END_REF], [START_REF] Tatalovic | Science comics as tools for science education and communication: a brief, exploratory study[END_REF], [START_REF] Kruger | shoothis book makes me to think! education, entertainment, and life-skills comics in south africa[END_REF], [START_REF] Schwarz | Graphic novels for multiple literacies[END_REF], [START_REF] Gonick | The Cartoon History of the Universe: Volumes 1-7: From the Big Bang to Alexander the Great[END_REF], [START_REF] Cohn | The Visual Language of Comics: Introduction to the Structure and Cognition of Sequential Images[END_REF], [START_REF] Farthing | Illustrating cognition-a review of the visual language of comics[END_REF], [START_REF] Farthing | Data from graphic medicineas a mental health information resource: Insights from comics producers[END_REF] is that with a smart mixture of text or/and other visual information, artists communicate the content by drawing the viewer attention to specific areas.

Estimating where an observer looks within a comic is useful to drive a variety of application such as cropping [START_REF] Santella | Gaze-based interaction for semi-automatic photo cropping[END_REF], recomposing, or augmenting traditional comics with digital effects such as animations, stereoscopy, moveson-stills [START_REF] Thirunarayanan | Creating segments and effects on comics by clustering gaze data[END_REF], [START_REF] Jain | Predicting moves-on-stills for comic art using viewer gaze data[END_REF]. For these applications, the prerequisite is to get prior knowledge of the visual route used by the viewer, or intended by the artist. Rather than performing time-consuming and cumbersome eye tracking experiments, we propose a deep neural network to address this problem.

The technical challenge here is that there are very few eye tracking dataset on comics; to the best of our knowledge, the dataset proposed in [START_REF] Thirunarayanan | Creating segments and effects on comics by clustering gaze data[END_REF] is the sole publicly available dataset at this time. However, this dataset is too small to perform an end to end training for a deep network. To overcome this problem, we propose to train the deep network on publicly available eye tracking datasets that are similar either in terms of visual content or in terms of gaze behavior. With this transfer learning [START_REF] Pan | A survey on transfer learning[END_REF], we show that the learned features are relevant enough to predict saliency on comics. Our contributions can be summarized as follows: (i) we propose the first saliency model dedicated towards predicting the saliency maps on comics; (ii) we show that the proposed model trained on visual categories that have similar gaze deployment as comics performs much better than traditional saliency models and than models trained on natural scenes; (iii) we evaluate the benefit to use fake dataset, i.e. data that has been computationally generated, for training the proposed model.

This paper is organized as follows. Section II investigates the gaze deployment on comics. Section III presents the deep network used for predicting the salience on comics. Section IV presents the performance of the proposed model and compare it to state-of-the-art methods. We conclude this paper in the last section.

II. Gaze deployment on comics

We investigate the gaze deployment of observers while watching comics using [START_REF] Thirunarayanan | Creating segments and effects on comics by clustering gaze data[END_REF]'s dataset, which is composed of 23 images taken from public domain legacy comics. Five viewers participated in the experiment. More details are available in [START_REF] Thirunarayanan | Creating segments and effects on comics by clustering gaze data[END_REF].

The joint probability distribution p(d, φ), estimated as described in [START_REF] Meur | Saccadic model of eye movements for free-viewing condition[END_REF] represents the probability that a fixation featured by a distance d and an angle φ occurs:

p(d, φ) = 1 n n i=1 K h (d -d i , φ -φ i ) (1)
where, d i and φ i represents the distance (expressed in visual angle) and the angle (expressed in degree) between each pair of successive fixations respectively. n is the total number of samples and K h is a two-dimensional Gaussian kernel.

The distribution p(d, φ) obtained on the comics dataset is illustrated on Figure 1 (Top-left). The polar plot indicates the probability of saccade targeting when the current fixation point is located at the center of the plot. Radial position indicates saccadic amplitudes expressed in degree of visual angle. Three main observations can be done: first, the rightward saccades are much more numerous than other saccades (see arrow numbered 1). The amplitudes of those saccades are rather small, between 1.5 and 2 degrees of visual angle. This indicates that observers move their eye with small jumps from the left-hand side to the right-hand side of the comics images. Second observation concerns the re-positioning saccades (see arrow numbered [START_REF] Borji | Cat2000: A large scale fixation dataset for boosting saliency research[END_REF], and webpages from [START_REF] Shen | Webpage saliency[END_REF]. Bottom: Representative stimuli images of the visual categories.

2). These leftward saccades are used to re-position eyes on the left-hand side of the stimulus. The third observation (see arrow numbered 3) concerns the downward saccades, indicating that observers look the images in a top-down manner.

We evaluate the degree of similarity between the comic distribution and four other distributions, computing on Art, Cartoon, Sketch and Webpages images; some representative stimuli images of these visual categories are illustrated on Figure 1 (Bottom row). Distributions for Art, Cartoon and Sketch are computed by using the eye tracking dataset CAT2000 [START_REF] Borji | Cat2000: A large scale fixation dataset for boosting saliency research[END_REF] whereas the eye tracking dataset of [START_REF] Shen | Webpage saliency[END_REF] is used to estimate the distribution of saccade amplitudes and orientations on webpages. Figure 1 plots these joint distributions of saccade amplitudes and orientations. Table I provides the similarity values between the comics distribution and Art, Cartoon, Sketch and Webpages distributions. The similarity is assessed by the Kullback-Leibler divergence, the linear correlation and intersection of histograms [START_REF] Bylinskii | Mit saliency benchmark[END_REF]. As shown by Table I, the comics joint distribution turns out to be similar to the gaze deployment observed on webpages and on sketches. Webpages and Comics share several properties. As mentioned earlier, the gaze deployment on comics is close to those observed on webpages. Observers explore the webpages starting from the upper left corner and progressively move the gaze in a top-down manner. Regarding the sketch category, the similarity with comics gaze deployment is primarily related to the amplitude and orientations of saccades. As for comics, saccade amplitudes are rather small and mostly along the horizontal direction. The lowest similarity is observed for the category Art.

The similarity in content is also evaluated by computing the KL-divergence between the average amplitude Fourier spectrums of Comics and the aforementioned categories. Table I indicates that Cartoon is the most similar category whereas Sketch is the most dissimilar. Interestingly, the similarity in content (based on the average Fourier spectrum) is correlated negatively to the similarity in gaze deployment.

Our motivation is to evaluate the benefit to train a deep convolutional network with eye tracking data collected on visual categories similar either in terms of gaze behavior or similar in terms of visual content.

III. Predicting saliency A. Deep saliency network

For predicting the salience of comics, the architecture of network, illustrated in Figure 2, is based on the deep gaze network of [START_REF] Kümmerer | Deepgaze ii: Reading fixations from deep features trained on object recognition[END_REF] and on the multi-level deep network of [START_REF] Cornia | Multilevel net: A visual saliency prediction model[END_REF]. The pre-trained VGG-16 network [START_REF] Simonyan | Very deep convolutional networks for large-scale image recognition[END_REF] is used for extracting deep features of an input image (800×450) from layers conv3 pool, conv4 pool, conv5 conv3. Feature maps of layers conv4 pool and conv5 conv3 are rescaled to get feature maps with a similar spatial resolution.

Similarly to [START_REF] Cornia | Multilevel net: A visual saliency prediction model[END_REF], a smaller network, composed of two convolutional layers, is built. The first layer has a kernel size 3 × 3 and 64 feature maps. The second is a simple convolution layer, with a kernel 1 × 1, used to reduce the data dimensionality. Both these two layers have a Relu activation. The last layer outputs a reduced predicted saliency map which is upsampled to recover the initial resolution H × W .

B. Training and implementation

Three strategies are used to train the deep network. (i) Training with dataset having a similar gaze deployment: we use the webpages dataset of [START_REF] Shen | Webpage saliency[END_REF] which is composed of 149 screen-shots of webpages. They were instructed to free-view the webpages. We also use the 100 images from the Sketch visual category from the CAT2000 eye tracking dataset [START_REF] Borji | Cat2000: A large scale fixation dataset for boosting saliency research[END_REF]. Observers were instructed to look around the image.

(ii) Training with dataset that has a similar content: we use the Art and Cartoon categories of the CAT2000 dataset. Each category is composed of 100 images.

(iii) Generating fake training data for webpages content: we generate computationally a new dataset of saliency maps for webpages in order to increase the training corpus. This fake dataset could is used to (pre)train the proposed method. We collected 316 new screenshots of webpages. The predicted saliency maps are computed by adapting iSEEL model [START_REF] Tavakoli | Exploiting inter-image similarity and ensemble of extreme learners for fixation prediction using deep features[END_REF]. The modification just consists in adding a webprior computed from [START_REF] Shen | Webpage saliency[END_REF]. Once the proposed deep network is (pre)trained, it could be fine-tuned with the eye tracking dataset [START_REF] Shen | Webpage saliency[END_REF].

Concerning the implementation, input images are first resized to a resolution of 800 × 450 pixels. The loss function is

L(w) = 1 N N i=1 g(y i ) p i -y i 2 with g(x) = 1 + β1 1+exp(-β2(x-γ))
. N represents the number of images per batch (N = 20), p i and y i represent the vectorized i th predicted saliency map and ground truth map in the current batch, respectively. We assume that both predicted and ground truth maps are normalized in the range [0, 1]. The function g() is chosen in order to give more importance to errors occurring on high ground truth salience. This is a positive-definite increasing function. By default, we use β 1 = β 2 = 9 and γ = 0.6.

The network was trained using stochastic gradient descent. To prevent over-fitting, a dropout layer is added before the 3x3 convolutional layer. The rate of dropout is set to 0.5. During training, the network was validated against the validation set after every iterations to monitor convergence and over-fitting. The learning rate is set to 0.001. To test the model, we added a convolution with Gaussian filter after the last layer, to smooth the generated saliency map. This map is first resized to 800 × 450 and is then filtered by the proposed Gaussian filter.

IV. Performance

To carry out the evaluation, we use seven quality metrics used in the MIT benchmark [START_REF] Bylinskii | Mit saliency benchmark[END_REF], [START_REF] Meur | Methods for comparing scanpaths and saliency maps: strengths and weaknesses[END_REF]

: CC (correlation coefficient, CC ∈ [-1, 1]), SIM (similarity, intersection between histograms of saliency, SIM ∈ [0, 1]), AUC (Area Under Curve, AU C ∈ [0, 1]), NSS (Normalized Scanpath Saliency, N SS ∈ ]-∞, +∞[), EMD (Earth Mover Distance, EM D ∈ [0, +∞[) and KL (Kullback Leibler divergence, KL ∈ [0, +∞[).
A. Performance of existing models Table II presents the results. We put to the test 13 models: 6 unsupervised models (dark pink in Table II) and 7 models based on deep learning approach (light pink in Table II). Note that we also tested the saliency model proposed in [START_REF] Shan | Two-stage transfer learning of end-to-end convolutional neural networks for webpage saliency prediction[END_REF] for predicting saliency on webpages. This model is built over [START_REF] Pan | Shallow and deep convolutional networks for saliency prediction[END_REF] and has almost the same performance in this context.

Except SAM-VGG, SAM-resNet [START_REF] Cornia | Predicting human eye fixations via an lstm-based saliency attentive model[END_REF]and SalGAN [START_REF] Pan | Salgan: Visual saliency prediction with generative adversarial networks[END_REF], all the tested models perform quite poorly to predict salience on comics. The best unsupervised model is Zhang's model [START_REF] Zhang | Sun: A bayesian framework for saliency using natural statistics[END_REF]. The supervised models do not present a significant advantage over unsupervised ones. These observations, consistent with a previous analysis [START_REF] Khetarpal | A preliminary benchmark of four saliency algorithms on comic art[END_REF], were expected. Indeed, all these models have been designed for predicting saliency on natural scenes. Most of them embed implicitly or explicitly a center bias accounting for observers' viewing tendency to look at the center of screen. However, this viewing tendency is not systematic [START_REF] Meur | Introducing context-dependent and spatially-variant viewing biases in saccadic models[END_REF] and depends on the visual content display onscreen. For both webpages and comics, a top-left bias is observed. Considering this point, a straightforward approach consisting in weighting predicted saliency maps by a webpage prior (learned on eye tracking dataset [START_REF] Shen | Webpage saliency[END_REF]) may significantly increase the performance. For instance, the performance of iSEEL model significantly increases when a prior web is used. Although the gain in CC is about 0.07, the performances are still limited.

B. Performance of proposed models

Compared to existing methods, the proposed methods (see light green cells in Table II) outperform in a significant manner both unsupervised (see dark pink cells in Table II) and most of supervised models (see link pink cells in Table II). The training with eye tracking dataset on Sketches provides the best performance (i.e. with a correlation coefficient of 0.594). The proposed deep model trained on Webpages ranks second (i.e. with a correlation coefficient of 0.582). Performances, when Art and Cartoon are considered for the training, are poorer (i.e. with a correlation coefficient of 0.407 and 0.560, respectively). This ranking is in accordance with the similarity between joint distributions, as shown by Table I. Finally, when we consider all visual categories for the training (more than 400 images), the performances (e.g. CC = 0.566) are not as good as the model trained with Sketch (e.g. CC = 0.594) (only 100 images). All these observations suggest that it is possible to train efficiently a deep network on visual categories for which the gaze behavior is close to the targeted category. This transfer learning is especially interesting when datasets are small.

To go further, we train the proposed model with a dataset composed of predicted saliency maps computed on webpages; we call it fake dataset in Table II. Performances are rather good compared to unsupervised and supervised models. The fine-tuning of the pre-trained model with a real webpage dataset [START_REF] Shen | Webpage saliency[END_REF] does not allow to improve in a significant manner the performance. Moreover, this training strategy does not allow to outperform models tat are directly trained with real eye tracking data. This model performs at 0.501 whereas the best model trained with eye tracking data performs at 0.594.

V. Conclusion

In this paper, we train a deep network for predicting the salience on comics. Since existing eye tracking datasets for comics are extremely limited, we rely on transfer learning. We show that training the proposed deep network with visual categories showing similar gaze deployment to comic content provides the best performance. To be more specific, the use of eye tracking data collected on Sketches and on Webpages allows us to outperform in a significant manner the same deep network trained with eye tracking collected on Art and Cartoon. We also observe that it is even better to train the network with computationally generated data computed on Webpages than using real eye tracking data collected on visual categories, e.g. Art, for which the gaze deployment is dramatically different from the one observed on comics.

Fig. 1 .

 1 Fig.1. Distribution of saccade amplitudes and orientations for Comics (top-left), Cartoon, Art and Sketch from the CAT2000 eye tracking dataset[START_REF] Borji | Cat2000: A large scale fixation dataset for boosting saliency research[END_REF], and webpages from[START_REF] Shen | Webpage saliency[END_REF]. Bottom: Representative stimuli images of the visual categories.

Fig. 2 .

 2 Fig. 2. Deep network for predicting saliency on comics images. 1280 deep feature maps are extracted by using the VGG-16 network. A smaller network (bottom-right) is trained by using eye tracking data.

TABLE I

 I Similarity between Comics and Art, Cartoon, Sketch, Webpages categories in terms of gaze deployment (top) and Fourier spectrum (bottom). ↑ indicates the higher the better, whereas ↓ indicates the lower the better.

	Dist. similarity	Art	Cartoon Sketch Webpages
	KL-divergence ↓	0.376	0.400	0.267	0.2417
	Correlation ↑	0.773	0.796	0.813	0.854
	Similarity ↑	0.706	0.707	0.739	0.758
	Content similarity
	KL-divergence ↓	0.172	0.157	0.69	0.238

  TABLE II Performance of existing and proposed saliency models over comics dataset. Best performances are in bold. (AUC-B=AUC-Borji;

	AUC-J=AUC-Judd)
		CC ↑	SIM ↑	AUC-J ↑ AUC-B ↑ NSS ↑ EMD ↓	KL ↓
	Existing saliency models
	GBVS [23]	0.098	0.462	0.556	0.477	-0.006	3.183	1.118
	Zhang [24]	0.424	0.523	0.715	0.738	0.871	2.841	0.714
	AIM [25]	0.297	0.492	0.697	0.660	0.392	3.081	1.737
	SIM [26]	-0.023	0.442	0.496	0.467	-0.088	3.374	1.009
	AWS [27]	0.293	0.502	0.668	0.673	0.618	3.016	0.772
	RARE2012 [28]	0.058	0.419	0.556	0.522	0.100	3.639	1.217
	DeepConvNet [29]	0.378	0.543	0.680	0.629	0.622	2.590	1.485
	ML-Net [20]	0.280	0.488	0.668	0.581	0.429	2.570	1.629
	iSEEL [22]	0.342	0.537	0.670	0.581	0.442	2.418	0.925
	iSEEL [22]+webprior	0.411	0.554	0.712	0.646	0.618	2.126	0.782
	SAM-VGG [30]	0.517	0.549	0.769	0.670	1.011	1.690	2.112
	SAM-resNet [30]	0.609	0.601	0.811	0.733	1.279	1.508	1.569
	SalGAN [31]	0.556	0.599	0.784	0.749	1.091	1.963	0.824
	Proposed method with Sigmoid loss (see Section III)
	Trained with Art dataset [15]	0.407	0.525	0.688	0.684	0.756	2.666	0.796
	Trained with Cartoon dataset [15]	0.560	0.568	0.731	0.749	1.115	2.251	0.697
	Trained with Sketch dataset [15]	0.594	0.572	0.758	0.775	1.166	2.420	0.600
	Trained with Webpages dataset [16]	0.582	0.586	0.740	0.748	1.115	2.076	0.628
	Trained with all visual categories	0.566	0.570	0.740	0.742	1.085	2.376	0.624
	Trained with fake webpages dataset	0.501	0.540	0.730	0.748	0.971	2.646	0.676
	Trained with fake webpages dataset
	and fine tuning on [16]	0.511	0.545	0.727	0.743	0.982	2.575	0.666