Jules Vandeputte

Pierrick Herold

Mykyt Kuslii

Paolo Viappiani

Laurent Muller

Christine Martin

Olga Davidenko

Fabien Delaere

Cristina Manfredotti

Antoine Cornuéjols

Nicolas Darcel
email: nicolas.darcel@agroparistech.fr

Principles and Validations of an Artificial Intelligence-Based Recommender System Suggesting Acceptable Food Changes

Keywords: behaviour change, food recommendation algorithms, decision sciences

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Introduction

Changing eating behaviors is critical to ensure food systems that are both healthy and environmentally sustainable [START_REF] Willett | Food in the Anthropocene: the EAT-Lancet Commission on healthy diets from sustainable food systems[END_REF]. These changes are very difficult since eating habits are firmly rooted [START_REF] Jastran | Eating routines. Embedded, value based, modifiable, and reflective[END_REF] and resistance to changes is often strong [START_REF] Bouton | Why behavior change is difficult to sustain[END_REF][START_REF] John | Financial incentives for extended weight loss: a randomized, controlled trial[END_REF]. The use of computer-based recommender systems appears to be a promising strategy to change consumption behaviour towards more desirable diets. Smartphones have become a personal assistant for many individuals, beyond simply allowing customers to search for product information, compare prices, and seek feedback, smartphone apps could use algorithms to provide users with personalised suggestions [START_REF] Piotrowicz | Introduction to the Special Issue Information Technology in Retail: Toward Omnichannel Retailing[END_REF]. Such algorithms would interact with humans [START_REF] Oh | A systematic review of artificial intelligence chatbots for promoting physical activity, healthy diet, and weight loss[END_REF] to propose a series of punctual substitutions [START_REF] Ge | Using Tags and Latent Factors in a Food Recommender System[END_REF] or new recipes or entire meals [START_REF] Freyne | Intelligent food planning: personalized recipe recommendation[END_REF] and thus move individuals towards healthier diets through small iterative changes. Research in this field of application is still largely uncomplete and among the many research questions to be addressed in food recommendation technologies. The consumer acceptability of recommender systems relies on (i) the user's compliance with the machine and (ii) the relevance of its suggestions. Therefore two questions seem particularly salient: first, what should the artificial intelligence suggest? Second how should the artificial intelligence suggest? This question concerns the form of human-computer interaction that is most likely to ensure user compliance.

A first difficulty is that eating decisions are complex processes [START_REF] Köster | Diversity in the determinants of food choice: A psychological perspective[END_REF] influenced by numerous and intricated factors, such as individual preferences, social influences or contextual effects [START_REF] Sobal | Constructing Food Choice Decisions[END_REF][START_REF] Rozin | The socio-cultural context of eating and food choice[END_REF][START_REF] Higgs | People in context-The social perspective[END_REF][START_REF] Robinson | Liking food less: the impact of social influence on food liking evaluations in female students[END_REF][START_REF] Cruwys | Social modeling of eating: A review of when and why social influence affects food intake and choice[END_REF] and they all display a strong variability across individuals [START_REF] Schüz | It's the power of food: individual differences in food cue responsiveness and snacking in everyday life[END_REF] or groups [START_REF] Bennett | A Comparison of Dietary Patterns and Factors Influencing Food Choice among Ethnic Groups Living in One Locality: A Systematic Review[END_REF]. Identifying acceptable recommendations will therefore require an understanding of how one or several of these factors shape acceptability [START_REF] Mcauley | Inferring Networks of Substitutable and Complementary Products[END_REF][START_REF] Zheng | Substitutes or complements: another step forward in recommendations[END_REF]. While the literature on how acceptability is driven [START_REF] Symmank | Predictors of food decision making: A systematic interdisciplinary mapping (SIM) review[END_REF], either by individual preferences, food variability and dynamics, or by the effects of the social context is rather extensive, few studies have focused on the rules governing the meal composition. A meal or a menu is not just a random arrangement of foods, it is a complex assembly that often complies with very strict rules of associations or exclusions between food items. Since the deduction of such rules by a human observer would be very limited, machine learning algorithms could be used to explore large volumes of consumption data to come up with relevant suggestions. The first hypothesis tested is that a recommender system based on automatically learning simple association rules between dishes of the same meal would make it possible to identify plausible and acceptable substitutions from a consumer standpoint.

Finally, considering that the acceptability of a suggestion varies depending on how it is presented to users, identifying the most persuasive recommendation method is an essential issue. Several directions have so far been explored, such as personalization [START_REF] Aldenaini | How Effective is Personalization in Persuasive Interventions for Reducing Sedentary Behavior and Promoting Physical Activity: A Systematic Review[END_REF], gamification [START_REF] Radhakrishnan | Interactive Digital e-Health Game for Heart Failure Self-Management: A Feasibility Study[END_REF] but also the engagement of the user in the selection process [START_REF] Reilly | Dynamic Critiquing[END_REF]. The question of the effect of user engagement on acceptability is an open question. Indeed, two opposite hypotheses can be formulated. It can be argued that receiving a recommendation without much effort can be perceived as comfortable and attractive to the user, thus favoring his or her future acceptability. Conversely, it can be assumed that if the user feels that he or she has control over the suggestions, he or she will be more inclined to accept them in the end. The second hypothesis tested is that for an identical set of dietary swaps suggestions, the more the user is -or the more the user thinks to be-involved in the process of identifying the suggestion, the higher their probability of accepting the suggestion.

In this article we present three studies related to testing these hypotheses. The first two studies are related to our first hypothesis about the validity of automatic identification of food recommendation the last work focused on the hypothesis formulated above on the most effective form of the recommendation. Firstly, we present the principles of a method to reveal relevant substitutions from a large food consumption database. Secondly we evaluate the plausibility of these automatically mined suggestions via online tests. Afterwards, we investigate the persuasiveness of three suggestion methods of such recommendations in a population of healthy volunteers.

Methods

Design and implementation of an algorithm able to identify relevant substitutions

Problem statement and objectives Our first objective was to develop an algorithm able to mine substitutable foods. Given a database of consumed meals, we aimed to extract substitutability relationships based on the implicit rules applied by consumers when they compose their meals. The hypothesis on which we based our mining method of relevant substitutions was that "two food items are substitutable if they are consumed in a similar dietary context but rarely together". A detailed description of this algorithm has been realised and was the subject of a previous publication [START_REF] Akkoyunlu | Investigating substitutability of food items in consumption data[END_REF].

Defining context

The dietary context of a food item x is the set of food items C with which x is consumed. For instance, in the meal , the dietary context of is:

Computing a substitutability score Substitutability is not a binary relationship because foods can be substitutable to various extents. For instance, it is plausible to replace potatoes with rice, less plausible to replace them with bread, but much less plausible to replace those same potatoes with ice cream. Moreover, if two items are consumed together, they are less substitutable because they might be associated. Therefore, we designed a function to quantify the relationship of substitutability that incorporates the possibility of associativity. Detailed computing of the substitutability score are presented in supplementary methods

Mining of relevant substitutions

The French dataset INCA2 [START_REF] Anses | Données de consommations et habitudes alimentaires de l'étude INCA 2 -data[END_REF] was used for the mining of relevant substitutions. This dataset is the result of a survey conducted during 2006-2007 on individual food consumption. Individual 7-day food diaries were reported for 2624 adults and 1455 children over several months accounting possible seasonality in eating habits. It should be noted that since the start of this study, which was the subject of an initial communication, another more recent survey has become available [START_REF] Dubuisson | The Third French Individual and National Food Consumption (INCA3) Survey 2014-2015: method, design and participation rate in the framework of a European harmonization process[END_REF]. Since the results of the search for substitutability differed little over this period of time, we prefer to maintain consistency with the initial study. A typical day was composed of three main meals: breakfast, lunch and dinner. The moments in between were denoted as snacking. For the main meals, the location (home, work, school, outdoor) and the companion (family, friends, co-workers, alone) were registered. The 1280 food entries were organised in 110 groups of food items. We chose to consider this "subgroup" level of hierarchy to capture substitution relationships inter-groups and intra-groups. Indeed, it would have been possible to choose a finer categorisation (more groups) but this would have led to the identification of only substitutions between very similar sub-groups. On the other hand, we could have chosen a coarser categorisation (fewer groups), but this would have greatly constrained the algorithm's searches. The level of grouping chosen was hence determined in order to obtain the best compromise between very relevant but closely related changes and important but not very relevant changes. Only adults are considered in this work. The meal database was split according to contextual (type of meal) information in order to get better results. We compared the results of our methodology on three sub-datasets corresponding to (breakfast, lunch and dinner) as well as (breakfast and lunch and dinner).

Evaluation of the plausibility of the substitutions mined by the recommendation algorithm

To evaluate the extent to which the algorithm was able to generate plausible substitutions suggestions, an online task was designed in the manner of a "Turing test" [START_REF] Turing | Computing Machinery And Intelligence[END_REF] in which participants were asked to guess whether the proposed food substitutions were issued by an artificial intelligence or by a human being.

Participants

Volunteers were invited to participate in an online experiment via a public mailing list run by the French National Centre for Scientific Research (Information Relay in Cognitive Sciences, Paris, France, www.risc.cnrs.fr). The inclusion criteria were to be over 18 years of age and to be able read and understand French language properly. Participants could not participate more than once. Upon completion of the experiment, participants could enter in a draw to win 15 €. A total of 255 participants participated in the study, none of them reported to have guessed the objective of the study.

Online task

The experimental task consisted of three presentations of a series of 12 meals for which a proposal for change was made. Proposals were made either by a professional dietician, or by the substitution mining algorithm. In the latter case, to test the relevance of the substitutability scoring system, algorithm suggestions either reflected substitutions with the highest substitutability score (expert algorithm), or substitutions with a low substitutability score (clumsy algorithm). All these suggestions concerned the same items of the 12 same meals. For each pair of meal + modified meal, participants had to answer ('yes' or 'no') to the following question: "some of these suggestions are made by an artificial intelligence, others by a dietician, do you think this substitution was made by an artificial intelligence?".

The supporting software was developed using the PC IBEX platform [START_REF] Schwarz | PennController for Internet Based Experiments (IBEX)[END_REF].

Data analysis

The dependant variable is the binary answer to the question on the emitter of the substitution recommendation (human / non-human). Binary logistic regressions and resulting odds ratio were used to evaluate whether the answer was influenced by the actual type of emitter.

Acceptability of identified recommendation on a group of volunteers

This work examined whether the extent of the user involvement in the suggestion process substitutions by the AI-based recommender system affects the acceptability of that suggestion. For this purpose, a food coaching interface based on the plausible substitution mining algorithm presented earlier was tested on participants. Participants were different from the volunteers of the previously described online study.

Ethics approval

The study was conducted according to the Helsinki declaration guidelines and all procedures were approved by the Ethics Committee of Université Paris-Saclay (decision CER-Paris-Saclay-2021-055).

Written informed consent was obtained from all participants. Access to the General Data Protection Regulation (GDPR) is permanently available from the application interface. The participation in the experiment was compensated by a gift voucher worth 50€.

Participants

Based on similar studies [START_REF] Zheng | Substitutes or complements: another step forward in recommendations[END_REF][START_REF] Akkoyunlu | Investigating substitutability of food items in consumption data[END_REF][START_REF] Jaccard | The Distribution of the Flora in the Alpine Zone.1[END_REF][START_REF] Anses | Données de consommations et habitudes alimentaires de l'étude INCA 2 -data[END_REF], we estimated that 30 volunteers were needed for this study.

Considering a dropout and non-completion rate of the experiment of 50%, to obtain approximately 30 complete and exploitable responses, 60 candidates were recruited. The recruitment was done via an online form distributed via a public mailing list run by the French National Centre for Scientific

Research (Information Relay in Cognitive Sciences, Paris, France, www.risc.cnrs.fr). The inclusion criteria were being over 18 years old, not being on a diet, and owning a smartphone. To avoid possible effects of the order of presentation, the participants received the different modalities in a random way.

Operation of the algorithm:

A smartphone application (virtual nutrition coach) was specifically designed for this study. The principle was as follows: 1-the participant declares the meal they intend to eat the next day to the virtual coach. 2the virtual coach makes a substitution suggestion targeting one of the items of the meal according to three modes of suggestion (detailed below). 3-The participant can accept or refuse the suggestion(s). 4-If they accept, they commit to implement the recommendation and to certify it by sending a picture of their meal via the mobile application. For 1 declared meal made by the user, 4 suggestions were identified by the virtual nutrition coach via an online query to the substitution mining algorithm (described above), the list of these possible substitutions was based on the 4 most substitutable items presenting a better nutritional profile (according to their Rayner's score [START_REF] Rayner | Nutrient profiles: Development of Final Model Final Report[END_REF]). The three modalities were:

-(A) all 4 options were presented simultaneously, and the user could choose either one or nothing -(B) identified options were presented one by one and each time the user could refuse (in which case they must justify their refusal) until the list of proposals is exhausted, if the last proposal is not accepted then no option is chosen.

-(C) the coach asked the user for their preferences and proposed a single dish, which best matched the announced criteria. The user could either accept or refuse.

Measured parameters

At the beginning of the experiment, the volunteers filled out a questionnaire indicating their age, sex and BMI. For each recommendation session, the acceptance or refusal data were recorded, as well as the constituents of the meals filled in by the volunteers and the elements suggested by the coach.

Data collection procedure

The study used a within-subject design to test two meal conditions. The experiment lasted 3 weeks, spanning June and July 2021. During the sessions, the recruited volunteers had 6 recommendation sessions every Tuesday and Thursday, during which they received a suggestion that they could accept to implement the next day. These 6 sessions were divided into 2 sessions for each of the recommendation methods.

Statistical analysis

In order to explain the influence of the mode by which the recommendation is given on the probability of acceptance, a binary logistic regression analysis has been implemented. The statistical model used is therefore described as follows:

All statistical analyses were performed using R (version 3.6.3) and R-Studio (RStudio 2021.09.1+372

"Ghost Orchid" Release). To represent the probabilities of acceptance, odds ratios were computed from the logistic regression model.

Results

Identification of substitutions

Applied to the three meals datasets (breakfast, breakfast and lunch, lunch), the algorithm retrieved a series of substitutable items for all considered items in the database. Substitutable items for each element of a list of items for breakfast are represented in figure 1. If substitutions across food categories turned out to be proposed by the algorithm, the most frequent substitutable items were intra-category substitutions. When considering all foods listed in the consumption database, on average 6 % of all retrieved substitutions were within the same food category, this proportion increased to 20 % when only considering the three most substitutable items for each food. The substitutions proposed were also consistent with regards to eating practices: substitutes of drinks were also drinks: 54 % if considering all substitutions; 100 % when considering three most substitutable items (e.g.: the substitutes of coffee are tea, cocoa and chicory) or the substitutes for butter for breakfast are spreadable items (26 % vs 100 %).

Detailed description of the results obtained by this algorithm and in particular of the comparisons between substitutability scores according to the meals was carried out and was the subject of a previous publication of our team [START_REF] Akkoyunlu | Investigating substitutability of food items in consumption data[END_REF].

INSERT FIGURE 1 HERE

Plausibility of mined substitutions retrieved by the algorithm A total of 255 participants participated in the study, none of them reported to have guessed the objective of the study. When comparing human and artificial intelligence recommenders, we found that the probability that participants judge recommendations made by a human to be "made by non-humans" was low (0.26 ± 0.01), while the probability that participants judge recommendations made by an artificial intelligence (clumsy and expert) to be "made by non-humans" (0.67 ± 0.01) this difference appeared as highly statistically significant . When comparing the two artificial intelligence-based emitters (clumsy vs expert), we observed that a recommendation made by the "clumsy" artificial intelligence had a significantly higher chance of being judged as not emitted by a human (0.71 ± 0.01) than a recommendation made by the "expert" (0.64 ± 0.01), here again this difference appeared as highly statistically significant .

Real life acceptability of identified suggestions and effect of recommendation methods

Of the 60 candidates initially enrolled in the experiment, the results of 27 of them were complete and exploitable for analysis (yielding a total of 162 meals). The final sample was composed of 20 women and 7 men. The average age was 37.5 ± 15.2 years. The average BMI was 22.2 ± 4.1 with two overweight individuals and two others moderately obese. Of the 162 interaction outcomes between the participants and the coach we observed that 74 of them resulted in the acceptance of a recommendation, reflecting an overall average acceptability of 46%.

Analysis of the odds ratios corresponding to the different factors of influence of the acceptability revealed that modality B only, (i.e. when the user is participating to the selection of the suggestions, OR:

3.168; CI 95%: 1.688, 6.061; p<0.0004) was associated with an odds ratio exceeding the significance threshold. A tendency was noted for the effect of age (p=0.08) indicating that younger participants had a higher propensity to accept recommendations. Sex (p=0.14), and BMI (p=0.32) did not have a significant effect on acceptability.

Discussion

This work presents an artificial intelligence-based food recommendation system designed to make suggestions of food substitutions to its users. Two critical issues raised by such a recommendation technology were addressed in this study: first, the method to identify relevant substitutions suggestions and, second, the most efficient form that should be used to ensure acceptability from users. To identify acceptable substitutions, we hypothesised that two foods consumed with the same other foods can be replaced by each other with a relatively high probability of acceptance. Our results indicated that such a method, based on the analysis of a large-scale food consumption database and without any prior information of the considered dishes, showed overall coherence as the suggested swaps were often within similar food types. We also found that such mining of relevant substitutions based solely on the analysis of the meal context, allowed to produce plausible suggestions from a human perspective.

Regarding the form that should be used to suggest a substitution, our results obtained from a group of 27 volunteers over a period of 3 weeks suggest that, when users are involved in the selection of the suggestion, the resulting suggestions are more likely to be accepted.

The substitutability score presented in this study was based on the estimation, for two foods to be substituted, to what extent these items are consumed in similar meals (i.e., with the same other foods).

While no semantic information describing ingredients or usual positioning of foods in meals was available for the recommender system, substitutions between food items of the same nutritional food groups were found. Such contextual information appears to be relevant to derive food substitutions. To our knowledge, this is the first study showing the richness of the information contained not in what we eat but in what we combine with what we eat. A substantial amount of research has already been performed in the area of recommendations to induce behaviour changes [START_REF] Ge | Using Tags and Latent Factors in a Food Recommender System[END_REF][START_REF] Freyne | Intelligent food planning: personalized recipe recommendation[END_REF][START_REF] Zheng | Substitutes or complements: another step forward in recommendations[END_REF][START_REF] Vandeputte | Coaching Agent: Making Recommendations for Behavior Change. A Case Study on Improving Eating Habits[END_REF], most existing approaches focused on recommending similar foods (with similar taste for instance) to consumers without considering any additional contextual information. By showing that such information may matter in the acceptability decision, we believe that we are opening a promising field of research for automated dietary recommendations. The main strength of this study is that it links fundamental algorithmic considerations related to dietary decisions with studies on healthy volunteers in online or real-life consumption conditions.

Nevertheless, this work has focused on identifying solutions that are acceptable to the consumer. In this context, we have chosen to study the plausibility of the identified solutions only from the consumer's point of view, without trying to identify changes leading to a better nutritional quality diet. But it is obvious that such technologies, in order to be really effective on improving the quality of diets, will have to integrate additional filtering rules allowing to select, among the plausible suggestions, only the foods improving the quality of the diet. For this purpose, scores taking into account past food consumption history could be used [START_REF] Chaltiel | Programme National Nutrition Santéguidelines score 2 (PNNS-GS2): development and validation of a diet quality score reflecting the 2017 French dietary guidelines[END_REF].

Additional limitations are that food swaps recommendations issued by this work cannot be generalised as the data set was collected in France and may not be relevant for other countries. Also as the dataset was obtained in 2006-7, the substitution relationships are likely to have changed along with the modification of the food offer, for example, an increase in plant-based meat substitutes would require new data on their consumption relationships. Future work will need to address these limitations.

In a second step, using a task in which participants were invited to judge if the emitter of a given suggestion was a human or a machine, we observed that recommendations made by an artificial intelligence were often recognized as originating from a non-human recommender. This indicates that there is a margin of progress in our ability to derive substitutability information from food consumption databases. Interestingly, we observed that depending on the substitutability scores chosen by the artificial intelligence for selecting the recommendation (best scores or weaker scores), the participants estimated differently the plausibility of the resulting suggestions, as the higher the score was the lower the probability of judging the emitter as a non-human. This substitutability score is thus a promising but perfectible proxy of the acceptability of substitutions. To gain predictive capacity, it could be useful to extend the concept of "contextual information" to include other information that is known to influence eating decisions such as the consumptions made at the preceding meals, the time and/or the location [START_REF] Köster | Diversity in the determinants of food choice: A psychological perspective[END_REF][START_REF] Bisogni | Who we are and how we eat: a qualitative study of identities in food choice[END_REF] of the meal, or the company of others [START_REF] Higgs | Social norms and their influence on eating behaviours[END_REF][START_REF] Vartanian | Modeling of food intake: a meta-analytic review[END_REF].

It is however important to be cautious when interpreting these results. We indeed assumed that the criteria on which participants based their response was the plausibility of the suggestion (e.g. "if I think it is a non-sense, it must have been issued by a machine"). However, it cannot be excluded that participants' responses were based on other criteria. The reasoning that they would attribute to the machine (e.g., "the recommender is expected to make recommendations being of higher nutritional quality, if it is not the case it must be a machine") could strongly influence the results. It would thus be interesting to combine these approaches with qualitative measures in which explanations of the reasons for the choices indicated in the task would be recorded.

In a third step, we showed on a cohort of 27 participants that among three modalities of suggestion presentation, the one that seemed to have the highest efficiency was the one that involved the user in repeated exchanges, and even if the suggested options were the same as what would have been offered by the other modalities, the impression of dialogue and thus of control over the production of the recommendation yielded the greater acceptability from the consumer. In the field of recommender systems, such an approach is called critiquing-based recommendation as it relies on users' feedback (critiques) to iteratively improve the recommendation's acceptability. The advantage of such an approach can be explained by the illusion of control that individuals may have in the case of a critiquingbased system. Indeed, studies in psychology or behavioural economics on the endowment effect [START_REF] Morewedge | Thought for Food: Imagined Consumption Reduces Actual Consumption[END_REF] (popularly known as the IKEA effect, ( 39)) have established that the more we engage in a task (in this case, finding a consumption suggestion), the more we are attached to the result, and the greater the value of this result is for us.

The critiquing-based recommendation modality system has emerged as the most acceptable, it is interesting to note that this performed even better than modality C which registered preferences before making a recommendation. This advantage could be explained by the fact that when user's preferencesbased recommender systems have little information about the users they often fail to establish a meaningful profile (cold start problem [START_REF] Lika | Facing the cold start problem in recommender systems[END_REF]). Conversely, it is likely that if virtual coaches had access to large amounts of data on user preferences, they would be more effective than critiquing-based coaching modalities.

Such results should however not be generalised, as the profile of the participants remains not representative, being constituted mainly by young women. The number of participants and the design of the study did not allow to identify different profiles, but, if the modality based on "critiquing" seems to be more effective, we cannot exclude that another modality would be more effective in inducing behavioural changes for some subgroups.

Conclusion

While computerised recommender technologies may be a promising tool for changing eating behaviours, several questions remain open. First, concerning the methods for selecting acceptable alternatives, we proposed a substitutability score based on a limited amount of contextual information (i.e., foods eaten with the food to be substituted) and showed that it can be used to identify plausible and acceptable alternatives. This is a promising approach, but it is highly perfectible and will gain in quality if we consider other contextual elements in the substitution retrieval method. Second, the interactions between the recommendation technologies and the users are another facet determining the acceptability that it is necessary to consider. We have highlighted that approaches involving the users seem to have an advantage, but this work remains to be refined by putting into perspective the individual profiles and the most effective suggestion modalities. Future research should therefore focus on understanding the dynamics of consumption (recent food history, for example) and on the effects of the diversity of consumer profiles on the types of human-machine interactions to consider. Finally, additional work will also have to take into account the nutritional quality of the suggested swaps to improve overall diet quality.

FIGURE 1 :

 1 FIGURE 1: Substitutability scores computed between most frequent breakfast items: The breakfast

Acknowledgements:

The authors of this article are thankful to the "PennController for IBEX" team for providing us with this user-friendly virtual experimentation domain: Florian Schwarz and specially Jeremy Zehr who answered all our questions regarding the programming and coding parts of the online experiment. Author contributions: J.V: methodology, investigation, formal analysis, visualization, writingoriginal draft;


Marie Anquetil

Nadège Roche-Labarbe

Sandrine Rossi

Tactile sensory processing as a precursor of executive attention: toward early detection of attention impairments and neurodevelopmental disorders

Recent studies in developmental neuroscience tend to show the existence of neural attention networks from birth. Their construction is based on the first sensory experiences that allow us to learn the patterns of the world surrounding us and preserve our limited attentional resources.

Touch is the first sensory modality to develop, although it is still little studied in developmental psychology in contrast to distal modalities such as audition or vision. Atypical tactile sensory processing at an early age could predict later attention dysfunction, both of them being part of the symptomatology of neurodevelopmental disorders (NDD). We review the state of knowledge on tactile sensory processing and its links with attention, executive attention in particular, and propose that abnormal tactile sensory processing at an early age could provide markers of executive attention dysfunctions, contributing to the early detection of NDD.

Graphical/Visual Abstract and Caption

Tactile sensory perception at an early age could predict the quality of executive attention development and thus provide new leads for the early detection of neurodevelopmental disorders.

Introduction

Humans first experience the world through sensory perception, long before birth. During the first weeks of life, sensory perception is actively used by the newborn and infant to make sense of the world, building the first cognitive processes. The tactile modality is the first sense to develop and is the foundation of sensory development [START_REF] Bremner | Chapter Seven -The Development of Tactile Perception[END_REF]. According to the neuroconstructivist framework, early sensory experiences constrain the construction of brain networks which develop considerably during the first years of life [START_REF] Karmiloff-Smith | Preaching to the Converted? From Constructivism to Neuroconstructivism[END_REF]. Variability in perceptual skills at an early age, therefore, affects neural networks underlying attention systems [START_REF] Rohr | Functional network integration and attention skills in young children[END_REF] and predicts later cognitive development [START_REF] Bertenthal | Advancing Our Understanding of Early Perceptual and Cognitive Development[END_REF]. Atypical sensory profiles, particularly affecting somesthetic senses, are a frequent feature of neurodevelopmental disorders (NDD) [START_REF] Cascio | Somatosensory processing in neurodevelopmental disorders[END_REF].

Executive attention (EA) is a cognitive construct part of the three attentional networks modelized by [START_REF] Posner | The Attention System of the Human Brain[END_REF] and necessary to executive functions (EF), formed by different cognitive processes like inhibition and interference control, cognitive flexibility, and updating in particular [START_REF] Diamond | Executive Functions[END_REF]. There is increasing interest in the early development of EF because they are critical for school achievement, and because executive impairment is also a common feature of NDD [START_REF] Ozonoff | Brief Report: Specific Executive Function Profiles in Three Neurodevelopmental Disorders[END_REF].

NDD are characterized by developmental impairments in cognitive, motor, affective and social skills. They include various disorders such as attention-deficit/hyperactivity disorder (ADHD), autistic spectrum disorder (ASD), communication disorders (CD), intellectual disability (ID), learning disabilities (LD), and motor disorders (MD). A systematic review by [START_REF] Francés | Current state of knowledge on the prevalence of neurodevelopmental disorders in childhood according to the DSM-5 : A systematic review in accordance with the PRISMA criteria[END_REF] reports the following worldwide incidence rates: ADHD, 5-11%; ASD, 0.70-3%; CD, 1-3.42%; ID, 0.63%; LD, 3-10%; and MD, 0.76-17%. In European countries such as Spain, the study of [START_REF] Bosch | Neurodevelopmental disorders among Spanish school-age children: Prevalence and sociodemographic correlates[END_REF] reports the following incidence rates: ADHD, 9.92%; ASD, 0.70%; CD, 1.05%; ID, 0.63%; LD, 10.0%; and MD, 0.76%. In the United States, the data show an increase in the overall prevalence of disorders between 2014 and 2016 [START_REF] Zablotsky | Estimated Prevalence of Children With Diagnosed Developmental Disabilities in the United States, 2014-2016[END_REF]. The authors also point out the variability of prevalence rates depending on the pathology, the population, and also on the resources available to clinicians for diagnosis.

One of the global public health challenges is to better understand the emergence of NDD to propose early detection and remediation programs [START_REF] Ozonoff | Editorial: Early detection of mental health and neurodevelopmental disorders: the ethical challenges of a field in its infancy[END_REF]. Although there is extensive knowledge of the many environmental risk factors for NDD [START_REF] Arpino | Preterm birth and neurodevelopmental outcome: A review[END_REF][START_REF] De Felice | Multifactorial Origin of Neurodevelopmental Disorders: Approaches to Understanding Complex Etiologies[END_REF][START_REF] Martens | Genetic and Environmental Factors in Complex Neurodevelopmental Disorders[END_REF] abnormal brain development is often reported as a vulnerability to these subsequent risk factors and precedes clinical impairment [START_REF] Jeste | Neurodevelopmental Behavioral and Cognitive Disorders[END_REF].

We propose that atypical tactile perception at an early age puts children at risk of developing atypical attention, evolving into dysexecutive syndromes at a later age, these deficits being aspects of the same pathological process in NDD. We review research on tactile sensory processing and EA in young children and consider new research paradigms integrating these two components to provide new markers of EA deficit. Such markers could be used for the early identification of children at risk of EF NDD and other cognitive impairment, and to design new early interventions.

SOMATOSENSORY PROCESSING IS THE FOUNDATION OF COGNITIVE

DEVELOPMENT

The somatosensory system collects all the information that we receive from our tactile skin receptors (touch, pain, temperature) and proprioceptive receptors in joints and ligaments (head and body position and movement). Touch is the first sensory modality to develop and the first sensations we experience are tactile [START_REF] Bremner | Chapter Seven -The Development of Tactile Perception[END_REF]. As early as 7 weeks of gestation, the first tactile receptors are mature and somatosensory functions quickly follow.

Touch is considered the foundation upon which other perceptive, cognitive, motor and affective functions develop [START_REF] Ardiel | The importance of touch in development[END_REF][START_REF] Lickliter | The integrated development of sensory organization[END_REF][START_REF] Dumont | Psychobiological foundations of early sensory-motor development and implications for neonatal care[END_REF]. Because of this sensory scaffolding function, touch is believed to play a critical role in attachment [START_REF] Weiss | The tactile context of a mother's caregiving: Implications for attachment of low birth weight infants[END_REF] and early sensory-motor development [START_REF] Fearon | Development of tactile responses in human preterm and full-term infants form 30 to 40 weeks postconceptional age[END_REF], the representation of our own body in space [START_REF] Serino | Touch and the body[END_REF], and to be the precursor of verbal communication [START_REF] Hertenstein | The Communicative Functions of Touch in Humans, Nonhuman Primates, and Rats: A Review and Synthesis of the Empirical Research[END_REF]. A recent review of the importance of touch for all aspects of development can be found in [START_REF] Cascio | Social touch and human development[END_REF]. Despite the relevance of tactile processing for understanding both typical and pathological developmental courses, early somatosensory development remains poorly exploited in developmental cognitive science, compared with the later-developing modalities of audition and vision. In this section, we address how somatosensory processing, and particularly prediction, is related to attention development.

Somatosensory processing and prediction

One of the fundamental mechanisms of cognitive development is sensory prediction. The brain constantly extracts information from its environment to develop internal mental models that will serve to predict future sensory inputs [START_REF] Friston | A theory of cortical responses[END_REF]. Predictions optimize our cognitive and behavioral resources by anticipating changes in our environment and preparing appropriate responses, resulting in a cognitive and motor economy [START_REF] Schubotz | Prediction and Expectation[END_REF]. The infant's brain is highly predictive from a young age and prediction guides sensory processing and learning [START_REF] Baek | How an infant's active response to structured experience supports perceptual-cognitive development[END_REF]. From the age of 2 months, infants can anticipate the caregiver's actions, for example when being picked up, and adjust their posture to enter into interaction [START_REF] Reddy | Anticipatory Adjustments to Being Picked Up in Infancy[END_REF]. In a recent study, [START_REF] Dumont | Somatosensory prediction in the premature neonate brain[END_REF] showed that premature neonates can predict a tactile stimulus and that the somatosensory cortex activity is modulated according to the temporal regularity of the stimulation sequence, similar to what has been described in adults [START_REF] Van Ede | Anticipation Increases Tactile Stimulus Processing in the Ipsilateral Primary Somatosensory Cortex[END_REF]. Somatosensory representations support body representations [START_REF] Bremner | Chapter Seven -The Development of Tactile Perception[END_REF], therefore, they are critical for the development of motor planning and motor control [START_REF] Assaiante | Body schema building during childhood and adolescence: A neurosensory approach[END_REF]. Sensory prediction then develops in distal modalities such as auditory in 6-months-olds [START_REF] Emberson | Expectation affects neural repetition suppression in infancy[END_REF] and visual in 12-months-olds [START_REF] Kouider | Neural dynamics of prediction and surprise in infants[END_REF]. Impaired tactile prediction may thus impair both sensory prediction in later developing distal modalities and motor development. In support of this hypothesis, experimental data show that performance in motor control and executive skills such as inhibition and working memory are related in infants [START_REF] Gottwald | An Embodied Account of Early Executive-Function Development: Prospective Motor Control in Infancy Is Related to Inhibition and Working Memory[END_REF], and therefore, may rely on common processes early in life. Indeed, anticipating a stimulus regulates its processing, be it enhancement or suppression depending on the stimulus' relevance [START_REF] Summerfield | Neural repetition suppression reflects fulfilled perceptual expectations[END_REF], and this process forms the basis of attention, the ability to select and filter relevant sensory inputs depending on context and goals.

Prediction and executive attention

Attention commonly refers to a state in which we have a level of concentration that allows us optimal filtering of information from the environment [START_REF] Conejero | Early Development of Executive Attention[END_REF]. During the first months of life, the salience network (SN), consisting of the dorsal anterior cingulate cortex and anterior bilateral insulae [START_REF] Ham | Cognitive Control and the Salience Network: An Investigation of Error Processing and Effective Connectivity[END_REF] supports the development of exogenous attention, captured by salient stimuli in the environment, such as being touched. The SN partially overlaps with the alerting and orienting attention networks [START_REF] Uddin | Salience network of the human brain[END_REF], as they were described by [START_REF] Posner | The Attention System of the Human Brain[END_REF]. Alerting and orienting attention develop first, then they serve as the foundation for endogenous attention, controlled by the subject and directed towards relevant but not salient stimuli. This latest form of attention is called executive attention (EA). The successive development of attention networks (alerting, orienting, and executive attention networks) during the first years of life is associated with behavioral improvements in tasks involving executive functions (EF) such as inhibition of distractors [START_REF] Holmboe | Freeze-Frame: A new infant inhibition task and its relation to frontal cortex tasks during infancy and early childhood[END_REF] and tasks combining executive and motor control such as reaching for a hidden object [START_REF] Diamond | Developmental Time Course in Human Infants and Infant Monkeys, and the Neural Bases of, Inhibitory Control in Reaching[END_REF].

EA and EF are related constructs. EA is associated with the fields of attention and cognitive psychology and designates mechanisms that enable regulation between our perceptions and our actions [START_REF] Rueda | Attention: The grounds of self-regulated cognition[END_REF]. EF is associated with the field of neuropsychology and refers to top-down control mechanisms involved in the modulation of our thoughts and actions when automatic processes are not sufficient. EA is a component of inhibitory control in Miyake's model of EF (Miyake & Friedman, 2012), but it is also a component of Self-Regulation (SR) in Diamond's model [START_REF] Diamond | Executive Functions[END_REF]. SR is an internal process that allows us to regulate our thoughts, emotions, and behaviors to overcome an internal conflict [START_REF] Nigg | Annual Research Review: On the relations among self-regulation, selfcontrol, executive functioning, effortful control, cognitive control, impulsivity, risk-taking, and inhibition for developmental psychopathology[END_REF]. Although these models of adult cognition and neuropsychology consider EA as a part of EF or SR, from a developmental standpoint they should be conceived differently. The literature on young children shows that EF and SR are in fact overlapping constructs and that EA is a distinct and earlier process serving as the developmental foundation for the development of these two functions [START_REF] Garon | Executive function in preschoolers: A review using an integrative framework[END_REF][START_REF] Tiego | Common mechanisms of executive attention underlie executive function and effortful control in children[END_REF]. EA promotes EF and SR by allowing the child to control what sensory information is being processed. It enhances or dampens processing based on contextual or goal-related relevance [START_REF] Kok | Attention reverses the effect of prediction in silencing sensory signals[END_REF], and predictions that were built through previous experience [START_REF] Bubic | Prediction, cognition and the brain[END_REF]. [START_REF] Johansson | Sustained attention in infancy as a longitudinal predictor of self-regulatory functions[END_REF] confirmed that sustained attention at 1 year of age is predictive of EF and SR at 2 years.

In this section we described why somatosensory prediction is a precursor of multisensory prediction, which allows exogenous forms of attention to emerge and scaffold the development of executive attention, which in turn serves as a developmental step toward SR and EF, fostering learning and higher order cognition. Individual differences in EA appear in early infancy and remain stable across childhood [START_REF] Johansson | Sustained attention in infancy as a longitudinal predictor of self-regulatory functions[END_REF], and children with greater EA efficiency have better understanding and performance in school, which promotes academic success and well-adjusted behaviors in the classroom [START_REF] Blair | Relating Effortful Control, Executive Function, and False Belief Understanding to Emerging Math and Literacy Ability in Kindergarten[END_REF]. Therefore, it is important to understand individual differences in the earliest foundations of EA such as prediction, and this can be achieved in the youngest babies by using the somatosensory modality. In the following section, we review how the constructs discussed above are affected in NDD.

SENSORY IMPAIRMENTS AND EXECUTIVE ATTENTION DEFICITS AT THE CORE OF NEURODEVELOPMENTAL DISORDERS

The prevalence of NDD in western countries is increasing [START_REF] Elsabbagh | Global Prevalence of Autism and Other Pervasive Developmental Disorders[END_REF][START_REF] Zablotsky | Estimated Prevalence of Children With Diagnosed Developmental Disabilities in the United States, 2014-2016[END_REF]. Many factorsgenetic, perinatal, and environmentalare involved in the etiology of NDD, and some tend to increase, such as environmental pollution or late pregnancies, which could play a role in this phenomenon. Among them, one possible explanation is that improvements in neonatal intensive care reduce mortality rates in prematurely born children whereas preterm birth is a high-risk factor for atypical neurodevelopment, especially in very preterm infants (<32 weeks) [START_REF] Ancel | Le point sur la prématurité en 2009[END_REF]. Cohort follow-up studies such as Epipage-2 in France or Epicure in the UK report increased developmental deficits in a variety of domains:

cognitive, motor alterations, and socio-emotional problems which are later expressed in heterogeneous patterns [START_REF] Twilhaar | Profiles of Functioning in 5.5-Year-Old Very Preterm Born Children in France : The EPIPAGE-2 Study[END_REF]. Sensory profiles and EF deficits in the preterm population, even without NDD diagnosis, are similar to those found in children diagnosed with NDD regardless of birth status, suggesting common pathological processes in these two populations for these particular domains [START_REF] Bröring | Sensory modulation in preterm children: Theoretical perspective and systematic review[END_REF]. In this section, we discuss the sensory and executive aspects of NDD symptomatology and how somatosensory processing impairments may be early precursors of attention and executive deficits in NDD trajectories.

Somatosensory impairments in neurodevelopmental disorders

NDD symptomatology frequently involves atypical sensory processing or modulation, particularly in the tactile modality [START_REF] Cascio | Somatosensory processing in neurodevelopmental disorders[END_REF]. Tactile defensiveness, i.e., hyperreactive or aversive reactions to innocuous tactile stimulations, is often reported in ASD, Tourette's syndrome, and ADHD.

In boys with ADHD, [START_REF] Parush | Somatosensory function in boys with ADHD and tactile defensiveness[END_REF] found that tactile defensiveness is associated with deficits in finger discrimination and larger somatosensory evoked potentials (SEP) on the electroencephalogram (EEG). The authors suggest it results from an impaired inhibition of the cortical processing of tactile inputs.

In children with ASD, [START_REF] Tomchek | Sensory processing in children with and without autism : A comparative study using the short sensory profile[END_REF] used Dunn's Sensory Profile, a questionnaire designed to quantify sensory processing in different modalities in everyday situations [START_REF] Dunn | Factor Analysis on the Sensory Profile From a National Sample of Children Without Disabilities[END_REF]. Completed by a caregiver, it provides a basis for assessing the impact of sensory processing on the child's performance and quality of life. They found that 95% of children with ASD have sensory processing dysfunction, with greater differences in underresponsiveness or sensory-seeking behaviors, auditory filtering, and tactile sensitivity.

Recently, [START_REF] Espenhahn | Tactile cortical responses and association with tactile reactivity in young children on the autism spectrum[END_REF] found that the N140 response, a component of SEP, had a lower amplitude and shorter latency in children with ASD. As for ADHD, the authors propose that an imbalance between excitation and inhibition in the brain underlies these observations. This is consistent with observations in mouse models of ASD: genetic alterations linked with ASD in some human patients cause alterations in mechanoreceptors, leading to impairments in inhibition of peripheral somatosensory neurons in mice [START_REF] Orefice | Peripheral Mechanosensory Neuron Dysfunction Underlies Tactile and Behavioral Deficits in Mouse Models of ASDs[END_REF]. This results in impaired tactile discrimination and sensitivity, provoking anxiety and aberrant social behaviors when mice grow up. In parallel, human studies of children and adolescents with ASD revealed overactivation of the neural salience network, suggesting an overinvestment of attentional resources in favor of basic sensory processing [START_REF] Green | Salience Network Connectivity in Autism Is Related to Brain and Behavioral Markers of Sensory Overresponsivity[END_REF]. Sensory overload would not allow for investment in social interactions with others and would favor more rigid routines.

Hence, tactile processing impairment is not a correlate but a precursor of other autistic symptoms. It is interesting that this seems specific to NDD and not to all behavioral issues: [START_REF] Mammen | Infant Avoidance During a Tactile Task Predicts Autism Spectrum Behaviors in Toddlerhood[END_REF] found that tactile avoidance in 9 months old infants was not predictive of emotion regulation deficits (neither internalizing nor externalizing symptoms) at 18 months, but was predictive of pervasive developmental disorders symptoms.

In the recent paper by [START_REF] Espenhahn | Tactile cortical responses and association with tactile reactivity in young children on the autism spectrum[END_REF], it was particularly interesting that differences in SEP correlated with parent-reported tactile reactivity in both ASD and typical children, supporting a dimensional approach to NDD and their somatosensory precursors. Even before the age of a possible diagnosis, abnormal sensory profiles are also more prevalent in children at high risk of NDD because of premature birth [START_REF] Wickremasinghe | Children born prematurely have atypical Sensory Profiles[END_REF]. In addition to untimely stress on all physiological systems, first sensory experiences are modified due to the premature rupture with the intrauterine environment. Tactile stimulation is altered by the medical environment and care protocols (blood sampling or punctures for example) that impair sensory integration [START_REF] Dumont | Psychobiological foundations of early sensory-motor development and implications for neonatal care[END_REF]. [START_REF] Chorna | Abnormal sensory reactivity in preterm infants during the first year correlates with adverse neurodevelopmental outcomes at 2 years of age. Archives of disease in childhood[END_REF] showed that in children born before 30 weeks of gestational age, sensory reactivity was abnormal at the age of 24 months.

Responses to tactile deep pressure and vestibular stimulation were most frequently affected, as confirmed by [START_REF] Crozier | Sensory Processing Patterns in Children Born Very Preterm[END_REF]. Longitudinal studies of preterm neonates showed that sensory processing was predictive of later cognitive development and the onset of ASD symptoms from the age of two years [START_REF] Vlaeminck | Predicting cognitive development and early symptoms of autism spectrum disorder in preterm children: The value of temperament and sensory processing[END_REF]. In very preterm neonates, lower tolerance of handling at birth was associated with tactile sensitivity and poorer executive functioning at 4 years [START_REF] Meether | Neurobehaviour of very preterm infants at term equivalent age is related to early childhood outcomes[END_REF].

Sensory processing impairment, and particularly somatosensory, is a common feature of the symptomatology of children with NDD, mainly ASD and ADHD, and of children at high risk of NDD such as prematurely born infants. We now need to address the mechanisms mediating the relationship between somatosensory processing and NDD, taking into account the variety of syndromes. EEG studies in ADHD show increased SEP amplitude [START_REF] Parush | Somatosensory function in boys with ADHD and tactile defensiveness[END_REF], whereas in ASD they found decreased amplitude and shorter latency [START_REF] Espenhahn | Tactile cortical responses and association with tactile reactivity in young children on the autism spectrum[END_REF].

The discrepancy may come from other aspects of the stimulation, such as varying attentional demands exerting opposite effects on somatosensory processing depending on the disorder. In the previous section, we described how somatosensory prediction is the first building block of attention, the core mechanism of EF. Comparing SEP in ADHD and ASD patients for either expected or unexpected auditory stimuli, Gonzalez-Gadea et al. ( 2015) observed opposite frontal responses: ASD patients had increased frontal activity to expected stimuli and reduced activity to unexpected stimuli, whereas ADHD patients exhibited the opposite pattern. In other words, ASD patients dedicated more attention to predictable events, while ADHD patients favor unpredicted events. Both disorders build atypical attentional processes on atypical sensory processing but in different ways. This is consistent with differences in executive functioning observed in these populations as they grow up.

Executive and attention deficits in neurodevelopmental disorders

EF and attention impairments partially overlap across the various NDD [START_REF] Otterman | Executive functioning and neurodevelopmental disorders in early childhood: A prospective population-based study[END_REF].

Executive dysfunction has been demonstrated in ASD, mainly poor cognitive flexibility, for example using the Wisconsin Card Sorting Test (WCST) and its version for preschool children, the Dimensional-Change Card-Sort task (DCCS) [START_REF] Dichter | Performance of Children with Autism Spectrum Disorders on the Dimension-Change Card Sort Task[END_REF], but also planning difficulties [START_REF] Hill | Executive dysfunction in autism[END_REF]. Shifting and planning impairments explain the insistence on sameness that leads to highly distressing situations for children with ASD and their families. Authors proposed that attention deficits would be the underlying cause of EF deficits in ASD patients, but also the cause of their difficulties in processing social information in all sensory modalities.

The Attention Network Test (ANT) is designed to assess the efficiency of Posner's attention networks and was adapted for children by [START_REF] Rueda | Development of attentional networks in childhood[END_REF]. Using the ANT, Mutreja et al. (2015) found that children with ASD had specific impairment in orienting and executive attention, not alerting. Imaging studies showed that attention performance in ASD may be sustained by compensatory mechanisms in alternate brain networks [START_REF] Vaidya | Controlling attention to gaze and arrows in childhood: An fMRI study of typical development and Autism Spectrum Disorders[END_REF], resulting in a coupling between orienting and executive attention in ASD patients [START_REF] Keehn | Attentional networks in children and adolescents with autism spectrum disorder[END_REF], whereas these networks are independent in typically developing children. As a consequence of the chronology of the development of attention networks, the orienting impairment in ASD causes a secondary impairment in EA, resulting in EF deficits. ADHD on the other hand is by definition an attention deficit, reflected in inhibition deficits, for example tested by Go/No-Go or stop signal tasks [START_REF] Senderecka | Response inhibition of children with ADHD in the stop-signal task: An event-related potential study[END_REF], and everyday difficulties in behavioral regulation and learning [START_REF] Barkley | Behavioral inhibition, sustained attention, and executive functions: Constructing a unifying theory of ADHD[END_REF]. Using the ANT, Fabio and Urso (2014) described a specific EA impairment in children with ADHD compared with controls, but also compared with children with low attention without ADHD in routine screening questionnaires, confirming that EA deficit is a specific marker of NDD, not any developmental difficulty. It is important to note that attention deficits in ADHD also impair performance in task-switching [START_REF] Rauch | To what extent are task-switching deficits in children with attention-deficit/hyperactivity disorder independent of impaired inhibition?[END_REF] and that 30% of children with ASD also present comorbid ADHD, cumulating flexibility and inhibition impairments [START_REF] Craig | A review of executive function deficits in autism spectrum disorder and attentiondeficit/hyperactivity disorder[END_REF]. Children who have poorer EF when they start first core learnings in kindergarten are at greater risk for learning difficulties in elementary school [START_REF] Morgan | Executive function deficits in kindergarten predict repeated academic difficulties across elementary school[END_REF], compromising later academic success.

LD are frequently comorbid with ADHD but can be expressed distinctly [START_REF] Mayes | Learning Disabilities and ADHD: Overlapping Spectrum Disorders[END_REF].

Consistent with the link between motor and executive development, resulting from their common foundations in somatosensory processing, children with developmental coordination disorders or other motor difficulties also have poorer performance on executive tasks [START_REF] Leonard | Executive Functioning, Motor Difficulties, and Developmental Coordination Disorder[END_REF]. The heterogeneity of symptoms among patients with the same disorder, the frequent co-morbidity of NDD in the same patient, and the overlap of symptoms among NDD, strongly suggest they have common organic etiological factors and physiopathological pathways [START_REF] Taurines | ADHD and autism: differential diagnosis or overlapping traits? A selective review[END_REF]. These issues make early NDD detection and diagnosis a challenge when we consider each syndrome separately, but represent an opportunity if we assume a cross-syndromic approach and search for the early, sensory foundations of attention deficits. The neural mechanisms involved in atypical information processing in these populations are still poorly understood yet they could provide relevant information for developing appropriate therapeutic approaches.

Efforts to prevent NDD in high-risk populations, such as premature neonates, highlighted the high prevalence of dysexecutive syndromes and attention impairments in these children [START_REF] Johnson | Preterm Birth and Childhood Psychiatric Disorders[END_REF][START_REF] Van De Weijer-Bergsma | Attention development in infants and preschool children born preterm: A review[END_REF]. Even in the absence of a diagnosed NDD, children who were born preterm tend to perform lower on attentional and executive tasks such as problem-solving, cognitive flexibility, inhibitory control, or processing speed tasks [START_REF] Hodel | Early executive function differences in infants born moderate-to-late preterm[END_REF]. Inhibitory control at 20 months predicts attentional abilities and school performance at 8 years of age and is particularly impaired in very premature babies [START_REF] Jaekel | Preterm Toddlers' Inhibitory Control Abilities Predict Attention Regulation and Academic Achievement at Age 8 Years[END_REF]. Studies using the ANT in children who were born preterm but were not diagnosed with NDD showed a significantly higher correlation between the orienting and executive networks [START_REF] Pizzo | Attentional networks efficiency in preterm children[END_REF], similar to what was reported in children diagnosed with ASD [START_REF] Keehn | Attentional networks in children and adolescents with autism spectrum disorder[END_REF]. [START_REF] Adams | Sensory processing in preterm preschoolers and its association with executive function[END_REF] found that in preschool children born prematurely, sensory symptoms measured using the Short version of Dunn's Sensory Profile were associated with lower working memory and inhibition in a battery of EF tasks and a parental hetero-questionnaire, the Behavior Rating Inventory of Executive Functions -Preschool version (BRIEF-P).

Cognitive training programs at preschool age have shown positive effects in children without pathology [START_REF] Rueda | Enhanced efficiency of the executive attention network after training in preschool children: Immediate changes and effects after two months[END_REF] and seem to alleviate the symptoms of children with ADHD [START_REF] Vibholm | Training, executive, attention and motor skills (TEAMS) training versus standard treatment for preschool children with attention deficit hyperactivity disorder: A randomised clinical trial[END_REF]. However, the long-term effects of these programs and their generalization to everyday situations are still controversial. For this reason, we need to better understand the early basis of atypical developmental trajectories, to target appropriate processes for remediation. Considering that NDD share common impairments in attention yielding overlapping executive symptoms, and that attention develops from sensory prediction processes of which somesthesis is the earliest foundation, can somatosensory processing be used as a marker of a patient's vulnerability to any NDD, and a target for early intervention?

PERSPECTIVES FOR EARLY MARKERS AND SCREENING TOOLS

In the second section, we described a developmental process leading from somatosensory prediction to multisensory prediction, to successive forms of attention of which the latest, EA, supports EF. In the third section, we described common impairments in EF across NDD and in a population at high risk of NDD, prematurely born children, and the link between EF deficits and EA deficits in these populations. The chronology we presented explains why screening tools focusing on NDD symptoms are ineffective in very young children: clinical symptoms build upon other deficits that are not syndrome-specific. In this section, we present efforts being made to provide better markers and early screening tools using a cross-syndromic approach, and we offer new perspectives on how to use tactile sensory processing as the earliest cognitive marker to detect a risk of NDD in vulnerable babies.

Assessing early executive functions and executive attention

For many years, researchers have proposed tools to measure EF in preschoolers, such as a modified Stroop with animals instead of words [START_REF] Wright | A new Stroop-like measure of inhibitory function development: Typical developmental trends[END_REF], and many of them are now part of standard evaluation batteries in clinical practice. Through new tasks suitable for very young children and the contribution of neuroimaging techniques, we are getting a better understanding of EF and their underlying brain substrates from the end of the first year of life until the preschool period, even before children can give verbal responses [START_REF] Fiske | Neural substrates of early executive function development[END_REF]. [START_REF] Holmboe | Freeze-Frame: A new infant inhibition task and its relation to frontal cortex tasks during infancy and early childhood[END_REF] developed the Freeze-Frame task, to assess inhibitory control during infancy and childhood. This visual inhibition task requires inhibiting peripheral visual distractors around a central animated stimulus. Performance on this task at 9 months predicts later success at 24 months of age. In continuity with this work, [START_REF] Holmboe | The early childhood inhibitory touchscreen task: A new measure of response inhibition in toddlerhood and across the lifespan[END_REF] proposed a new task to measure inhibitory control during toddlerhood, the early childhood inhibitory touchscreen task (ECITT). In this task presented on a touchscreen, participants have to press the button presenting a happy face, a rewarded location. Sometimes, the happy face is presented in the other location and it requires toddlers to inhibit the prepotent location to give the correct response. Through accuracy and reaction times in the different conditions, this task provides information about inhibitory control development and can also be used from around 9 months of age. There is currently no data on how sensitive these new EF tasks are to NDD risk. Future studies should aim at comparing EF emergence in toddlerhood with other markers of risk and long-term outcome.

The use of computerized play tasks is also a promising way to better understand the early stages of EA development by combining behavioral and cerebral measures in very young children [START_REF] Berger | Computerized games to study the development of attention in childhood[END_REF]. The ANT, designed to assess the efficiency of the three attention networks in adults, was adapted for children to better understand the early development of EA in particular from the age of 6 years [START_REF] Rueda | Development of attentional networks in childhood[END_REF]. The participant must determine the direction of a central stimulus (left or right) presented between distractors ("flankers") by pressing buttons on a keyboard. This task involves inhibiting the flankers' information when it is incongruent with the target, but other parameters such as valid or invalid spatial cues also allow the assessment of alerting and orienting attention. Using the ANT, [START_REF] Rueda | Enhanced efficiency of the executive attention network after training in preschool children: Immediate changes and effects after two months[END_REF] showed EA improvement in 5-year-olds after several sessions of computerized attention training. Recently, a version of the ANT was proposed for even younger children, from 3 years of age [START_REF] Casagrande | Assessing the three attentional networks in children from three to six years: A child-friendly version of the Attentional Network Test for Interaction[END_REF]. In this version, the target information is not the direction of the central animal but its color, flankers having opposite colors. It will be interesting to see in the future how this new EA assessment applies to NDD screening and relates to other aspects of development, notably sensory processing.

Tactile sensory processing as an early marker of EA deficit and NDD vulnerability

Developmental cognitive research has recently focused on the theoretical framework of predictive coding (see Box) to explain NDD symptomatology. Authors proposed for example that impaired prediction abilities may be at the core of ASD, explaining repetitive behaviors, planning deficits, and insistence on sameness [START_REF] Palmer | Bayesian approaches to autism: Towards volatility, action, and behavior[END_REF][START_REF] Sinha | Autism as a disorder of prediction[END_REF][START_REF] Van De Cruys | Precise minds in uncertain worlds: Predictive coding in autism[END_REF]. Children with ASD would have more difficulty adjusting to their environment and perceiving its regularities, and symptoms would reflect attempts to control or compensate for an unpredictable environment. Using a bimodal auditory-visual paradigm and functional brain imaging, [START_REF] Emberson | Deficits in Top-Down Sensory Prediction in Infants At Risk due to Premature Birth[END_REF] showed that 6-months old infants who were born preterm showed a reduced occipital response to predicted visual stimuli, suggesting that prediction impairment may be an early marker of high risk of NDD. As mentioned in section 3.1, [START_REF] Gonzalez-Gadea | Predictive coding in autism spectrum disorder and attention deficit hyperactivity disorder[END_REF] observed opposed patterns of response enhancement in the brains of ASD and ADHD patients based on stimulus predictability. The nature of prediction deficits in NDD is debated [START_REF] Van De Cruys | Precise minds in uncertain worlds: Predictive coding in autism[END_REF] and beyond the scope of this article, but authors agree on the importance of sensory prediction for the development of attention, and the feedback role that attention plays in regulating sensory processing once the child transitions from externally directed attention towards endogenous, i.e. executive, attention. In babies, when a stimulus is perceptually salient, it attracts attention regardless of relevance. As EA comes into play, when a task requires sustained attention to a stimulus, it is made salient by internal top-down processes and the response is enhanced, whereas when attention is not required the response is dampened [START_REF] Kok | Attention reverses the effect of prediction in silencing sensory signals[END_REF]. Early in life, sensory processing scaffolds the development of attention through predictive mechanisms, and later, attention in turn regulates sensory processing.

Since the earliest sensory modality is tactile, and because tactile processing is specifically affected in NDD and preterm neonates, the time has come to search for early somatosensory processing markers of neurodevelopment. Rational and positive tactile stimulation is increasingly considered in neonatal intensive care units as a tool that provides many benefits to vulnerable neonates: improved physiological constants, shorter hospital stays, faster weight gain, and more favorable overall motor and cognitive outcomes [START_REF] Field | Massage therapy by parents improves early growth and development[END_REF][START_REF] Ardiel | The importance of touch in development[END_REF]. For example, multiple "therapeutic touch" interventions are an essential component of the widespread Individualized Developmental Care Assessment Program (NIDCAP) [START_REF] Diego | Procedural Pain Heart Rate Responses in Massaged Preterm Infants[END_REF]. These approaches do not specifically target one type of processing in particular, although they probably address the need for predictability implicitly: massage and caresses are naturally rhythmic interactions.

Recent studies focused more directly on the question of somatosensory prediction and regulation of somatosensory processing based on temporal parameters in premature neonates. [START_REF] Dumont | The manual orienting response habituation to repeated tactile stimuli in preterm neonates: Discrimination of stimulus locations and interstimulus intervals[END_REF] showed that neonates born more premature and with smaller birth weight, and therefore more at risk of NDD, had less efficient habituation to tactile stimuli at 35 weeks of corrected gestational age. In a follow-up study, they showed that somatosensory cortex activation is modulated according to the temporal regularity of the stimulation sequence as early as 33 weeks of gestational age [START_REF] Dumont | Somatosensory prediction in the premature neonate brain[END_REF]. This suggests that atypical tactile sensory regulation could allow us to identify neonates at risk of an atypical developmental trajectory and perhaps deploy very early sensory intervention methods. We will need to examine the link between somatosensory prediction in premature neonates and outcome, in both diagnosed and not diagnosed children, following a dimensional approach of EA and EF measurement, and a cross-syndromic approach of NDD.

We are still lacking studies addressing the link between early somatosensory processing and attention, but [START_REF] Weiss | Neural measures of anticipatory bodily attention in children: Relations with executive function[END_REF] showed that brain anticipatory activity of school-aged children (6-8 years) during a somatosensory selective attention task correlated with their performance at EF evaluations. This is highly encouraging and needs further investigation, especially at preschool age when EA is rapidly developing. It will also be important to examine this relationship in children with various NDD, not only ASD and ADHD but also developmental coordination and learning disorders for example. Providing a better understanding of the role of tactile sensory processing from the earliest age in relation to the development of EA, EF, motor control, and NDD diagnosis may make it possible to propose early screening and determine better intervention programs for all children.

Sidebar title: Predictive coding theory

The predictive coding theory is a theoretical framework of cognition based on the Bayesian theorem, aiming to explain how sensory inputs are processed and more generally how the brain works as inherently predictive [START_REF] Friston | A theory of cortical responses[END_REF]. It postulates that the human brain constantly compares new sensory stimuli from primary cortices with internal models built by higher-order cortices from previous sensory inputs. If the input matches the prediction, it stabilizes the mental model of the stimulus by learning its characteristics, otherwise, a prediction error is fed back to update the probabilistic model. This theory provides a basis to understand sensory prediction and repetition suppression (the suppression of the cerebral response to a repeated, innocuous stimulus). Repetition suppression is the consequence of the internal stability of the model [START_REF] Grill-Spector | Repetition and the brain: Neural models of stimulus-specific effects[END_REF]. Neural responses to stimuli can be enhanced or attenuated depending on their predictability and relevance to the subject's needs.

Conclusion

We reviewed knowledge on the place of somatosensory processing as the foundation of multisensory cognition, and its links with attention, executive attention in particular, and executive functions. We emphasized the developmental sequence that they form during typical child development, and detailed how neurodevelopmental disorders are associated with atypical or impaired performance at every step of this sequence, leading to clinical syndromes years later. We presented the emergence of new research on preschoolers' EF and EA assessment, and on the role of somatosensory processing, in particular prediction, as a precursor of attention. Finally, we proposed avenues of work to complete the picture with the links between somatosensory processing, early attention development, and cognitive outcome in both typically and atypically developing children. We propose that abnormal tactile sensory processing at an early age could provide markers of executive attention dysfunctions, which may promote new early screening programs for attention disorders and NDD. Research in these domains remains scarce and needs to be further explored, particularly in the preschool period, when EA is rapidly growing and is strongly required for core academic learning.

Funding Information

This work is supported by a PhD grant to Marie Anquetil by the region Normandie council (France).