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Socio-emotional Learning: How do We Learn in Connection with Others?

Mathieu CASSOTTI

LaPsyDÉ, CNRS, Université de Paris, Institut Universitaire, France

When it comes to solving a puzzle, you will probably turn to a strategy of trying to put together pieces that have similar colors, grouping them according to the areas you have previously identified on the model, trying to put aside those that have a straight edge in order to build the outline of the image, determining which sub-parts you will start with and try to identify the elements that fit together perfectly, etc.. These are all structured actions that will allow you to solve the puzzle with varying degrees of ease. When a young child is faced with a similar exercise for the first time, will they, like you, spontaneously implement this type of strategy? It is unlikely! They will certainly try to group randomly any pieces together without even worrying about whether they fit together properly or not. At worst, you just have to force it and it will eventually fit! So how did you develop this remarkable ability to solve problems? Probably not just by yourself, by learning from your mistakes and the actions you took on the puzzle pieces. Social interaction, and especially adult help, probably helped you learn how to solve this type of problem. Not only by imparting knowledge, but also and above all by structuring your thinking. Thus, another person will not only explain to you the rules of construction of a puzzle, they will also regulate your way of thinking when faced with this type of problem. They will, for example, help you to resist that first failed strategy of using force to fit the pieces together against their will. This example illustrates not only how important it can be to resist an immediate inappropriate strategy, but also how the social context can help the child learn to do so. Therefore, the purpose of this chapter will be to improve understanding of the role of social context and social interaction in the development of this form of self-regulation that developmentalists have named inhibitory control.

6.1. Inhibitory control in developmental psychology and the role of social context

The ability to inhibit automatisms or inappropriate responses is a central control and regulatory process in the cognitive and socio-emotional development of a child and adolescent (Borst et al. 2015; Houdé 2017, 2019, 2020). Indeed, developmental studies emphasize that inhibitory control (or cognitive control) is a fundamental process in the domains of number (Houdé and Guichart 2001), reasoning (Moutier et al. 2006; Cassotti and Moutier 2010), decision-making (Cassotti et al. 2012, 2014; Osmont et al. 2017) or creativity (Cassotti et al. 2016; Camarda et al. 2018). More generally, cognitive control plays a critical role not only in academic success but also in everyday life (Diamond 2013). Indeed, cognitive control abilities are better predictors of academic success, whether at school or university (Diamond and Ling 2016), than IQ or socio-economic status (Moffitt et al. 2011; Diamond and Ling 2016).

Since individuals are rarely alone in solving a problem, whether in the classroom, at school, or later in the workplace, it seems essential to first identify and understand how inhibitory control can be influenced by the social context.

6.1.1. Social context, a facilitator of inhibitory control?

Doing a complicated mathematics exercise alone is difficult enough for a child or teenager. But doing it under the evaluative pressure of a teacher or classmates at the board can quickly become almost impossible. However, if the social context changes, the exercise remains strictly the same. Social psychology studies have systematically shown that the social context (presence of peers, evaluative pressure, etc.), far from being trivial, has considerable effects on individual performance in multiple domains (Zajonc 1965; Belletier et al. 2019). What about inhibitory control?

In order to account for the influence of social presence on inhibitory control and, in particular, to explain why, in certain situations, a social facilitation effect appears, while in others the social presence proves to be deleterious for cognitive performance (Zajonc 1965; Belletier et al. 2019), an attentional hypothesis has been proposed and has been the subject of numerous works in social psychology (Baron 1986; Huguet et al. 1999). In this model, the presence of others constitutes a source of distraction that will constrain the way in which the individual will focus their attention on the task they must perform (Baron 1986). Indeed, others represent a source of social comparison, which makes it possible for individuals to measure the adequacy of their performance with that of others or with the teacher’s expectations, for example (Baron 1986). This is true not only in coaction situations (i.e. when the individual performs a task at the same time as others), but also in social evaluation situations, in which the individual looks to the reactions of others as a clue to their level of performance (Sanders et al. 1978).

From Baron’s perspective, the presence of others leads to an attentional conflict, since the participant is unable to focus attention both on the task at hand and on the reactions of others. The allocation of attentional resources between the task to be performed and the social presence leads to a modulation of performances. More specifically, this attentional conflict reduces attentional focus, as the participant seeks to avoid the distracting effects of the presence of others. In this context, social presence could have a facilitating effect when the task at hand involves focusing attention on central stimuli and not on peripheral elements. From this attentional perspective, it becomes possible to better understand how the social context can have a facilitating effect on inhibitory control. The experimental arguments for this model come mainly from studies on the influence of social context on the Stroop task (Belletier et al. 2019).

Box 6.1. The Stroop test (1935)

When the social presence is attentive or involves evaluation, the results confirm that interference in the Stroop test is lower than when individuals are alone to perform the task (Huguet et al. 1999; Belletier et al. 2019). These data have since been confirmed by other studies, which report greater attentional focus during stressful situations (Chajut and Algom 2003). In a recent study, social facilitation effects were found in the Stroop test in the presence of a humanoid robot (Spatola et al. 2018). However, it should be noted that this facilitation effect was only observed when the humanoid robot was unpleasant and threatening.

Some authors suggest that adolescents are more sensitive to the social evaluation of their peers induced by their presence than adults are (Dumontheil et al. 2016). This anticipation of social evaluation, which generates apprehension in adolescents, causes a greater attentional distraction than in older individuals (Blakemore 2018). This hypothesis has since been supported by studies on the effect of social observation in verbal reasoning (Dumontheil et al. 2016) and more directly on inhibitory control (Bouhours et al. 2021). Indeed, the latter study suggests that social evaluation by a peer or adult causes a greater social facilitation effect on inhibitory control in adolescents than in young adults.

6.1.2. Social context and inhibitory control: the decision-making paradox

Data obtained in social psychology attesting to the influence of social pre-presence on inhibitory control seem to contradict recent studies conducted in the developmental neuroscience field of risk-taking (Blakemore and Robbins 2012; Blakemore 2018). Indeed, several research studies suggest that the presence of peers does not directly influence adolescents’ inhibitory control (Chein et al. 2011; Smith et al. 2018).

For Steinberg (2008), the fact that increased risk-taking in adolescence is not systematically found in laboratory situations is not surprising, insofar as the participants are placed in weak socio-emotional contexts (Steinberg 2008). In other words, this author suggests that adolescents may decide as effectively as adults, but only under conditions where the influence of socio-emotional factors is absent or minimized. Risk-taking in adolescence is thus be influenced by the presence of peers or circumstances of emotional arousal, which classical laboratory tests do not induce. Steinberg’s view therefore remains very close to that of Casey’s (Casey et al. 2008, see Chapter 5) and mainly emphasizes the need to take into account the influence of the social context on emotional sensitivity.

The hypothesis that the presence of peers plays a decisive role in risk-taking was confirmed using a driving simulation task in which adolescents could participate alone or in groups (Gardner and Steinberg 2005). The results show that adolescents were as safe as adults when they solved the task individually. Conversely, when their friends were present, these same adolescents took more risks than adults. Furthermore, the influence of peer presence on risk-taking tended to decrease with age (Steinberg and Monahan 2007). To determine whether the presence of peers increases adolescents’ emotional reactivity or decreases cognitive control abilities, Steinberg’s team conducted an initial neuroimaging study (Chein et al. 2011). Their reasoning was that if the presence of peers increases emotional reactivity, then brain activity in regions involved in emotional processing, such as the nucleus accumbens, should be modulated by the socio-emotional context. Conversely, if this social context reduces cognitive control, then activation of prefrontal regions should be lower in adolescents when they are in the presence of their peers. Neuroimaging data confirm that the presence of peers selectively impacts brain activations of the emotional system, suggesting that this social context increases adolescents’ sensitivity to immediate rewards but has no effect on cognitive control per se. Indeed, this interpretation has since been supported using tasks involving reward delays (O’Brien et al. 2011; Weigard et al. 2014). It should be added that the social context seems to increase adolescents’ risk-taking, not only in situations of ambiguity, but also in those where they nevertheless have explicit information about the risk level of options (Smith et al. 2014). But then, how can we explain that, in these types of situations, social presence does not influence cognitive control when previous studies in social psychology have clearly shown the opposite?

6.1.3. Limitations of the neurodevelopmental approach

One limitation of the studies by Chein et al. (2011) and Steinberg (2008) is that they consider the role of social context only in terms of its effects on adolescents’ sensitivity to immediate rewards. It seems clear, however, that adolescents’ social representations of potentially group-valued behaviors can have an effect on behavior and performance, beyond the stimulation from sensitivity to immediate rewards. For example, adolescents may be more likely to conform to the norms of their peer group when making decisions that involve risk. In this context, social conformity, defined as “the modification of beliefs or behaviours by which an individual responds to various types of pressure by seeking to conform to ambient norms through the adoption of socially approved behaviours” (Fischer 2010, p. 74), could account for some of the social effects observed in adolescent decision-making and constitute a trigger for a form of self-regulation.

The most compelling experimental arguments for this hypothesis come from the study of the effects of social conformity on drinking in adolescence. Teunissen et al. (2014); Teunissen et al. (2016), for example, examined the extent to which peer norms for drinking could influence the prototype of an alcohol consumer that adolescents form and in turn change their willingness to drink. They were able to show that social interventions aimed at conveying an anti-alcohol norm led adolescents to feel more negatively about heavy drinkers than when they were exposed to a pro-alcohol norm. In a second study, Teunissen et al. (2012) sought to understand more specifically how adolescents adapted their drinking disposition when exposed to a pro- or anti-alcohol norm conveyed by peers during a social network conversation. After a conversation phase in which adolescents were confronted with an anti- or pro-alcohol norm conveyed by fictitious peers, hypothetical scenarios were proposed to them in order to assess their propensity to drink. The data obtained show that adolescents modified their willingness to drink alcohol according to the norm conveyed by the fictional peers. While a pro-alcohol norm triggered a greater willingness to drink, an anti-alcohol norm produced exactly the opposite result. These results therefore suggest that the desire to conform to a social norm may lead adolescents to up- or down-regulate their risk-taking.

Although this body of research improves our understanding of the effects of social context on children’s and adolescents’ cognitive performance, it should be emphasized that the social environment is not merely a source of distraction or a reference standard to which individuals seek to conform. Indeed, while children and adolescents appear to be influenced by the mere presence of others, this presence can also be a source of learning and a driving force for child and adolescent development.

6.2. Social learning

How can we consider the role of the social context in the development of cognitive control without referring to the richness of Lev Vygotsky’s theory (1985)? This theory is indeed an indispensable resource for understanding the place of the social context in the construction of the child’s intelligence. Let us specify from the outset that the purpose of this section is not to propose a complete description of the theory, but rather to emphasize the fundamental concepts that are now being used to reinforce cognitive control in schools (Diamond and Lee 2011).

6.2.1. Development of higher psychological functions

For Vygotsky (1985), social interaction is the driving force behind the development of higher psychological functions. The latter corresponds to a cognitive architecture ranging from focused attention and voluntary memory to logical reasoning. According to him, these higher psychological functions are based on the use of a set of cognitive and cultural tools that must be appropriated during ontogenesis. Among these tools, language has a privileged place in Vygotsky’s approach, since it is not only a cultural tool – insofar as it is created and shared by the members of a specific culture – but also a cognitive tool, in the sense that it will allow reasoning and the regulation of thought. Therefore, language will not only serve as a medium to transmit content, but also to facilitate abstract reasoning, imagination and creativity. Language is as critical in the appropriation of new cognitive and cultural tools as it is in the context of social interaction per se.

Higher mental functions cannot be formed without the constructive contribution of social interactions, which make the transmission of cognitive tools possible. It is therefore essentially in the context of asymmetrical interactions (with a more competent peer or an adult) that children will learn to use these cognitive and cultural tools. The instructions of the adult (or of a more competent peer) will gradually be internalized and the regulation of the initially interpersonal thought will then become a form of self-regulation. This internalization process is one of the keys to the general law of development proposed by Vygotsky (1985), according to which:

Any function in the cultural development of the child appears twice or on two planes: first it appears on the social plane, between people, as an inter-psychological category, and then on the psychological plane in the child as an intra-psychological category.

In other words, the adult (or the more competent peer) is the medium allowing the child to progressively internalize the cognitive tools of their culture, and consequently the development of higher mental functions. It is therefore within the framework of these social interactions that the adult will encourage the development of the child’s self-regulation of thought. Even if Vygotsky does not make explicit reference to executive functions or cognitive control as they were later theorized by authors such as Houdé (2020), there is a striking proximity between these theoretical concepts and the process of self-regulation of thought developed in the Vygotskian approach.

6.2.2. Regulation of thought and egocentric language

If for Piaget egocentric language has no particular function and was only the manifestation of the egocentric character of the child’s thinking, for him egocentric speech is a specific and autonomous form of language. According to Vygotsky, egocentric language does have a function, that of regulating the child’s thinking when confronted with a difficulty. Indeed, egocentric language allows for supporting the child’s thinking and takes the form of inner language in the adult. Moreover, even in adults, when confronted with a thorny issue, it is not uncommon to see a verbalization of thought emerge out loud. Adults, like children under the age of 8 years, use language to express their thoughts and to accompany the activity in order to develop a plan to solve a problem. While in the adult this language corresponds, most of the time, to a structured inner language, in the child it will take the form of egocentric language, which is more unstructured, and which will gradually become structured and internalized. In order to study egocentric language, Vygotsky (1985) proposed activities to children in which difficulties were introduced with the aim of disturbing the progress of the activity being carried out. Thus, he noted that when the child was confronted with a difficulty, egocentric language not only increased considerably but also served to structure the child’s actions to overcome the difficulty.

6.2.3. Social interaction and zone of proximal development

The strength of Vygotsky’s theory from the point of view of the teacher is that it does not focus solely on the child’s current level but directs attention to their potential level. It is in this context that the concept of the zone of proximal development emerged, which emphasizes the essential role of guidance by another (an adult, a more competent peer). This is the distance between the child’s current level (i.e. their ability to solve a given problem on their own) and the level of potential development (i.e. the ability to solve a problem under the guidance of an adult or in cooperation with more capable peers). More specifically, actual development corresponds to what the child has already mastered on their own, what they are capable of implementing autonomously when faced with a problem. The upper limit of the zone of proximal development, on the other hand, indicates the next stage of development. This is what they can do today with the help of others (interpersonal regulation) and what they will eventually achieve alone (intrapersonal regulation, in particular by mobilizing egocentric or inner language) at the end of the process of internalizing the cognitive and cultural tools used by the adult during learning.

6.3. Stimulating self-regulation of behavior through social interaction

6.3.1. The example of the Tools of the Mind program

If, as Vygotsky proposes, social interactions play a fundamental role in the regulation of thought and behavior, is it possible to design specific training programs on this basis to enhance the efficiency of cognitive control in children? This question has been the subject of a rigorous experimental study in preschool children in ecological settings (Diamond and Lee 2011; Diamond and Ling 2016). In this research, the authors used an educational program based on Vygotsky’s theory, Tools of the Mind, and developed by educational psychologists to strengthen children’s cognitive control directly in the classroom. It was therefore the teachers themselves, after specific training, who used the techniques and tools that the psychologists had designed to promote the development of cognitive control in the usual classroom activities. From then on, whatever the subject studied, mathematics or language skills for example, the teachers had at their disposal a set of tools aimed at stimulating inter- and intrapersonal regulation.

Since, according to Vygotsky, the regulation of thought and behavior takes place primarily at the interpersonal level, in the context of social interaction, the authors not only introduced mediators to facilitate the focusing of children’s attention, but also encouraged the development of egocentric regulatory language. This approach may seem surprising since, in most classrooms, it is customary to ask children to be quiet when doing an activity so as not to disturb their peers. Here, instead, they were able to talk and interact with their classmates to help them complete an activity. Did this mean that the class became a constant hubbub? No, because students also had tools to mediate and regulate speaking and social interaction in these activities. For example, they were given a small card symbolizing an ear when they had to listen to a classmate who had another small card symbolizing speaking. When the roles were reversed, they also exchanged the cards. Thus, the use of media encouraged the taking of the floor and the need to inhibit oneself so as not to react when it was not yet the child’s turn to speak. The latter then had a concrete external aid to regulate their intuitive reactions, such as speaking when it was not their turn.

Still based on Vygotsky’s approach, the authors promoted social interactions between children by reinforcing the control of actions by others. For example, a child could control the addition of numbers by another child and check the result once the calculation was completed. Teachers also encouraged the use of egocentric language and helped children to structure it. Thus, when children had to perform complex tasks, they could verbalize the sequence of actions to be performed. Using this egocentric language to regulate their thinking and the actions they needed to perform to solve a problem was intended to help them plan and apply particular strategies.

Beyond the originality of this pedagogical approach, the scientific interest of this research comes essentially from the evaluation of the cognitive effects caused in the children by implementing this program. This evaluation is based on the comparison of the performance, in laboratory tasks, of children who benefited from the program described above for several weeks with a control group of children who followed a more traditional program. It should be noted at the outset that the laboratory tasks used in this assessment were new to the children and had never been seen in the classroom. They were short, game-like tasks that are classic measures of cognitive control and inhibition in particular. The comparison between the two groups of children shows that, whatever the task measuring cognitive control considered, those in the group having benefited from the program based on a Vygotskian approach showed better performance on average than the control group. This means not only that this program is effective in stimulating cognitive control in preschool children, but also that the self-regulation skills trained in the classroom on school activities can be transferred to other new situations. In other words, the fact that a phenomenon of far transfer (i.e. from classroom activities to new tasks in the laboratory) is observed underlines the effectiveness of this type of program and confirms the relevance of Vygotsky’s approach for the teacher who wishes to strengthen children’s cognitive control.

6.3.2. Executive learning and overcoming reasoning biases

Is social learning only effective in children? Can interpersonal regulation of thought also be observed in logical reasoning? To answer these questions, we will explore in this new section not only the limits of our reasoning abilities but also show how a learning procedure aiming at reinforcing inhibitory control in the context of a tutorial interaction can help to overcome them in young adults.

6.3.2.1. Inhibitory control and reasoning bias

In an extensive program of research, Tversky and Kahneman (1974, 2004) further examined the quality of human probability judgment. In particular, they described a limited number of information processing heuristics, the most famous of which are representativeness heuristics and availability heuristics. Using many original paradigms, they have clearly shown that the application of these heuristics can lead, under certain circumstances, to judgments that do not conform to the rules for calculating probabilities. These errors are called reasoning biases, insofar as they are systematic.

Box 6.2. Illustration of the problem used to highlight the conjunction fallacy

Representativeness heuristics consist of basing a judgment of the probability of an individual’s membership to a category on psychological, individualistic information that corresponds to the stereotype of the category (Kahneman and Tversky 1972; Kahneman et al. 1982). In fact, individuals base their judgment more on the description of the event than on its probability. In other words, Kahneman and Tversky (1972) have shown that in situations where we have both numerical and psychological information, we have a strong tendency to neglect the objective data. This result is worrisome when one imagines its impact in a court of law. What happens to someone who has all the characteristics of the ideal culprit? This is all the more disturbing because the group solution of the problem we have just outlined led to an increase in responses based on the degree of similarity with the personal stereotype and the description of the character.

Moreover, the use of these heuristics sometimes leads to the transgression of certain elementary rules of probability. The conjunction fallacy (Kahneman 2003) is certainly the most demonstrative example.

The authors were primarily interested in ranking the following three options in their analysis: Linda now works at a bank counter (A); Linda is actively involved in a feminist movement (B); and Linda works at a bank counter and is actively involved in a feminist movement (A and B). According to the conjunction fallacy, the probability of conjunction of two events (A and B) cannot be greater than the probability of one of the two constituent events (A or B and hence P(A and B) ≤ P(A) since (A and B) is a subset of A). Therefore, in the “Linda” problem, the conjunction of the two states “bank employee and feminist” can in no way be more likely than one of them. However, the vast majority of subjects (over 85%) considered the probability of the conjunction of the two events “bank employee and feminist” to be more likely than the probability of only one of the two constituent events, “bank employee”. They therefore committed the conjunction fallacy, thus transgressing a fundamental principle of probability. Moreover, this deviation was observed regardless of the level of statistical competence of the individuals (the results even show that “semi-informed” subjects made more errors than “lay” subjects). For the authors, this resulted from the fact that the characteristics “feminist” was very representative of the description given of Linda. In other words, Linda was judged as a feminist bank employee insofar as, in addition to being a bank employee, she had the “stereotypical” characteristics of a feminist (single, assertive, etc.). The subjects made a similarity judgment rather than a probability judgment (Kahneman 2003). It should be noted in this regard that their probability estimates are strongly correlated with those made on the basis of similarity when the instruction explicitly calls for this.

In order to account for the many cognitive biases that have been identified in the area of uncertainty judgment and decision-making, some authors have postulated the existence of two distinct systems. These so-called dual process theories have in common that they oppose a form of intuitive and heuristic reasoning, called system 1, to a form of executive and analytical mental operation, called system 2. In children, adolescents and adults, the most recent research emphasizes the competitive relationship between these two forms of mental operations (Houdé 2020). Indeed, it appears that the tests that give rise to biases are in fact trap situations that induce a cognitive competition between, on the one hand, the subject’s logical competence (system 2) and, on the other hand, an intuitive bias resulting from a highly automated strategy (system 1). Under the impetus of Olivier Houdé’s work, as early as 1995, it was proposed to integrate into these models an executive process allowing for resolving these situations of conflict between the two systems: cognitive inhibition (Houdé 2020). In fact, according to him, the wanderings of reason observed in the domain of deductive reasoning – and also in that of probability judgment and decision-making – do not result from a lack of logical skills per se but rather from a difficulty in blocking or resisting intuitive responses automatically generated by system 1 (see for an overview Houdé and Borst (2014) and Houdé (2020)). Hence, at any point in development, one can distinguish the existence and strength of activation of intuitive and heuristic (system 1) or deliberative and analytic (system 2) strategies whose expression is governed by an inhibition process (system 3).

6.3.2.2. Learning to resist bias in the tutelage interaction

How can we redirect children, adolescents and adults toward logic? How do we make them aware of their reasoning biases and teach them the metacognitive skills that will enable them to resist them? Using an experimental executive learning procedure, Moutier and Houdé (2003) sought to answer these questions. This procedure is based on three key elements:

  • A tutorial interaction. Indeed, the learning procedure is based on the participant’s interaction with the experimenter, who transmitted to them “cognitive tools” allowing them to regulate their thinking in this type of situation.
  • The presence of executive alarms. These alarms sought to alert the participant to the danger of focusing too much on certain intuitive solutions. These verbal instructions thus had a dual executive and emotional component.
  • The symbolic materialization of the inhibition process through the use of a medium. The participants were thus invited to put the intuitive solutions under a hatched transparency in order to represent the inhibition of the intuitive strategy (Box 6.3).

In this perspective, Moutier and Houdé’s (2003) executive learning procedure aimed to provide individuals with the metacognitive regulation skills that would allow them to detect that their first intuitive response was biased and inhibited it by being put to the side. This learning, a real cognitive and cultural tool for overcoming a reasoning bias, is supposed to be progressively internalized and then reused autonomously afterward. The results indicate a specific effect of executive learning confirming, on the one hand, the key role of cognitive control in accessing logic, and, on the other hand, the possibility of transferring a metacognitive procedure of resistance to a bias, through a tutorial interaction (Cassotti and Moutier 2010; Houdé 2020).

Box 6.3. Details of the executive learning process from Moutier and Houdé (2003)

6.3.2.3. Neural bases of executive learning

Following this work, which was essentially based on behavioral data, a functional neuroimaging study has, for the first time, made it possible to examine the cerebral bases of the transgression of an elementary logic rule, as well as the neural impact of an executive learning procedure (Houdé et al. 2000). Houdé et al. (2000) replicated their learning paradigm in a neuroimaging study. In this research, participants performed the Evans conditional rule refutation task, which elicits a deductive reasoning bias, as a pre-test. They were then given an executive learning phase very similar to the one described above but adapted to the deductive reasoning task, based on the examination of the Wason task. For the post-test, individuals were again confronted with the Evans task (see Box 6.4). The behavioral results confirm the data obtained by previous cognitive psychology research (Moutier et al. 2002).

The performance of the subjects in the pre-test is characterized by a massive failure (more than 90% of the subjects were wrong). On the other hand, during the post-test, after the learning procedure, the subjects proposed a correct answer in accordance with the logical truth table. In terms of the level of brain activity, we notice a reconfiguration of the neural networks between the pre-test and the post-test. If, before learning, posterior regions are mainly involved in perceptual processing (occipital and parietal regions) that underlie the matching bias, after learning the activity switches to an anterior prefrontal network involved in logical information manipulation, error detection and regulatory inner language (Houdé 2020).

A second neuroimaging study (Houdé et al. 2001, 2003) highlighted the brain regions specifically activated by the executive component of the learning procedure. To this end, the same authors contrasted the neural impact of strictly logical learning (described as “cold”, i.e. without executive-emotional alarms) with that of inhibition learning (“hot”, i.e. with executive-emotional alarms). The results confirm that frontal activities are related to metacognitive alerts in executive learning. More interestingly, the most activated region in all subjects who were able to correct the matching bias in the post-test was the right ventromedial prefrontal cortex. The authors link this activation to the emotional aspects of the executive component (executive alarms experienced during the learning phase and reactivated during the post-test).

Box 6.4. Biases in deductive reasoning

6.4. Conclusion

This chapter has presented research findings that illustrate the need to consider the social context and interactions with others in child and adolescent development. Whether at home with a puzzle or at school with a math problem, children and adolescents must learn to resist immediate strategies that prove inappropriate. Social interaction plays a critical role in this learning process in order to promote the development of self-regulation of thought. These results naturally challenge the developmental psychology researcher, since they show that a child facing a problem alone will not react in the same way as in a situation of social presence or evaluation. More interestingly, the social context does not produce the same effects depending on the age and the task to be performed. Beyond simple presence, this work also questions the teacher since it is possible to design learning procedures specifically aimed at reinforcing self-regulation in children and adolescents. This work thus offers new ways of thinking about child development, not exclusively from a cognitive angle but by integrating a social dimension, whether by questioning the role of the social context or the potential effect of direct intervention. These observations are not new, and the work of Vygotsky and his successors is remarkable in terms of a more integrative approach to child development. There is no doubt that recent research on adolescence in cognitive neuroscience and the discovery of a specificity of sensitivity to the social context at this age will greatly contribute to putting this social dimension of development even more at the heart of the debates in developmental psychology.

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