# From social inhibition in childhood to social facilitation in adulthood: How social presence systematically enhances the predominant response strategy in cognitive tasks

Leslie Tricoche, Amélie Reynaud, Denis Pélisson, Jérôme Prado, Martine Meunier

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## To cite this version:

Leslie Tricoche, Amélie Reynaud, Denis Pélisson, Jérôme Prado, Martine Meunier. From social inhibition in childhood to social facilitation in adulthood: How social presence systematically enhances the predominant response strategy in cognitive tasks. Journal of Experimental Child Psychology, In press. hal-05144973

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Submitted on 4 Jul 2025

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From social inhibition in childhood to social facilitation in adulthood: How social presence

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systematically enhances the predominant response strategy in cognitive tasks.

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Leslie Tricoche<sup>1,2,*</sup>, Amélie Reynaud<sup>1</sup>, Denis Pélisson<sup>1</sup>, Jérôme Prado<sup>3</sup> & Martine Meunier<sup>1,°</sup>

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¹ IMPACT Team, Lyon Neuroscience Research Center, INSERM, U1028, CNRS, UMR5292, University

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of Lyon, France

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² Moral & Social Brain Lab, Department of Experimental Psychology, Ghent University, Belgium

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³ EDUWELL Team, Lyon Neuroscience Research Center, INSERM, U1028, CNRS, UMR5292, University

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of Lyon, France

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° Deceased on March 2, 2024

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* Corresponding author: leslie.tricoche@ugent.be

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Research Highlights

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* We used recognized cognitive tasks related to education aiming to replicate and expand earlier findings on age-related changes in social facilitation and inhibition (SFI). This age-inclusive study investigated developmental trajectory of SFI effects, encompassing children, adolescents, and adults for a comprehensive perspective on social influences.

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24 • Innovative non-linear analyses were conducted to explore nuanced relationships between age and social presence, capturing complex patterns missed in traditional group-level studies.

27 • Reaction time distribution analyses revealed varied decision-making strategies, advancing understanding beyond the conventional view of "simple task = facilitation / complex task = inhibition."

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36 **Abstract**

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38 This study investigated the developmental trajectory of social facilitation and inhibition across 39 207 participants aged 8-22 in numerosity and phonological comparison tasks. Tested alone or 40 in coaction with a familiar age-matched partner, younger participants exhibited social 41 inhibition on reaction times (RT), while a shift toward facilitation occurred around ages 13- 42 14. Analysis of individual RT distributions revealed that social presence modulated 43 predominant response strategies: younger participants used slower, reactive strategies, while 44 older participants employed faster, anticipative ones. By enhancing the strategy 45 predominating in each age group, social presence led to social inhibition in children and early 46 adolescents, but social facilitation in mid-adolescents and adults. There was no influence of
47 the partner's presence on accuracy, suggesting that while social presence modulated this
48 predominant response, the presence of a partner did not impact the task performance in terms
49 of correct answers. Together, these findings challenge Zajonc's "dominant response" theory,
50 suggesting that social presence affects overall decision-making strategies.

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52 **Keywords:** developmental trajectory, response strategies, social influence, bimodal RT
distributions, coaction

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59 **Introduction**

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61 Social facilitation and inhibition (SFI) effect refers to the positive or negative influence of
62 someone else presence on our own performance. This phenomenon has been described across
63 various basic behaviors (e.g., moving, dressing or eating) and cognitive skills (e.g., perceptual
64 and attentional abilities, decision-making, memory, executive functions), and has been the
65 subject of extensive literature since the late 19th century (Baron et al., 1978; R. Zajonc, 1965;
66 R. B. Zajonc et al., 1969). While studies exploring human behavior in general—and the SFI
67 effect in particular—have historically focused on young adults, primarily university students,
68 there has been a growing recognition of the importance of studying younger populations. At
69 the time of their publication, classic references in the literature, such as Guerin's book (2010)
and Bond and Titus's meta-analysis (1983), indicated that children and adolescents were underrepresented among studies investigating the effect of social presence. Indeed, only 3 to 6% of published studies focused on this age group at the time (Bond & Titus, 1983; Guerin, 2010). However, since a decade or so, there has been a growing interest for studying effects of SFI on children and adolescents (Albert et al., 2013; Telzer et al., 2017; Tricoche & Caspar, 2024; van Hoorn et al., 2019). However, this has been specifically directed towards certain behaviors. For example, studies have investigated effects of SFI in contexts which are relevant to combat childhood obesity, such as food consumption (Bevelander, Lichtwarck-aschoff, et al., 2013; Bevelander, Meiselman, et al., 2013; Drewett, 2007; Engelmann et al., 2015; Lumeng & Hillman, 2007; Salvy et al., 2008) and active *versus* sedentary activities (Barkley et al., 2014; Coppinger et al., 2010; Gonzales et al., 2019; Kieffer, 1977; Rittenhouse et al., 2011; Salvy et al., 2009; Siegmund et al., 2014). Together, these studies showed that the presence of others can positively influence consumption habits and physical effort, even if different factors might affect these effects (e.g., type of peer, whether an individual is obese or not...). Other studies have focused on risky decision-making in adolescents to better understand deviant behaviors during this developmental stage (Chein et al., 2011; Gardner & Steinberg, 2005; Hoffmann et al., 2018; Reniers et al., 2017; Smith et al., 2018; Somerville et al., 2019; Telzer et al., 2015, 2017; van Hoorn et al., 2018). Overall, this literature indicates that peer influence is stronger in adolescents than in adults, leading to an increase in risk-taking decisions when others are present, regardless of whether the associated reward is monetary or not. At the neural level, this is reflected in heightened activity in brain regions associated with reward processing, particularly the ventral striatum, in the presence of peers (Chein et al., 2011). Interestingly, social effects on adolescents are observed across various types of social presence but appear to be more pronounced when the presence is that of a peer rather than a parent (van Hoorn et al., 2018). These social effects can even be opposite
depending on the type of presence. Indeed, Telzer et al. (2015) showed that the presence of a
mother reduced risk-taking and encouraged safer decision-making by decreasing the
rewarding nature of risky choices and enhancing activity in brain regions related to self-
control (Telzer et al., 2015). Together, these findings reflect the unique developmental stage
of adolescence, during which sensitivity to peers is particularly heightened. However, a recent
meta-analysis found a small size of the effect of peer observation—whether the peer was
familiar or not—on risk-taking decisions in adolescents, except when the peer was actively
engaged in the decision-making process and expressed a preference for risky choices (Powers
et al., 2022). Importantly, this synthesis focused on the SFI effects on choice outcomes (risky
or not) rather than on the decision-making process itself, including its underlying neural
networks. This distinction is particularly relevant to the present study, as the presence of a
partner may influence the cognitive processes leading to a decision (e.g., the time taken or the
strategy used to make the decision) without necessarily affecting the decision outcome or
overall performance (accuracy).

While the issues discussed above (i.e., childhood obesity, risk taking) have major societal
relevance, it is surprising that the effects of social influence on cognitive skills and academic
knowledge remain relatively understudied in children (Bouhours et al., 2021; Camarda et al.,
2021; Dumontheil et al., 2016; Tricoche et al., 2021; Tricoche, Pélisson, et al., 2023; Wolf et
al., 2015), given that children spend most of their daily lives at school where they learn
among peers and are heavily influenced by them.

One reason effects of SFI in the context of academic learning are understudied could be that
schooling covers a wide developmental timespan and that the developmental trajectory of SFI
effect remains largely unknown. To our knowledge, no study has compared children,
adolescents, and adults on a continuum to evaluate the development of this basic form of
social influence. Indeed, most studies have focused on a single population. Those having
compared two age groups (Bouhours et al., 2021; Camarda et al., 2021; Chein et al., 2011; Dumontheil et al., 2016; Gardner & Steinberg, 2005; Haddad et al., 2014; Rosen et al., 2016; Ross et al., 2016; Somerville et al., 2019; Tricoche, Pélisson, et al., 2023; Tricoche et al., 2021) have overall reported an increase of SFI in younger participants as compared to adults.

However, they assessed the SFI effect of cognitive functions in adolescents (or children: Rosen et al., 2016) compared to adults in many different tasks: risk-taking tasks (Chein et al., 2011; Gardner & Steinberg, 2005; Haddad et al., 2014; Ross et al., 2016; Somerville et al., 2019), problem-solving tasks (Bouhours et al., 2021; Camarda et al., 2021; Dumontheil et al., 2016) or numerosity and phonology tasks (Tricoche et al., 2021; Tricoche et al., 2023). Further, these studies widely differed by the types of partner (familiar vs peer vs expert) or partner relationships (simple presence/observation vs coaction vs evaluation/judgement) or by the affective levels engaged (affective arousal). Therefore, the current body of empirical knowledge regarding the effect of age on SFI remains quite restricted and un-conclusive, as well as lacks investigations of the whole developmental trajectory. It is thus crucial to determine how and to what extent SFI effect evolves with age.

In the present study we explored the entire developmental trajectory of effects of SFI on participants from the age of 8 to the age of 22, using two tasks that are relevant in the context of academic learning and require participants to compare the numerosity of sets of dots and the phonology of words (stimuli were sequentially presented in both cases, see Figure 1). In the numerosity comparison task, participants had to decide which set contained the larger number of dots, and in the phonological comparison task, they had to decide whether the two words rhymed or not. Participants performed these tasks alone or in a coaction situation (i.e., both participants doing the same task at the same time) with a familiar age-matched partner (i.e., friend, sibling, life partner or close colleague). A main goal of the present study was to extend the developmental findings from a previous study conducted in 8- to 10-year-olds and
145 young adults. In that study, similar numerosity and phonological tasks were used and showed
146 a social facilitation in both tasks, though the effect was stronger in children than in adults
147 (Tricoche et al., 2021). Here we proposed to recruit an additional group of participants
148 (adolescents 11- to 19-year-old) and used a complementary analysis method to identify the
149 developmental changes of performance and processes modulated by social presence.

150 Specifically, in the present study we investigated the strategy adopted by participants to make
151 their decision in these tasks, both of which involve the sequential presentation of two stimuli
152 (i.e., the first stimulus-a set of dots or a word-is presented and then disappears before the
153 second one-another set of dots or another word-appears). Indeed, to perform these tasks,
154 individuals were previously found to adopt either an ‘anticipative’ strategy that was relatively
155 fast or a ‘reactive’ strategy that was slower (by 500 to 600ms), without difference in accuracy.

156 Individuals tended to respond either around 500-600ms or around 1000-1100ms, with few
157 responses falling in between, as revealed by a bimodal pattern of the group RT distribution.

158 This substantial gap between the two types of performance suggested two distinct strategies
159 rather than mere variations in response time along a slow-to-fast continuum of individual
160 subjects (unimodal group RT distributions should be seen in this alternative case). More
161 precisely, the reactive or “wait-and-see” strategy may involve a cautious evaluation of past
162 events (in this case, Stim-A) to update and integrate new information (Stim-B) before making
163 a well-informed decision. In contrast, the anticipatory strategy may rely on predicting future
164 events (Stim-B) based on the currently available information (Stim-A), allowing for a faster
165 decision at the cost of increased uncertainty and risk. This anticipatory strategy, with or
166 without social context, was more frequently observed in adults than in children or adolescent
167 (Tricoche et al., 2021; Tricoche et al., 2023). Here, we predicted that partner presence might
168 lead to a social facilitation by favoring the adoption of the anticipative strategy in children,
169 helping them to reduce their developmental gap as compared with adults’ performance. In
fact, in 2021, Tricoche et al., asked children aged 8–10 and adults aged 18–32 to complete
numerosity and phonological comparison tasks, and found that participants of both groups,
but particularly children, responded faster in a social context (coaction condition) as
compared to alone, without any loss in accuracy. For these tasks, mastered around 9-10 years
old (Phillips et al., 2008; Starr et al., 2013), the positive social effect on the decision time was
found whatever the trial difficulty (easy or hard). More precisely, children were not only
faster but also exhibited less variability in response initiation, with a greater proportion
adopting an anticipatory strategy. We hypothesized to find similar results in the present study
with a social facilitation effect improving the adoption of the fast strategy in children, for both
tasks and whatever the trial difficulty. Moreover, a developmental effect was previously
found on the preferred strategies adopted by individuals (Tricoche et al., 2023). If social
presence promotes the adoption of an anticipatory strategy regardless of age, then for
adolescents—who exhibit a more balanced distribution between the two strategies (Tricoche
et al., 2023)—the proportion of anticipatory profiles should, like children, increase to the
detriment of reactive profiles, in the coaction context. Conversely, among adults, who already
predominantly use this optimal strategy, the positive effect of social presence should be more
limited.

187 Methods

189 Participants & sample size justification

Two hundred and seven participants from 8 to 22 years old (113 females, mean age 15.81 years +/- 4.11) were recruited via social network advertising. All participants were native French speakers, without any psychiatric or neurological disorders. The study received approval from the local ethics committee (CPP Sud Est II Ethics Committee) on November 7,
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2018 (ClinicalTrials.gov Identifier: NCT03453216) as well as by the Institutional Review Board of the INSERM (protocol code 15-282-ter, date of approval: 19 December 2018). All participants, or their parents for participants under 18, signed an informed consent. Each participant received a 10 € compensation for their time.

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To explore the developmental trajectory of effects of SFI, we divided participants into five age groups: 27 children (8-10 years), 43 young adolescents (11-13 years), 50 middle adolescents (14-16 years), 28 late adolescents (17-19 years), and 58 young adults (20-22 years) (see table 1 for sample size and demographic information per group). To assign participants to age groups, we adhered to standard classifications (Powers et al., 2022; World Health Organization, n.d.), with our focus on defining age groups rather than inferring strict developmental differences between them. Moreover, as detailed in our subsequent analyses, we mainly investigated age as a continuous variable to capture the overall developmental trajectory from 8 to 22 years.

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Data from the fifty-eight participants in the young adult group and from eleven participants in the late adolescent group were previously analyzed and contributed to the results reported in Tricoche et al., 2021 (Tricoche et al., 2021). For these participants, the exact age in days was not collected (we only collected their birth year). Thus, we decided to estimate their age in days (a measure for some of our analyses, see below), by using the middle of the year as reference for each participant (e.g., a participant having 20 years: 20.5 years converted in 7483 days; a participant having 21 years: 21.5 years converted in 7848 days, etc.).

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Using G*Power, we conducted post-hoc power analyses to determine the achieved statistical power for our sample size of 207 participants with an alpha level (α) of 0.05. Assuming a low-to-medium effect size, which is commonly reported in the literature of SFI effect (see, for instance, Tricoche et al., 2021, with a comparable design), we obtained the following power estimates: 1) 99% for the Age x Condition x Task x Difficulty within-between ANOVAS

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(f=0.17 or η<sup>2</sup>=0.03), 2) 96% for the segmented regression model analyses (f<sup>2</sup>=0.08), and 3)

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62% for the Chi-square tests (V=0.2, df=4). In our study, effect sizes V for the Chi-square
tests ranged between 0.17 and 0.36, then suggesting that these analyses are slightly
underpowered.

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Social context

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Participants were tested in a coaction condition or alone. In the coaction condition,
participants were recruited by pairs, as friends, siblings, life partners or close colleagues. The
two members of each pair were matched in age (age difference ≤3 years) and gender. They
completed the seven-point Inclusion of the Other in the Self (IOS) scale (Gächter et al., 2015)
in order to evaluate the subjectively perceived closeness of their relationship. The mean score
obtained was of 5.73 (standard deviation: ±1.32), which confirmed an overall close
relationship. The members of the duo were seated side by side, each facing a computer screen
and a keyboard, and completed the same tasks at the same time. Both members of the duo
were included as participants, each one being the familiar partner of the other (coaction
situation). As trials order was randomized, the two partners completed a different version of
the task. They were instructed to focus on their computer screens throughout the session, and
to refrain from speaking and assisting each other.

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In the alone condition, participants were tested alone in the experimental room. In both
conditions, at the beginning of the experiment, after giving all the instructions, the
experimenter left the experimental room. One hundred and twelve participants were tested in
the coaction condition and 95 participants were tested in the alone condition. One participant
was excluded from the analyses as one of our main measures of interest, age, was not reported
in the dataset. Group composition is reported in Table 1.

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Table 1: Number of participants (N) in each age group according to the condition (coaction, alone), as well as their mean age (and standard deviation, sd) and gender repartition. IOS scores are also reported for the Coaction condition.

<table><thead><tr><th rowspan="2">Age sub-group</th><th colspan="4">Coaction condition</th><th colspan="3">Alone condition</th></tr><tr><th>N</th><th>Mean age ± sd</th><th>Gender Male - Female</th><th>Mean IOS score ± sd</th><th>N</th><th>Mean age ± sd</th><th>Gender Male - Female</th></tr></thead><tbody><tr><td><strong>8-10y</strong></td><td>15</td><td>9.06y ± 0.82</td><td>9 - 6</td><td>5.87 ± 1.41</td><td>12</td><td>8.61y ± 0.64</td><td>4 - 8</td></tr><tr><td><strong>11-13y</strong></td><td>19</td><td>12.69y ± 0.65</td><td>6 - 13</td><td>5.27 ± 1.67</td><td>24</td><td>12.62y ± 0.57</td><td>12 - 12</td></tr><tr><td><strong>14-16y</strong></td><td>32</td><td>14.84y ± 0.75</td><td>18 - 14</td><td>5.67 ± 1.49</td><td>18</td><td>14.72y ± 0.80</td><td>8 - 10</td></tr><tr><td><strong>17-19y</strong></td><td>12</td><td>18.08y ± 0.86</td><td>7 - 5</td><td>5.83 ± 1.19</td><td>16</td><td>17.94y ± 0.66</td><td>9 - 7</td></tr><tr><td><strong>20-22y</strong></td><td>33</td><td>20.85y ± 0.70</td><td>11 - 22</td><td>5.91 ± 0.98</td><td>25</td><td>21y ± 0.80</td><td>9 - 16</td></tr></tbody><tfoot><tr><td colspan="4">= 111</td><td colspan="3">= 95</td></tr></tfoot></table>

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## 249 Tasks & Stimuli

Participants completed the same two tasks that were previously used in Tricoche et al., 2021 (Tricoche et al., 2021) and based on previous studies (Prado et al., 2011, 2014). The first task was a numerosity comparison task, in which participants decided which of two sequentially displayed dots arrays contained the largest number of dots. The second task was a phonological comparison task, in which participants decided if two words appearing sequentially rhymed or not. These tasks were chosen for several reasons. First, they involve cognitive skills related to school education, specifically numeracy and literacy skills. Moreover, since the two tasks involve different cognitive processes and are mastered at different developmental stages (Ehri et al., 2001; Hyde et al., 2014; Phillips et al., 2008; Prado
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et al., 2011; Starr et al., 2013), it is important to us to examine whether social presence
influences them in the same way. Second, we aimed to replicate the findings from Tricoche et
al. (Tricoche et al., 2021) by using the same tasks to ensure consistency and comparability.

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Finally, the design of these sequential tasks initially allowed to study decision-making
strategies. Thus, we sought to replicate both the social effect on decision-making strategies
observed in Tricoche et al., 2021 (Tricoche et al., 2021) and their developmental effect
reported in Tricoche et al., 2023 (Tricoche, Meunier, et al., 2023).

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The trials time line is shown in Figure 1. All sub-groups completed the same version of the
tasks, each composed by 144 trials.

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Dot arrays used for numerosity comparison contained 12, 18, 24 or 36 dots. Differences in
cumulative surface areas and distribution of dot sizes were both controlled for each pair of dot
arrays (Gebuis & Reynvoet, 2011). Difficulty was varied according to the ratio between the
two dots arrays, from easy trials with a ratio comprised between 0.33 (e.g., 12 versus 36 dots)
and 0.5 to hard trials with a ratio comprised between 0.67 (e.g., 24 versus 36 dots) and 0.75.

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Words used for phonological comparison contained 1 or 2 syllables and 3 to 8 letters, with
similar frequency in French language as assessed by the New and Pallier's dictionary (New B.
et al., 2001). Difficulty was varied by manipulating orthography and phonology between the
two presented words. In easy trials the two words had congruent orthography and phonology
(both same, e.g. dix-six [dis-sis] or both different, e.g. jeu-doux [zoe-du]), whereas in hard
trials orthography and phonology were incongruent (e.g. dos-taux [do-to] or tapis-iris [tapi-
iris]).

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Stimuli were presented in a pseudorandomized order, mixing both numerosity and
phonological comparison tasks together in the same block of trials (i.e., no more than three
successive trials of the same difficulty or task was presented).

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<figure><img src="image_3.png" /><figcaption>Figure 1: Trial's temporality for Numerosity comparison (A) and Phonological comparison (B). Stimuli were presented successively during 800ms with a 200ms in-between interval.</figcaption></figure>

Participants made their decision by pressing one of two possible keys. They could respond as soon as the second stimulus appeared, but before the red square (displayed for a randomized duration) disappeared ('Response delay').

## Analyses

Frequentist statistical analyses were conducted using RStudio (v.4.0.0). We measured for each task and difficulty level the global accuracy as the proportion of correct responses relative to the total number of decisions (%Corr), then we measured the reaction time of each correct response (RT) as the delay between the second stimulus appearance and the key press. We conducted 5 X 2 X 2 X 2 ANOVAs, with Age (8-10y, 11-13y, 14-16y, 17-19y, 20-22y) and Condition (Coaction, Alone) as between-subject factors, and Task (Numerosity, Phonology) and Difficulty (Easy, Hard) as within-subject factors. Post-hoc comparisons were conducted through two-sample Student's t tests with FDR corrections. Effect sizes were reported using partial eta squared values ($\eta_p^2$). Frequentist statistical analyses were complemented by
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Bayesian statistics, calculating the Bayes Factor inclusion (BF<sub>incl</sub>), using JASP (v.0.10.2).

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Extreme, strong, moderate or anecdotal evidence for the null hypothesis (H0) were qualified for a BF<sub>incl</sub> < 0.001, < 0.01, < 0.33, <1 respectively; whereas extreme, strong, moderate or anecdotal evidence for the alternative hypothesis H1 were qualified for a BF<sub>incl</sub> > 100, > 10, > 3 and > 1 (Keysers et al., 2020; Wagenmakers et al., 2018).

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We also analyzed, separately for each task and each difficulty level, the relation between %Corr and Age (measured in days) as well as between RT of correct trials and Age, combining all sub-groups of participants in two superimposed scatter plots (coaction and alone condition). We then estimated the linear trend-lines (y= bx+a) for each condition (coaction and alone) using the least squares method. As reported in the Results part (for RT particularly), all 4 scatter plots (2 tasks x 2 difficulty levels) showed that the linear trend-lines intersected, indicating an age where the direction of the coaction effect changed. We adjusted to the %Corrr and RT data a segmented regression model (%Corr ~ Age*Condition and RT ~ Age*Condition) to estimate these age-breakpoints means and confidence intervals at 95% (CI95) and to investigate if there was a significant difference in the relation between the %Corr or RT and the Age according to the Condition ('segmented' package on R). This segmented regression model analysis was justified as it allows to identify potential nonlinear and complex relationships between variables (Age and Condition), that cannot be assessed by ANOVAs which focus on average effects. It also provides a nuanced view of developmental transitions in SFI effect by identifying breakpoints. By conducting both analyses—one treating age as a categorical variable and the other as a continuous variable—we were able to compare the results and gain insights into the relevance of each approach. This combination allowed us to leverage the strengths of both methods: ANOVAs provided a structured overview of age group differences and interactions, consistent with prior research

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methodologies and facilitating their comparison; while the regression models captured more
fine-grained, continuous age-related changes.

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Finally, we computed the RT distributions of correct responses for each subgroup and each task, comparing the two conditions using a Kolmogorov-Smirnov (K-S) test. For the sake of clarity, we combined easy and hard trials, as our analyses revealed similar social effects. As we mainly obtained bimodal RT distributions at group level indicating two discrete strategies, we calculated the proportion of participants in each profile (fast or slow responders) using the method previously developed (Tricoche et al., 2021; Tricoche et al., 2023) and detailed in **Appendix A**. We then conducted Chi-square tests with Yates continuity correction, comparing the proportion of fast and slow responders between the alone and coaction conditions, for the different ages and tasks separately. In addition to the X², we reported the Bayes Factor 10 (BF₁₀) and the effect sizes as estimated using Cramer's V (Cramer, 1946). Significance level was set at p<0.05 for all frequentist analyses.

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## Results

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### Social effect on accuracy and RT

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#### Accuracy

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As illustrated in Figure 2, the Condition x Age x Task x Difficulty ANOVA revealed a main effect of Age (F(4, 196)=22.37, p<0.001, ηₚ²=0.31, BFₘₐₗ>100), Task (F(1, 588)=4.52, p=0.03, ηₚ²=0.008, BFₘₐₗ>100) and Difficulty (F(1, 588)=742.07, p<0.001, ηₚ²=0.56, BFₘₐₗ>100), but no Condition effect (F(1,196)=1.14, p=0.29, BFₘₐₗ=0.05). We also observed

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two significant interactions: Age by Task (F(4, 588)=7.47, p<0.001, ηₚ²=0.05, BF<sub>incl</sub>>100)
and Difficulty by Task (F(1, 588)=6.57, p=0.01, ηₚ²=0.01, BF<sub>incl</sub>=2.74).

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As expected, accuracy was higher on easy trials (mean %Corr±SEM= 93.2%±0.4 when averaging numerosity and phonology comparisons) than hard trials (79.5%±0.5 when averaging numerosity and phonology comparisons). In line with a number of previous studies, accuracy was also higher for numerosity (85.9%±0.5 when averaging easy and hard trials) than phonology comparison (86.9%±0.7 when averaging easy and hard trials) (Prado et al., 2011, 2014; Tricoche et al., 2021; Tricoche, Meunier, et al., 2023; Tricoche, Pélisson, et al., 2023). Overall, older participants were more accurate than younger participants (8-10y: 76.4%±1.6; 11-13y: 86.5%±0.8; 14-16y: 86.1%±0.8; 17-19y: 89.3%±1.0; 20-22y: 89.8%±0.7). All post-hoc comparisons, including all interactions, are reported in **Appendix B**. Such post-hoc comparisons revealed that the Task by Difficulty interaction was driven by a difference in easy trials only; with no difference in hard trials. By considering a model without the Task factor (i.e., conducting ANOVAs for each task separately) we found results that are comparable to our single ANOVA including Task as a factor (see **Appendix C**).

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Substantiating the absence of main Condition effect, no interaction was found with this factor, suggesting no social effect on accuracy.

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<figure><img src="image_4.png" /></figure>

Figure 2: Accuracy expressed in %Corr according to the Age (by class), Condition (alone in purple, coaction in green), Task (A-B: numerosity; C-D: phonology) and Difficulty (A-C: easy trials; B-D: hard trials). Dots represent individual data, boxes show the medians, lower quartiles, upper quartiles and whiskers (±1.5*Inter-quartile range).

RT

As shown in Figure 3, we found a significant main effect of Age (F(4, 196)=15.42, p<0.001, ηₚ²=0.31, BF<sub>incl</sub>>100), Task (F(1, 588)=1306.22, p<0.001, ηₚ²=0.69, BF<sub>incl</sub>>100) and Difficulty (F(1, 588)=285.99, p<0.001, ηₚ²=0.33, BF<sub>incl</sub>>100) on the RTs. Condition effect did not reach the conventional threshold for significance, and Bayesian analyses indicated anecdotal evidence toward H0 (F(1, 196)=3.86, p=0.051, ηₚ²=0.02, BF<sub>incl</sub>=0.59). Three interactions were significant: Age by Task (F(4, 588)=30.62, p<0.001, ηₚ²=0.17,
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$BF_{incl}$>100), Difficulty by Task (F(1, 588)=46.48, p<0.001, $\eta_p^2$=0.07, $BF_{incl}$>100), and Condition by Difficulty (F(1, 588)=4.09, p=0.04, $\eta_p^2$=0.007, $BF_{incl}$=0.61).

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<figure><img src="image_5.png" /></figure>

Figure 3: RT expressed in ms according to the Age (by class), Coaction (in purple, coaction in green), Task (A-B: numerosity; C-D: phonology) and Difficulty (A-C: easy trials; B-D: hard trials). Same conventions as in Figure 2. A significant social facilitation was found by comparing alone (purple) and coaction (green) conditions, for hard trials (*: p=0.01).

The direction of social effect is modulated by age

Accuracy

We then investigated the relation between %Corr and age according to the social context. As illustrated in Figure 4, the estimated segmented regression models did not show significant any Age by Condition effects, irrespective of the task and the difficulty (t's=[1.37:1.77], p's=[0.08:0.17]). This suggests that the effect of Age on the %Corr was not modulated by the social Condition. This result corroborates the frequentist analyses, which revealed no social effect on %Corr.
<figure><img src="image_6.png" /></figure>

Figure 4: Relation between the Age (in days) and the accuracy (in % of correct responses) according to the Condition (alone in purple, coaction in green), Task (A-B: numerosity; C-D: phonology) and Difficulty (A-C: easy trials; B-D: hard trials). The breakpoints estimated by the model are indicated (dashed lines). However, the segmented regression models showed no significant effect in each of the 4 situations, suggesting that the effect of Age on the %Corr is not modulated by the Condition.

RT

When investigating the relation between RT and age according to the social context, we found a developmental change in the direction of the social effect. As illustrated in Figure 5, linear trend-lines intersected around 13-14 years old. This pattern indicates a social inhibition before 13-14 years (longer RTs in coaction than alone condition) and a social facilitation after this period (shorter RTs in coaction than alone condition), for both tasks and both trial difficulties. Applying a segmented regression model to the data for each task and difficulty level allowed
429 us to estimate breakpoints falling in a narrow age-range [5150 - 5220 days] for all 4 cases
430 (Numerosity-Easy: 5159 days, CI95: ±56.35; Numerosity-Hard: 5159 days, CI95: ±57.69;
431 Phonology-Easy: 5220 days, CI95: ±76; Phonology-Hard: 5159 days, CI95: ±118.3). Finally,
432 contrasting with the ANOVA results reported above, the results of the segmented regression
433 models indicated a significant Age by Condition effect (t's=[-2.53:-2.21], p's=[0.01:0.03]).

434 Thus, this analysis which does not require pooling participants into different sub-groups,
435 showed that the effect of Age on RT was modulated by the Condition, whatever the Task and
436 Difficulty. Overall, we conclude that children under 13-14 years old were socially inhibited
437 whereas older children were socially facilitated.

438

<figure><img src="image_7.png" /></figure>

439

Figure 5: Relation between the Age (in days) and the RT (in ms) according to the Condition (alone in purple, coaction in green), Task (A-B: numerosity; C-D: phonology) and Difficulty (A-C: easy trials; B-D: hard trials). The segmented regression models indicated significant
443 an Age by Condition effect in each of the 4 situations, showing that the effect of Age on the RT
444 is modulated by the Condition. Further, the direction of the social effect
445 (inhibition/facilitation) changed around 5100-5200 days, as revealed by the estimated
446 breakpoints (red dashed lines, with confidence intervals at 95%). Note that the slight
447 difference between the observed graphical intersection of the curves and the statistically
448 determined values of breakpoint can be explained by the fact that the latter is affected by
449 precision of statistical modeling, contrary to the former.

450

451 The social context improves the preferred strategy

452 To evaluate whether the strategies potentially used by participants could change according to
453 age, tasks, or conditions, we plotted the RT distributions of correct responses in each age
454 group and for both tasks. First, to focus on the developmental effect, we pooled the two social
455 conditions together (Figure 6A). These distributions were all bimodal (i.e., two peaks of
456 density). This pattern, which is in line with previous studies (Tricoche et al., 2021; Tricoche et
457 al., 2023) is particularly conspicuous for older participants. Based on these bi-modal
458 distributions at the group level, participants were classified in two profiles according to their
459 individual RT distribution: participants who responded relatively fast (mean RT around
460 600ms) and participants who responded relatively slowly (around 1,100ms) (Appendix A).
461 This distinction between fast responders around 600ms and slow responders around 1,100ms
462 replicated previous results, as well as the observed developmental trajectory (Tricoche et al.,
463 2021; Tricoche et al., 2023). Indeed, while the youngest participants adopted preferentially the
464 slow strategy, the number of fast responders increased with age, such that it becomes the
465 predominant strategy in the 17 years old group (Figure 6A).
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Then, to test whether there was an effect of Condition, we plotted for each sub-group and each task, the ‘‘alone’’ versus ‘‘coaction’’ distributions. Conducting K-S tests, we found that the ‘‘alone’’ distributions differed from the ‘‘coaction’’ distributions for all sub-groups in both tasks (D’s=[0.10: 0.23], all p’s<0.001) (Figure 6B). Using the method previously developed (Tricoche et al., 2021;2023) and detailed in **Appendix A**, we calculated the proportion of participants in each profile (fast responders, slow responders), and investigated for each task separately, if this proportion differed with the Condition according to the Age (Chi-square tests). For Numerosity, we found a significant effect of Condition on the proportion of fast and slow responders for the 11-13y (X²=9, p=0.003, V=0.21, BF₁₀=1.04), 14-16y (X²=6.1, p=0.01, V=0.17, BF₁₀=0.69) and 20-22y (X²=9.8, p=0.002, V=0.22, BF₁₀=1.35). For Phonology, this Condition effect was found for the 8-10y (X²=16.46, p<0.001, V=0.29, BF₁₀=1.91), 11-13y (X²=26.33, p<0.001, V=0.36, BF₁₀=7.49) and 20-22y (X²=12.75, p<0.001, V=0.25, BF₁₀=2.1), while being marginal for the 14-16y (X²=3.19, p=0.07, V=0.13, BF₁₀=0.49) (Figure 6C). Interestingly, and as illustrated in Figure 6B&C, the direction of this social effect seemed to differ according to the sub-groups, favoring the slow strategy in younger participants (8 to 13 years), while improving the fast strategy in older ones (>14 years).
A

<figure><img src="image_8.png" /></figure>

B

<figure><img src="image_9.png" /></figure>

C

Proportion of fast responders (in %)

<figure><img src="image_10.png" /></figure>

Proportion of slow responders (in %)

<figure><img src="image_11.png" /></figure>
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Figure 6: A: RT distributions of correct responses superimposed for the different Age sub-groups (colors) separately for Numerosity (left) and Phonology (right). B: RT distributions of correct responses superimposed for the alone (purple) and coaction (green) Conditions separately for the Age sub-groups (left-to-right) and for Numerosity (upper row) and Phonology (lower row). Two profiles of participants can be seen: fast (first peak of density around 600ms) and slow (second peak of density around 1,100ms) responders, with a developmental trajectory from the slow to the fast strategy. C: Proportion of fast (upper row) and slow (lower row) responders in the alone (purple) and coaction (green) condition, ranked according to the Age sub-groups separately for Numerosity (left) and Phonology (right). Even though the proportion of slow responders complements that of fast responders, both graphs are displayed for enhanced visual clarity. While the slow strategy dominates in younger participants (8 to 13 years), the fast strategy increases in older ones (>14 years) (#: p<0.08; *: p<0.05; **: p<0.01; ***: p<0.001).

Discussion

In this study, we investigated the developmental trajectory of social presence effects in two judgment tasks (numerosity or phonology) involving a comparison of two items presented sequentially. We compared five age sub-groups, from childhood to adulthood, tested either alone or in a coaction condition with a familiar age-matched partner. In this study, partners could be friends, siblings, life partners, or close colleagues. Duos had an equivalent level of closeness, as indicated by the IOS scale results. While partners type may influence social effect on behavioral outcomes differently, particularly in adolescents, existing literature suggests that such differences are more often linked to the type of the presence (e.g., mother
509 vs. peer, familiar vs. unfamiliar, audience vs. evaluation) rather than the nature of the
510 relationship with an age-matched partner (e.g., friend, sibling, colleague, life partner)
511 (Bouhours et al., 2021; Powers et al., 2022; Salvy et al., 2008; Telzer et al., 2015; van Hoorn
512 et al., 2018, 2019; Wolf et al., 2015). Notably, Tricoche et al. (2021), using the same
513 paradigm, found no significant difference in results based on the nature of the partner
514 relationship (Tricoche et al., 2021).

515 When analyzed at the group level, without considering developmental differences, the
516 coaction condition led in both tasks to a social facilitation on RT (but not on accuracy) for the
517 hard trials. These findings only partially replicated findings from a previous study in the
518 youngest and oldest subjects groups (Tricoche et al., 2021), then not fully supporting our
519 hypotheses. Indeed, we previously found a social facilitation in these tasks for both groups of
520 participants, and for both trial types (including easy trials). However, in our previous study,
521 some of the children completed a simplified version of the tasks (i.e., less number of dots or
522 less controlled stimuli in numerosity comparison). This might explain why, at the group level,
523 they were facilitated by social presence across all trials. In contrast, in the present study, all
524 participants completed a more difficult version of the task. This may have led to a more
525 heterogeneous performance at first: while some participants (essentially the older participants)
526 might have been mature enough to succeed at the required level, others (essentially the
527 youngest participants) might not have fully developed the necessary skills. Consequently, the
528 effect of social presence on RTs may have fluctuated between facilitation and inhibition
529 depending on the participants' individual skill levels, which could not be fully captured in a
530 group-level analysis. Thus, these results at the group level must be considered cautiously,
531 especially as the segmented regression analysis of the effect of age (in days) and condition on
532 RT led to a more nuanced pattern. Indeed, this analysis indicated that the social effect on RT
533 was modulated by age, with a switch between social inhibition and social facilitation around
534

13-14 years old. This finding suggests an important age-dependent inter-individual variability: while the youngest participants are inhibited, older participants are facilitated by social presence. Camarda and collaborators (2021) also found a similar pattern of social influence on a creativity task, with a social inhibition in early adolescents (11 years old) and a social facilitation in middle adolescents (15 years old) (Camarda et al., 2021), suggesting that a switch can occur in several cognitive tasks. This age-dependent variability may be further amplified when considering different contexts that could modulate the observed effects. For example, Bouhours et al. (2021) demonstrated that an evaluative presence leads to greater attentional distraction in adolescents compared to adults during an emotional Stroop task, ultimately resulting in better inhibitory control (Bouhours et al., 2021). This indicates, in line with other studies, that both the type of task and the nature of the presence interact with the developmental time-course of social influence. Therefore, the shift observed in the present study may occur at different ages depending on the context in which performance is measured. In particular, for adolescents, the presence of evaluators or parents could have stronger effects—either enhancing or impairing performance. This is supported by a report from Wolf et al., indicating that performance in a relational reasoning task is influenced by the participant's age (younger vs. older adolescents vs. adults), the type of presence (evaluation by a friend vs. an experimenter), and the task complexity (low vs. high reasoning level). Additionally, research by Telzer and colleagues highlights specific neural activity modulations depending on whether the presence is that of a parent, a close peer, or whether the individual is alone, ultimately leading to performance changes (Telzer et al., 2015; van Hoorn et al., 2018). Moreover, while the question of how different familiar figures—such as siblings versus friends—might differentially modulate social influences is indeed relevant, the literature on this issue remains scarce, particularly in younger age groups. Among the few studies that have addressed this question, a systematic review found no consistent difference

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of social influence between siblings and friends in the context of eating behavior (Ragelien & Grønhøj, 2020). That review also reported mixed findings—both positive and negative influences—even within the same category of social presence, making it difficult to generalize conclusions across different behaviors or forms of social influence. Similarly, another study suggested that the influence of siblings and peers on substance use in adolescents may vary depending on cultural background, with effects that can be either similar or distinct (Rowan, 2016). However, these studies were mainly concerned with interactive forms of social influence involving direct social interactions. To our knowledge, no study has specifically investigated the distinction between siblings, friends, or other relevant partners in the context of SFI effect. In the present study, the heterogeneous and uneven distribution of partner types prevented us from controlling for this factor or evaluating it systematically. Further studies will be necessary to systematically examine the interaction of these factors and their relative influence across development. Ultimately, this will contribute to a deeper understanding of social learning environments in schools and the impact they may have on the acquisition of academic skill. Moreover, a notable limitation of this study is the precision of age measurement for participants over 20 years old (as well as a few participants aged 18–19). For this subgroup—initially recruited for a previous study—we only recorded their birth year and subsequently estimated their age in days (see Method section). This approach reduced variability within the subgroup and partially altered the overall age distribution, potentially introducing measurement error in our analyses, particularly in breakpoint estimation. Nonetheless, we opted to include this age category because it enhances the representativeness of the sample by spanning the full developmental curve and capturing its overall trends. This method also provided preliminary evidence warranting further investigation. Future studies should ideally collect precise age data across
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all groups, which would allow for a more accurate identification of the specific age range at
which the transition from social inhibition to social facilitation occurs.

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Another limitation worth noting is that participants in the coaction condition were paired with
familiar partners, which may introduce a degree of similarity between dyad members—for
instance in terms of cognitive abilities, motivation, or response style. Such similarity could
potentially reduce variability within dyads and lead to clustering effects that were not
accounted for in the present analyses. While the IOS scale confirmed comparable closeness
across pairs, future studies should consider modeling potential dyadic dependencies
statistically (e.g., with multilevel approaches) or include control conditions with randomly
paired individuals.

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Furthermore, another critical contribution of our study, based on the analysis of RT
distributions, is that the difference in the direction of the social effect might be due to an
improvement in the participants' predominant response strategy. Based on our previous
findings, we initially predicted that the social context in our task would facilitate performance
by promoting the adoption of the anticipative strategy—optimal for its faster responses
without cost on accuracy—across all age groups. However, our results revealed that social
presence could influence both strategies, particularly reinforcing the one that is predominant
for each age group. Nonetheless, the sensitivity analysis we conducted indicated that the Chi-
square tests (leading to effect sizes ranging between 0.17 and 0.36) may have been slightly
underpowered (power estimate = 62%), and these results should be interpreted with some
caution and require replication in future studies. For the youngest participants, the
predominant strategy was to produce relatively slow responses, whereas older participants
adopted the optimal fast strategy. (Tricoche, Meunier, et al., 2023). Social presence may drive
the cognitive system and lead participants to focus on the most appropriate way to succeed in

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the task. Our proposal is that by improving the predominant strategy used by participants, partner presence led to social inhibition in children and young adolescents (<13-14 years) and to social facilitation in mid-adolescents and adults. This interpretation aligns with and complements Zajonc's seminal theory therein social presence increases the probability of the dominant response, which is the most probable or common response during the performed task (R. Zajonc, 1965; R. B. Zajonc et al., 1969). While Zajonc's physiological perspective primarily focuses on an arousal modulation origin of social facilitation/inhibition effects, our findings suggest a broader perspective of social influence. Instead of simply inducing moderate changes of the reaction time (~a few tens of msec) of dominant responses through increased arousal, we revealed a global change in the participants' response strategy, and hence of the decision-making process, ultimately resulting in a substantial modification of response time up to several hundred milliseconds. This suggests a more comprehensive strategic adjustment due to social context rather than a mere reflexive response driven by heightened arousal. In Tricoche, Meunier, et al., 2023, it was proposed that the fast strategy could be associated with an anticipatory process which develops with age in relation with the growing capacities of working memory (Adam et al., 2012; Chen et al., 2007; Swensson & Edwards, 1971); it was also argued that young children, on the other hand, adopted a more reactive and cautious strategy, waiting for the second stimulus appearance before making a decision with a high degree of certainty (see also Hedge et al., 2019). The lower working memory abilities in individuals before thirteen years old (Van Duijvenvoorde et al., 2012) could be a potential explanation of why they preferentially adopted the reactive strategy. Thus, this developmental factor may also help to account for the presently-observed inhibition before this period of age and switch to facilitation later on.

631
Interestingly and contrary to the present results, it was previously found that social presence
helped children to adopt the fast strategy, allowing them to become more ‘‘adult-like’’ in their
behavior (Tricoche et al., 2021). This difference between the two studies could be explained
by the adapted difficulty in this previous study where children completed a simplified version
of the task, in numerosity comparison particularly. This adaptation possibly enhanced their
ability to adopt a fast strategy for this very well mastered task. More specifically, both task
versions (the simpler one used previously, with fewer dots and less controlled stimuli, and the
more complex version used here) included both easy and hard trials, defined by the ratio
between the two dot arrays. However, previous findings indicate that trial difficulty within the
task does not influence the adopted strategy (Tricoche, Meunier, et al., 2023). Participants
seem to choose their strategy at the start and maintain it throughout the task, regardless of
fluctuations in difficulty. In the previous study, where the overall task was simpler, children
may have found it easier to develop an anticipative strategy early on, feeling more confident
in their ability to master the task. In contrast, the present study, with its increased
complexity—particularly due to the doubled number of dots—may have made it harder for
them to rely on anticipation. Future studies will have to better explore these postulated
different effects on the chosen strategy between the global task difficulty and the single trials
difficulty, considering the self-confidence of individuals to complete these tasks.

To our knowledge, no other study has been conducted to investigate the social influence on
these decision-making strategies, preventing us from comparing our findings with the existing
literature. Moreover, it is important to keep in mind that these conclusions are only valid for
these specific sequential comparative judgment tasks and cannot yet be extended to other
forced-choice tasks or general decision-making abilities. Nonetheless, our findings open the
door to a better understanding of the social influence effect on the traditionally known
"dominant response." They also aim to highlight the importance of considering age as a
657 continuous variable over than a categorical factor, and analyzing performance at the
658 individual level to accurately capture variations in SFI effects across development and map its
659 precise trajectory.

660

661 **References**

662 Adam, R., Bays, P. M., & Husain, M. (2012). Rapid decision-making under risk. *Cognitive Neuroscience, 3*(1), 52–61.

664 Albert, D., Chein, J., & Steinberg, L. (2013). The Teenage Brain: Peer Influences on Adolescent Decision Making. *Current Directions in Psychological Science*, 22(2), 114–120. https://doi.org/10.1177/0963721412471347

667 Barkley, J. E., Salvy, S. J., Sanders, G. J., Dey, S., Von Carlowitz, K. P., & Williamson, M. L. (2014). Peer influence and physical activity behavior in young children: An experimental study. *Journal of Physical Activity and Health, 11*(2), 404–409.
669 https://doi.org/10.1123/jpah.2011-0376

671 Baron, R. S., Moore, D., & Sanders, G. S. (1978). Distraction as a source of drive in social facilitation research. *Journal of Personality and Social Psychology, 36*(8), 816–824.
672 https://doi.org/10.1037/0022-3514.36.8.816

674 Bevelander, K. E., Lichtwarck-aschoff, A., Anschütz, D. J., Hermans, R. C. J., & Engels, R. C. M. E. (2013). *Imitation of snack food intake among normal-weight and overweight children*. 4(December), 1–7. https://doi.org/10.3389/fpsyg.2013.00949

677 Bevelander, K. E., Meiselman, H. L., Anschütz, D. J., & Engels, R. C. M. E. (2013). Television watching and the emotional impact on social modeling of food intake among children q. *Appetite*, 63, 70–76. https://doi.org/10.1016/j.appet.2012.12.015
680 Bond, & Titus, L. J. (1983). Social facilitation: a meta-analysis of 241 studies. *Psychological Bulletin, 94*(2), 265–292. https://doi.org/https://doi.org/10.1037/0033-2909.94.2.265

682 Bouhours, L., Camarda, A., Ernst, M., Osmont, A., Borst, G., & Cassotti, M. (2021). How does social evaluation influence Hot and Cool inhibitory control in adolescence? *PLoS ONE, 16*(9 September), 1–14. https://doi.org/10.1371/journal.pone.0257753

685 Camarda, A., Bouhours, L., Osmont, A., Le Masson, P., Weil, B., Borst, G., & Cassotti, M. (2021). Opposite Effect of Social Evaluation on Creative Idea Generation in Early and Middle Adolescents. *Creativity Research Journal, 33*(4), 399–410. https://doi.org/10.1080/10400419.2021.1902174

689 Chein, J., Albert, D., Brien, L. O., Uckert, K., & Steinberg, L. (2011). Peers increase adolescent risk taking by enhancing activity in the brain's reward circuitry. *Developmental Science, 14*(2), 1–10. https://doi.org/10.1111/j.1467-7687.2010.01035.x

693 Chen, J., Hale, S., & Myerson, J. (2007). Predicting the size of individual and group differences on speeded cognitive tasks. *Psychonomic Bulletin & Review, 14*(3), 534–541. https://doi.org/10.3758/bf03194103

696 Coppinger, T., Jeanes, Y. M., Dabinett, J., Vögele, C., & Reeves, S. (2010). Physical activity and dietary intake of children aged 9-11 years and the influence of peers on these behaviours: A 1-year follow-up. *European Journal of Clinical Nutrition, 64*(8), 776–781. https://doi.org/10.1038/ejcn.2010.63

700 Cramér, H. (1946). *Mathematical Methods of Statistics* (Princeton). Princeton University Press.

702 Drewett, R. F. (2007). The social facilitation of food intake. *Archives of Disease in
703 *Childhood*, 92(5), 377. https://doi.org/10.1136/adc.2006.108332

704 Dumontheil, I., Wolf, L. K., & Blakemore, S.-J. (2016). Audience effects on the neural correlates of relational reasoning in adolescence. *Neuropsychologia*, 87, 85–95.

706 https://doi.org/10.1016/j.neuropsychologia.2016.05.001

707 Ehri, L. C., Nunes, S. R., Willows, D. M., Schuster, B. V., Yaghoub-Zadeh, Z., & Shanahan, T. (2001). Phonemic Awareness Instruction Helps Children Learn to Read: Evidence

709 From the National Reading Panel's Meta-Analysis. *Reading Research Quarterly*, 36(3), 250–287. https://doi.org/10.1598/rrq.36.3.2

711 Engelmann, J. M., Herrmann, E., & Tomasello, M. (2015). The effects of being watched on resource acquisition in chimpanzees and human children. *Animal Cognition*.

713 https://doi.org/10.1007/s10071-015-0920-y

714 Gächter, S., Starmer, C., & Tufano, F. (2015). Measuring the closeness of relationships: A comprehensive evaluation of the “inclusion of the other in the self” scale. *PLoS ONE*, 10(6), 1–19. https://doi.org/10.1371/journal.pone.0129478

717 Gardner, M., & Steinberg, L. (2005). Peer influence on risk taking, risk preference, and risky decision making in adolescence and adulthood: An experimental study. *Developmental

719 *Psychology*, 41(4), 625–635. https://doi.org/10.1037/0012-1649.41.4.625

720 Gebuis, T., & Reynvoet, B. (2011). Generating nonsymbolic number stimuli. *Behavior Research Methods*, 43(4), 981–986. https://doi.org/10.3758/s13428-011-0097-5

722 Gonzales, T., Jackson, M. L., & Nicolson, A. (2019). The Effects of Peer Presence on Variables Maintaining Moderate-to-Vigorous Physical Activity in Children. *Behavior

724 *Modification*, 014544551985074. https://doi.org/10.1177/0145445519850748

725 Guerin, B. (2010). *Social facilitation*. Cambridge University Press.
726 Haddad, A. D. M., Harrison, F., Norman, T., & Lau, J. Y. F. (2014). Adolescent and adult risk-taking in virtual social contexts. *Frontiers in Psychology, 5*(DEC), 1-7.
727
728 https://doi.org/10.3389/fpsyg.2014.01476

729 Hedge, C., Vivian-Griffiths, S., Powell, G., Bompas, A., & Sumner, P. (2019). Slow and steady? Strategic adjustments in response caution are moderately reliable and correlate across tasks. *Consciousness and Cognition*, 75(July), 1-17.
730
731
732 https://doi.org/10.1016/j.concog.2019.102797

733 Hoffmann, F., Puetz, V. B., Viding, E., Sethi, A., Palmer, A., & McCrory, E. J. (2018). Risk-taking, peer-influence and child maltreatment: A neurocognitive investigation. *Social Cognitive and Affective Neuroscience*, 13(1), 124-134.
734
735
736 https://doi.org/10.1093/scan/nsx124

737 Hyde, D. C., Khanum, S., & Spelke, E. S. (2014). Brief non-symbolic, approximate number practice enhances subsequent exact symbolic arithmetic in children. *Cognition*, 131(1), 92-107. https://doi.org/10.1016/j.cognition.2013.12.007

740 Keysers, C., Gazzola, V., & Wagenmakers, E. J. (2020). Using Bayes factor hypothesis testing in neuroscience to establish evidence of absence. *Nature Neuroscience*, 23(7), 788-799. https://doi.org/10.1038/s41593-020-0660-4

743 Kieffer, L. F. (1977). The Relationship of Trait Anxiety, Peer Presence, Task Difficulty, and Skill Acquisition of Sixth Grade Boys. *Middle School Research Selected Studies*, 1-3(1), 107-115. https://doi.org/10.1080/08851700.1977.11670190

746 Lumeng, J. C., & Hillman, K. H. (2007). Eating in larger groups increases food consumption. *Archives of Disease in Childhood*, 92(5), 384-387.
747
748 https://doi.org/10.1136/adc.2006.103259
749 New B., Pallier C., Ferrand L., & Matos R. (2001). Une base de données lexicales du français contemporain sur internet: LEXIQUE. *L'Année Psychologique*, **101**(3), 447-462.
750
751 http://www.lexique.org

752 Phillips, B. M., Clancy-Menchetti, J., & Lonigan, C. J. (2008). Successful Phonological Awareness Instruction With Preschool Children: Lessons From the Classroom. *Topics in Early Childhood Special Education*, 28(1), 3-17.
753
754
755 https://doi.org/10.1177/0271121407313813

756 Powers, K. E., Schaefer, L., Figner, B., & Somerville, L. H. (2022). Effects of Peer Observation on Risky Decision-Making in Adolescence: A Meta-Analytic Review. *Psychological Bulletin*, 148(11-12), 783-812. https://doi.org/10.1037/bul0000382

759 Prado, J., Mutreja, R., & Booth, J. R. (2014). Developmental dissociation in the neural responses to simple multiplication and subtraction problems. *Developmental Science*, 17(4), 537-552. https://doi.org/10.1111/desc.12140

762 Prado, J., Mutreja, R., Zhang, H., Mehta, R., Desroches, A. S., Minas, J. E., & Booth, J. R. (2011). Distinct representations of subtraction and multiplication in the neural systems for numerosity and language. *Human Brain Mapping*, 32(11), 1932-1947.
763
764
765 https://doi.org/10.1002/hbm.21159

766 Ragelien, T., & Grønhøj, A. (2020). The influence of peers' and siblings' on children 's and adolescents' healthy eating behavior. A systematic literature review. *Appetite*, 148(104592). https://doi.org/10.1016/j.appet.2020.104592

769 Reniers, R. L. E. P., Beavan, A., Keogan, L., Furneaux, A., Mayhew, S., & Wood, S. J. (2017). Is it all in the reward? Peers influence risk-taking behaviour in young adulthood. *British Journal of Psychology*, 108(2), 276-295. https://doi.org/10.1111/bjop.12195
772 Rittenhouse, M., Salvy, S. J., & Barkley, J. E. (2011). The effect of peer influence on the amount of physical activity performed in 8- to 12-year-old boys. *Pediatric Exercise Science*, 23(1), 49–60. https://doi.org/10.1123/pes.23.1.49

775 Rosen, D., Patel, N., Pavletic, N., Grillon, C., Pine, D. S., & Ernst, M. (2016). Age and Social Context Modulate the Effect of Anxiety on Risk-taking in Pediatric Samples. *Journal of Abnormal Child Psychology*, 44(6), 1161–1171. https://doi.org/10.1007/s10802-015-0098-4

779 Ross, V., Jongen, E. M. M., Brijs, K., Brijs, T., & Wets, G. (2016). Investigating risky, distracting, and protective peer passenger effects in a dual process framework. *Accident Analysis and Prevention*, 93, 217–225. https://doi.org/10.1016/j.aap.2016.05.007

782 Rowan, Z. R. (2016). Social Risk Factors of Black and White Adolescents' Substance Use: The Differential Role of Siblings and Best Friends. *Journal of Youth and Adolescence*, 45(7), 1482–1496. https://doi.org/10.1007/s10964-016-0473-7

785 Salvy, S., Kieffer, E., & Epstein, L. H. (2008). *Effects of social context on overweight and normal-weight children's food selection*. 9, 190–196.
https://doi.org/10.1016/j.eatbeh.2007.08.001

788 Salvy, S., Roemmich, J. N., Bowker, J. C., Romero, N. D., Stadler, P. J., & Epstein, L. H. (2009). Effect of Peers and Friends on Youth Physical Activity and Motivation to be Physically Active. *Journal of Pediatric Psychology*, 34(2), 217–225.
https://doi.org/10.1093/jpepsy/jsn071

792 Siegmund, L. A., Naylor B., J. B., Santo S., A. S., & Barkley E., J. E. (2014). The effect of a peer on VO2 and game choice in 6-10 year old children. *Frontiers in Physiology*, 5 JUN(June), 1–10. https://doi.org/10.3389/fphys.2014.00202
795 Smith, A. R., Rosenbaum, G. M., Botdorf, M. A., Steinberg, L., & Chein, J. M. (2018). Peers influence adolescent reward processing, but not response inhibition. *Cognitive, Affective and Behavioral Neuroscience*, 18(2), 284–295. https://doi.org/10.3758/s13415-018-0569-5

799 Somerville, L. H., Haddara, N., Sasse, S. F., Skwara, A. C., Moran, J. M., & Figner, B. (2019). Dissecting “Peer Presence” and “Decisions” to Deepen Understanding of Peer Influence on Adolescent Risky Choice. *Child Development*, 90(6), 2086–2103. https://doi.org/10.1111/cdev.13081

803 Starr, A., Libertus, M. E., & Brannon, E. M. (2013). Number sense in infancy predicts mathematical abilities in childhood. *Proceedings of the National Academy of Sciences of the United States of America*, 110(45), 18116–18120. https://doi.org/10.1073/pnas.1302751110

807 Swensson, R. G., & Edwards, W. (1971). Response strategies in a two-choice reaction task with a continuous cost for time. *Journal of Experimental Psychology*, 88(1), 67–81. https://doi.org/10.1037/h0030646

810 Telzer, E. H., Ichien, N. T., & Qu, Y. (2015). Mothers know best: Redirecting adolescent reward sensitivity toward safe behavior during risk taking. *Social Cognitive and Affective Neuroscience*, 10(10), 1383–1391. https://doi.org/10.1093/scan/nsv026

813 Telzer, E. H., Rogers, C. R., & Van Hoorn, J. (2017). Neural Correlates of Social Influence on Risk Taking and Substance Use in Adolescents. *Current Addiction Reports*, 4(3), 333–341. https://doi.org/10.1007/s40429-017-0164-9

816 Tricoche, L., & Caspar, E. A. (2024). The power of social influence on (non-)moral decision-making: A review of behavioral effects and neural mechanisms. *Preprint from OSF*. https://doi.org/https://osf.io/preprints/osf/s6jtk_v1
819

Tricoche, L., Meunier, M., Hassen, S., Prado, J., & Pélisson, D. (2023). Developmental

820

Trajectory of Anticipation: Insights from Sequential Comparative Judgments. *Behavioral

821

Sciences, 13(8). https://doi.org/10.3390/bs13080646

822

Tricoche, L., Monfardini, E., Reynaud, A. J., Epinat-Duclos, J., Pélisson, D., Prado, J., &

823

Meunier, M. (2021). Peer Presence Effect on Numerosity and Phonological Comparisons

824

i in 4th Graders: When Working With a Schoolmate Makes Children More Adult-Like.

825

*Biology, 10*(902). https://doi.org/10.3390/biology10090902

826

Tricoche, L., Pélisson, D., Longo, L., Koun, E., Poisson, A., Prado, J., & Meunier, M. (2023).

827

Task-independent neural bases of peer presence effect on cognition in children and

828

adults. *NeuroImage*, 277(120247).

829

https://doi.org/10.1016/j.neuroimage.2023.120247

830

Van Duijvenvoorde, A. C. K., Jansen, B. R. J., Bredman, J. C., & Huizenga, H. M. (2012).

831

Age-related changes in decision making: Comparing informed and noninformed

832

situations. *Developmental Psychology*, 48(1), 192–203.

833

https://doi.org/10.1037/a0025601

834

van Hoorn, J., McCormick, E. M., Rogers, C. R., Ivory, S. L., & Telzer, E. H. (2018).

835

Differential effects of parent and peer presence on neural correlates of risk taking in

836

adolescence. *Social Cognitive and Affective Neuroscience*, 13(9), 945–955.

837

https://doi.org/10.1093/scan/nsy071

838

van Hoorn, J., Shablack, H., Lindquist, K. A., & Telzer, E. H. (2019). Incorporating the social

839

context into neurocognitive models of adolescent decision-making: A neuroimaging

840

meta-analysis. *Neuroscience and Biobehavioral Reviews*, 101, 129–142.

841

https://doi.org/10.1016/j.neubiorev.2018.12.024

842

Wagenmakers, E. J., Love, J., Marsman, M., Jamil, T., Ly, A., Verhagen, J., Selker, R.,
843 Gronau, Q. F., Dropmann, D., Boutin, B., Meerhoff, F., Knight, P., Raj, A., van

844 Kesteren, E. J., van Doorn, J., Šmíra, M., Epskamp, S., Etz, A., Matzke, D., ... Morey,

845 R. D. (2018). Bayesian inference for psychology. Part II: Example applications with

846 JASP. *Psychonomic Bulletin and Review*, 25(1), 58–76. https://doi.org/10.3758/s13423-

847 017-1323-7

848 Wolf, L. K., Bazargani, N., Kilford, E. J., Dumontheil, I., & Blakemore, S. J. (2015). The

849 audience effect in adolescence depends on who’s looking over your shoulder. *Journal of*

850 *Adolescence*, 43, 5–14. https://doi.org/10.1016/j.adolescence.2015.05.003

851 World Health Organization. (n.d.). *Adolescence: A period needing special attention—*

852 *Recognizing-adolescence*. Retrieved in 2025 from Https://Www.Who.Int/Health-

853 Topics/Adolescent-Health.

854 Zajonc, R. (1965). Social facilitation. *Science*, 149, 269–274.

855 https://doi.org/10.1126/science.149.3681.269

856 Zajonc, R. B., Heingartner, A., & Herman, E. M. (1969). Social enhancement and impairment

857 of performance in the cockroach. *Journal of Personality and Social Psychology*, 13(2),

858 83–92. https://doi.org/10.1037/h0028063

859