# The neural basis of transactive technical cognition

François Osiurak, Chloé Bryche, Maximilien Metaireau, Alexandre Bluet, Camille Gramaje, Josselin Baumard, Yves Rossetti, Mathieu Lesourd, Giovanni Federico

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François Osiurak, Chloé Bryche, Maximilien Metaireau, Alexandre Bluet, Camille Gramaje, et al. The neural basis of transactive technical cognition. NeuroImage, 2025, 321, pp.121527. 10.1016/j.neuroimage.2025.121527. hal-05453662

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# The neural basis of transactive technical cognition

François Osiurak<sup>a,b,*</sup>, Chloé Bryche<sup>a,†</sup>, Maximilien Metaireau<sup>a,c</sup>, Alexandre Bluet<sup>a,d</sup>, Camille Gramaje<sup>a</sup>, Josselin Baumard<sup>b,e,‡</sup>, Yves Rossetti<sup>f,g</sup>, Mathieu Lesourd<sup>c,f,§</sup>, Giovanni Federico<sup>h</sup>

## ARTICLE INFO

### Keywords:

- Causal understanding

- Collaboration

- Human-technology interaction

- Metacognition

- Teaching

## ABSTRACT

Human societies are organized around the division of labor and cultural specialization. This organization is effective only if individuals can represent their own and others' technical cognition skills and adjust them according to the nature of the collaborative activity. We conducted a behavioral (n = 30) and an fMRI (n = 29) experiment to investigate these adjustments in two collaborative activities: Delegation and teaching. Here we show that, when individuals delegate a technical task to an expert, they partially disengage the technical reasoning network (left area PF, left inferior frontal gyrus, dorsolateral prefrontal cortex) they recruit when planning to perform the task themselves in favor of the social reasoning network (temporal pole, temporoparietal junction, and anterior medial prefrontal cortex) that helps them represent the expert's skills. When individuals envision being taught by an expert, they preferentially recruit both networks, forming an integrative representation of their own and the expert's skills. This integrative representation may serve as the foundation for "who" social learning strategies by enabling individuals to estimate the knowledge gap between themselves, as learners, and a potential teacher, much like teachers do when planning to teach a learner. Individuals also use this integrative representation when considering following a Web tutorial, confirming that the Internet, and other technologies, can be the targets of transactive processes. Taken together, these findings open new avenues for understanding how transactive technical cognition has co-evolved with cultural specialization and supports human-technology interactions.

## 1. Introduction

Cultural specialization, where some individuals become proficient at making certain tools or technologies and accumulate specialized knowledge not shared by the rest of the group (Ben-Oren et al., 2023; Migliano and Vinicius, 2022; Smolla and Akçay, 2019), benefits group members by increasing the overall probability of success for various specific tasks, a phenomenon initially conceptualized by Smith (1776) in his iconic *Wealth of Nations*. This specialization imposes cognitive demands on individuals seeking to benefit from it. One of these demands is the ability to identify knowledgeable individuals, if any, capable of

either performing the specific task themselves (delegation) or supervising others in its execution (teaching). In some cases, undertaking the task independently may also be viable. Thus, the effectiveness of a specialization-based organization depends on the group members' ability to represent their own and others' technical cognition skills (Osiurak and Reynaud, 2020). Hereafter we refer to this ability *transactive technical cognition*. The goal of this study is to shed light on the processes underlying this specific form of metacognition, which is essential for engaging effectively in collaborative technical activities. Here, metacognition describes meta-representations of our own or others' cognitive abilities.

<footnote><sup>a</sup> Laboratoire d'Étude des Mécanismes Cognitifs, Université de Lyon, Lyon, France</footnote>

<footnote><sup>b</sup> Institut Universitaire de France, Paris, France</footnote>

<footnote><sup>c</sup> Université Marie et Louis Pasteur, INSERM, UMR 1322 LINC, F-25000 Besançon, France</footnote>

<footnote><sup>d</sup> Karolinska Institutet, Stockholm, Sweden</footnote>

<footnote><sup>e</sup> Normandie Univ, UNIROUEN, CRFDP 76000 Rouen, France</footnote>

<footnote><sup>f</sup> Centre de Recherche en Neurosciences de Lyon, Trajectoires Team, CNRS US292, Inserm U1028, Université de Lyon, France</footnote>

<footnote><sup>g</sup> Mouvement, Handicap, et Neuro-Immersion, Hospices Civils de Lyon et Centre de Recherche en Neurosciences de Lyon, Hôpital Henry Gabrielle, St Genis Laval, France</footnote>

<footnote><sup>h</sup> Laboratory of Experimental Psychology, Suor Orsola Benincasa University, Naples, Italy</footnote>

<footnote>* Corresponding author.
E-mail address: francois.osiurak@univ-lyon2.fr (F. Osiurak).</footnote>
There is a rich body of research on the psychological processes involved in how people represent other minds in collaborative activities. Some of this research has focused on everyday joint actions that require temporal coordination between at least two individuals, such as playing a piano duet or lifting a two-handled picnic basket (Sebanz and Knöblich, 2021; Sebanz et al., 2006; Török et al., 2019; Vesper et al., 2010). More relevant to the present study are works that have investigated the psychological foundations of the “group mind”, exploring how people work together by using their combined knowledge (Sloman and Rabb, 2016; Xiang et al., 2023). About four decades ago, Wegner and colleagues (Wegner et al., 1991) discovered that when people know what they and their partners are good at, they naturally develop strategies to organize their work more effectively. For instance, when participants were tasked with remembering 64 sentences (e.g., “Luke and Laura got married on ‘General Hospital’”) from seven domains of expertise (e.g., television), pairs who knew each other (natural pairs) did better than pairs who were randomly put together (impromptu pairs). The natural pairs divided the task by focusing on their own areas of expertise and relying on their partners to remember information related to their partner’s areas of expertise. This division of labor exemplifies what Wegner and colleagues (Wegner et al., 1985, 1991) have termed *transactive memory*, which consists of two main components: Individual memory systems and transactive processes that facilitate communication between these systems. Transactive processes rely on several *social* metacognitive, or mentalizing, components. These include personal knowledge (Gallagher and Frith, 2003; Ross and Olson, 2010; Rouse et al., 2024)—the knowledge we have about our own and others’ cognitive abilities (also known as social semantic memory)—and theory of mind (Gallagher and Frith, 2003; Leslie, 1987; Premack and Woodruff, 1978), namely, the ability to adopt perspectives other than our own by forming alternative, decoupled representations of what others might believe or know about a situation. Nevertheless, transactive cognition is not synonymous with metacognition, as some *individual* metacognitive components are not directly engaged in transactive processes, such as metacognitive monitoring, which refers to the assessment of one's own cognitive abilities based on the private experience with a task (Flavell, 1979). We will hereafter use the term *social reasoning* to refer to this social metacognitive component (Jack et al., 2013). At the cerebral level, the social reasoning network has been described as involving lateral temporal and parietal regions, as well as medial frontal and parietal regions. Three main regions have been identified as playing a particular role: The bilateral temporal pole for person knowledge, and the bilateral temporo-parietal junction and anterior medial prefrontal cortex for theory of mind (Gallagher and Frith, 2003; Molenberghs et al., 2016; Van Overwalle and Baetens, 2009). Notably, evidence suggests that the social reasoning network may be supported by the brain's task-negative or default mode network (Buckner and Carroll, 2007).

Wegner's work suggests that people are adept at identifying experts within a group and potentially delegating specific tasks to them. However, identifying these experts can also be valuable for asking them to supervise a given task by providing demonstrations, offering advice, or even scaffolding—in other words, by teaching, which refers to behavior that aims to facilitate learning in others (Caro and Hauser, 1992; Kline, 2015). A large body of literature on social learning and culture has shown that humans and nonhumans can follow multiple strategies to select appropriate models from whom to learn [e.g., copying prestigious models or copying the majority (Kendal et al., 2018; Laland, 2004)]. Some have argued that humans can explicitly engage in these strategies, making them metacognitive (Dunstone and Caldwell, 2018; Heyes, 2016). Support for this claim comes from studies on ‘who’ strategies, which have revealed that participants can report their strategies after the experiment (Diaconescu et al., 2014; Heyes, 2016). Thus, social reasoning is recruited whenever an individual seeks help from an expert, whether in a context of delegation or teaching.

Wegner's concept of transactive memory focuses on how people can use others'—mainly episodic—memory. Nevertheless, there are as many

forms of transactive cognition as there are forms of cognition, with transactive processes being the common denominator. Thus, when someone seeks an expert for a technical activity such as making a bow or performing a surgical intervention, the targeted cognitive domain is not memory but technical cognition. This gives rise to the concept of *transactive technical cognition*. Technical cognition can be defined as the ability to shape the physical world in an instrumental way to meet one's ends (Osiurak, Lesourd, et al., 2020). Neuropsychological and neuroimaging studies have revealed that this ability might be primarily supported by a specific form of causal reasoning oriented toward the physical world, also called *technical reasoning* (Osiurak et al., 2023; Osiurak and Reynaud, 2020). Microsociety experiments have shown that technical reasoning skills strongly predict cumulative performance in technical tasks (De Oliveira et al., 2019; Osiurak, De Oliveira, et al., 2020; Osiurak et al., 2016). Evidence also indicates that the improvement of a physical system over generations is accompanied by an increase in its understanding ([Osiurak et al., 2021, 2022) but see (Derex et al., 2019)]. Taken together, these findings highlight that technical reasoning plays a major role when individuals engage in technical activities, either alone or collectively. At the cerebral level, the technical reasoning network includes areas in the dorsal parietal and lateral prefrontal cortices, with two main brain areas that could play a more central role, namely the left area PF (Parietal F) and the left inferior frontal gyrus (Reynaud et al., 2016, 2019). The term mechanical reasoning network has also been used to describe this network, which has been found to share neural regions and processes with the task-positive network (Jack et al., 2013) that includes, among other areas, the bilateral dorsolateral prefrontal cortex, a region recognized for its role in metacognitive monitoring. (Fleming and Dolan, 2012).

The goal of the present study was to provide insights into the processes underlying transactive technical cognition, thus contributing to the development a cognitive framework for how individuals engage in collaborative technical tasks. To achieve this, we designed two experiments: A behavioral study with 30 participants and an fMRI study with 29 participants. In these experiments, participants were asked to estimate the probability of success in completing technical tasks (do-it-yourself [DIY] home improvement or gardening) in four conditions (Fig. 1): Alone, Tutorial (i.e., following a Web tutorial), Teaching (i.e., under the supervision of an expert), and Delegation (i.e., the expert performs the task independently). A detailed description of the expert was provided to the participants before the experiment. This description emphasized that the expert was a self-taught individual, highly skilled in DIY home improvement (expert-compatible stimuli) but with a dislike for gardening (expert-incompatible stimuli). Participants' technical reasoning skills were also measured independently with a psychotechnical test.

A preliminary hypothesis was that the Alone condition would primarily engage the technical reasoning network, confirming that our task could target technical cognition. The hypotheses associated with the Delegation and Teaching conditions were more directly relevant to transactive technical cognition. Three hypotheses could be formulated, each assigning different roles to the social reasoning network. The first is the null hypothesis, which assumes that thinking about how to solve a technical task, whether alone or by consulting an expert, preferentially activates the technical reasoning network. The second is the exclusive hypothesis, which posits that the technical reasoning network is primarily activated when someone thinks about how to solve a technical task alone, while the social reasoning network is primarily recruited when someone thinks about how to solve a technical task by soliciting an expert in both the delegation and teaching contexts. The third is the integrative hypothesis, which postulates that the exclusive hypothesis holds true only for delegation contexts, while in teaching contexts, individuals preferentially activate both the technical social reasoning networks because these contexts require integrating information from both cognitive domains. The literature on collaborative cognition can help us assess the plausibility of these hypotheses. A significant body of
<figure><img src="image_3.png" /><figcaption>**Fig. 1.** Experimental tasks. (A) The main experiment involved estimating the probability of success in completing technical tasks (DIY home improvement or gardening) in four conditions: Alone, Tutorial (i.e., following a Web tutorial), Teaching (i.e., under the supervision of an expert), and Delegation (i.e., the expert performs the task alone). A detailed description of the expert was provided to the participants before the experiment, emphasizing that the expert was self-taught and highly skilled in DIY home improvement (expert-compatible stimuli) but with a dislike for gardening (expert-incompatible stimuli). In the behavioral experiment, the participants made their judgment on a continuous scale, while in the fMRI experiment, they used a 3-point scale. (B) Participants' technical reasoning skills were also assessed independently (and outside the scanner in the fMRI experiment) with a psychotechnical test. In this test, the participants were presented with four different situations and had to choose the correct one or the most effective one. For each item, a brief description of the problem was provided (e.g., for the wheelbarrow item: "Which picture shows the least effortful way of transporting the sack?"; for the boat item: "Which picture shows the tractor-boat coupling that allows the boat to go straight?").</figcaption></figure>

work has shown that people spontaneously confuse their own abilities with those of experts when they believe they have access to the experts' knowledge, a psychological bias known as the community of knowledge (Sloman and Rabb, 2016; Wilson and Keil, 1998). Therefore, individuals should preferentially recruit their social reasoning network to think about how to solve a technical task when collaborating with an expert, thus ruling out the null hypothesis. Empirical testing remains necessary to distinguish the exclusive hypothesis from the integrative hypothesis.

Finally, we explored the collaboration with a “nonhuman” partner, specifically following a Web tutorial, thereby contributing to the growing literature on how people spontaneously extend their cognition into the physical environment, including technologies (Fernandez-Velasco and Spiers, 2024; Risko and Gilbert, 2016; Sparrow et al., 2011). The Internet plays a crucial role in our evolving world, progressing through Web 1.0, 2.0, 3.0, and beyond—a world in which

people build desks by following YouTube tutorials. The question was whether participants would behave in the Tutorial condition as they did in the Alone, Teaching, or Delegation conditions. Given that following a Web tutorial can be viewed as a mediated teaching episode, we hypothesized that participants would behave in this condition more like they would in the Teaching condition rather than the Alone or Delegation conditions.

## 2. Results

### 2.1. Behavioral results

Fig. 2a illustrates participants' estimated success across the four conditions for the behavioral experiment. We conducted separate analyses for expert-compatible and expert-incompatible stimuli. For expert-

<figure><img src="image_4.png" /><figcaption>**Fig. 2.** Behavioral results. Estimation of success in the behavioral experiment (A) and the fMRI experiment (B) as a function of experimental conditions and the compatibility of the technical task with the expert's skills. Expert-compatible stimuli corresponded to DIY home improvement stimuli, while expert incompatible stimuli were gardening stimuli. A linear effect Alone < Tutorial < Teaching < Delegation was observed in both experiments for expert-compatible stimuli, but not for expert-compatible stimuli. Boxplots indicate the upper quartile, median and lower quartile, with whiskers extending to the most extreme data point that is no >1.5 times the interquartile range from the edge of the box. (C) Correlations between success estimation in the four experimental conditions and the performance on the psychotechnical test. Significant correlations were found between the Alone, Tutorial, and Teaching conditions as well as with the performance on the psychotechnical test, suggesting a common cognitive process, potentially related to technical reasoning. Estimation in the Delegation condition was also correlated with that in the Teaching condition, indicating that the estimation in both conditions may be based on a metacognitive representation of the expert. All the r > 0.24 are significant at p < .05. Aln: Alone; Ttr: Tutorial; Tch: Teaching; Dlg: Delegation; Psy: Psychotechnical test).</figcaption></figure>
compatible stimuli, we found a significant main effect of condition, *F* (3,87) = 72.93, *p* < .001, *η*² = 0.72. Post-hoc pairwise tests revealed that estimated success was lower in the Alone condition than in the Tutorial condition, which was in turn lower than the Teaching condition, itself lower than the Delegation condition (all *p* < .036, Cohen's *d* between 0.85 and 3.72). A significant effect of condition was also observed for expert-incompatible stimuli, *F*(3,87) = 37.43, *p* < .001, *η*² = 0.56. Post-hoc pairwise tests indicated that estimated success was lower in the Delegation condition than in the other three conditions (all *p* < .001, Cohen's *d* between 2.03 and 2.49). No other significant differences were found. The opposite pattern observed for the Delegation condition between expert-compatible and expert-incompatible stimuli, as well as the higher estimation in the Teaching condition compared to the Alone condition for expert-compatible stimuli but not for expert-incompatible stimuli, confirmed that participants considered the expert's level of expertise when estimating the success. The key finding of this first experiment is the linear increase in success for expert-compatible stimuli: Alone < Tutorial < Teaching < Delegation. We tested this linear trend with a linear mixed model (LMM) in the fMRI experiment, with condition treated as a numeric variable. As shown in Fig. 2b, very similar behavioral results were obtained in this second experiment, suggesting that our paradigm was robust and resistant to experimental variations, such as the range of possible responses (0 to 100 in the behavioral experiment versus a three-point scale in the fMRI experiment). The LMM analysis confirmed the linear increase in success (Alone < Tutorial < Teaching < Delegation), *F*(1,86) = 152.67, *p* < .001, *η*² = 0.64.

We pooled together the data from the two experiments and performed Pearson correlation analyses between performance on the psychotechnical test and the four experimental conditions for expert-compatible stimuli only. These analyses revealed significant correlations with the Alone, Tutorial, and Teaching conditions (all *r* > 0.24, *p* < .035). Significant positive correlations were also found between estimations in the Alone, Tutorial, and Teaching conditions (all *r* > 0.70, *p* < .001). Additionally, a positive significant correlation was reported between estimated success in the Teaching and Delegation conditions (*r* = 0.24, *p* = .031). As summarized in Fig. 2c, these results suggest that estimations in the Alone, Tutorial, and Teaching conditions share a common cognitive determinant, which could be participants' technical reasoning skills, as indicated by the link with performance on the psychotechnical test. In contrast, estimated success in the Delegation condition did not seem to be influenced by technical reasoning skills, as it was independent of both performance on the psychotechnical test and estimation in the Alone condition. Finally, estimation in the Teaching condition appeared to be multidetermined, given its links with both Alone condition and, to a lesser extent, the Delegation condition. To test the idea that estimation in the Teaching condition could be determined by the participants' representations of both their own and the expert's technical skills, we compared three distinct LMMs using estimation in the Teaching condition as the outcome variable: An Alone-only model, a Delegation-only model, and an Alone*Delegation model. This analysis revealed that the Alone*Delegation model was a better fit for our data compared to the Alone-only and Delegation-only models (*χ*² = 52.20, *p* < .001), confirming that estimation in the Teaching condition was based on participants' representation of both their own and the expert's technical skills.

## 2.2. fMRI results

The fMRI experiment allowed us to explore the level of engagement in the technical and social reasoning networks across the four experimental conditions. As mentioned above, four regions of interest (ROIs) were associated with the technical reasoning network, namely the left area PF, the left inferior frontal gyrus and the bilateral dorsolateral prefrontal cortex, and five ROIs were associated with the social reasoning network, namely the bilateral temporal pole, the bilateral

temporo-parietal junction and the anterior medial prefrontal cortex. We conducted LMM analyses for each of the ROIs mentioned above and examined the presence of a linear effect: Alone < Tutorial < Teaching < Delegation. As shown in Fig. 3 and Table S1, this linear effect was negative and statistically significant for three of the four ROIs in the technical reasoning network (all *F*(1,86) > 7.01, all *p* < .010, *η*² between 0.07 and 0.15). A non-significant negative linear effect was also found for the left inferior frontal gyrus (*F*(1,86) = 3.13, *p* = .080). By contrast, this linear effect was positive and significant for all the ROIs in the social reasoning network (all *F*(1,86) > 4.82, all *p* < .031, *η*² between 0.05 and 0.21). All pairwise comparisons between the four conditions are provided in Table S2, demonstrating that the linear effect was driven by differences among the conditions. The whole-brain univariate analyses for the Alone > Delegation contrast and the Delegation > Alone contrast further illustrated this linear effect, with greater recruitment of the technical reasoning network for the Alone > Delegation contrast and greater recruitment of the social reasoning network for the Delegation > Alone contrast (Fig. 4). This finding rules out the null hypothesis, which predicts the primarily involvement of technical reasoning across the four conditions. Moreover, this finding seems to support the integrative hypothesis rather than the exclusive hypothesis, suggesting that technical reasoning and social reasoning might work together even more closely in the Tutorial and Teaching conditions.

We conducted functional connectivity analyses to further distinguish between the two hypotheses. To reduce the number of potential correlations, we focused only on the ROIs of the left hemisphere for the dorsolateral prefrontal cortex, the temporal pole, and the temporo-parietal junction. As shown in Fig. 5 and Table S3, these analyses revealed two main clusters across the four conditions, one consisting of positive connections between the brain areas associated with technical reasoning (all *F*(2,27) > 20.91, all *p* < .001, *η*² between 0.61 and 0.78) and the other consisting of positive connections between the brain areas associated with social reasoning (all *F*(2,27) > 92.01, all *p* < .001, *η*² between 0.87 and 0.92). The presence of these two clusters, even in the Alone and Delegation conditions, suggests that the brain areas within each cluster functioned synchronously, irrespective of their level of activation. Importantly, a third cluster emerged in the Tutorial and Teaching conditions, but not in the Alone and Delegation conditions (both *F*(2,27) > 3.55, both *p* < .043, both *η*² > 0.21). This cluster primarily included negative connections between, on the one hand, brain areas involved in technical reasoning, and, on the other hand, brain areas involved in social reasoning. Overall, participants in the Tutorial and Teaching conditions recruited an interconnected network that integrated both technical and social reasoning. The presence of negative connections might indicate back-and-forth exchanges between the technical and social reasoning networks, exchanges that could be anti-correlated, with each network successively inhibiting the other, as previously observed with the anti-correlation between the task-positive network and the default-mode network—two networks closely related to the technical and social reasoning networks (Jack et al., 2013).

## 3. Discussion

To sum up, our results support the integrative hypothesis and rule out both the null hypothesis and the exclusive hypothesis. When people estimate their likelihood of success in undertaking a technical task alone, they preferentially engage their technical reasoning. When they estimate their success by delegating the task to an expert, they preferentially recruit their social reasoning. When they estimate their success while being taught by an expert or assisted by a tutorial, they follow an intermediate strategy that involves a stronger integration of both technical and social reasoning. In the next sections, we discuss how these findings open new avenues for future research on transactive technical cognition.

The first key finding is that the estimation of success in the Alone condition is primarily driven by participants' technical reasoning skills.
<figure><img src="image_5.png" /><figcaption>**Fig. 3.** Mean BOLD parameter estimates as a function of experimental conditions and regions of interest (ROIs). We tested the presence of the linear effect: Alone < Tutorial < Teaching < Delegation. The results are negative and statistically significant linear effect for the four ROIs of the technical reasoning network (left area PF, left IFG and bilateral dorsolateral prefrontal cortex). In contrast, the linear effect was positive and statistically significant for all ROIs within the social reasoning network (bilateral temporal pole, bilateral temporo-parietal junction, and anterior medial prefrontal cortex). Boxplots indicate the upper quartile, median and lower quartile, with whiskers extending to the most extreme data point that is no >1.5 times the interquartile range from the edge of the box. PF: Parietal F; IFG: inferior frontal gyrus; PFC: Prefrontal cortex; TPJ: Temporo-parietal junction; Reason.: Reasoning; Left: Left hemisphere; Right: Right hemisphere.</figcaption></figure>

This conclusion is supported by the association with performance on the psychotechnical test and the recruitment of the technical reasoning network, specifically the left area PF. Evidence suggests that brain damage to this region results in difficulties not only in using common tools but also in making or using novel tools to solve mechanical problems (Goldenberg and Spatt, 2009; Osiurak et al., 2023). Neuroimaging studies have reported that this brain area is preferentially activated when people reason about tool-use actions (Reynaud et al., 2016), understand physical events (Fischer et al., 2016), or watch others using or making tools (Reynaud et al., 2019). Moreover, individuals' performance on psychotechnical tests can be predicted by individuals' cortical thickness of the left area PF (Federico et al., 2022). Nevertheless, no study so far has demonstrated that technical reasoning plays a role in prospective judgment, that is, in estimating the likelihood of success in undertaking a technical task. The present study provides evidence that, even in this situation, people reason about the activity in a technical way and rely on the metacognitive experience associated with it to make their estimation of success.

The second key finding is that the participants preferentially engaged their social reasoning to estimate the success of the activity in the Delegation condition. The estimation of success in this condition was not derived from participants' technical reasoning skills as stressed by the lack of association with the performance on the psychotechnical test. There was also lesser recruitment of the technical reasoning network in this condition, which indicates that the participants seemed to partially suspend this form of reasoning. In a way, this resembles the

phenomenon of cognitive offloading (Risko and Gilbert, 2016), which here describes the avoidance of unnecessary effort in processing technical information that will be managed by the expert. This cognitive offloading could have taken place because of the participants' ability to take the expert's perspective rather than their own, as corroborated by the greater recruitment of the anterior medial prefrontal cortex and temporo-parietal junctions known for their involvement in perspective taking. Nevertheless, taking the perspective of another person is not sufficient to estimate their potential success in a technical task. What is also needed is some information about their technical skills, which can be obtained through person knowledge. The activation of temporal poles in the Delegation condition confirms the recruitment of this knowledge. In broad terms, when people intend to delegate an activity, they think more about "who" than "how" to perform the activity to estimate its success. This metacognitive strategy requires possessing knowledge about others and notably their domains of expertise (Lutz and Keil, 2002) or "function", in the same way as we know the function of artifacts (Kelemen, 1999).

The third key finding is that the integration of the technical and social reasoning networks seemed to be more pronounced to estimate the success of undertaking the activity in the Teaching condition. Social reasoning, and particularly theory of mind, helps the teacher assess the knowledge gap that separates them from the learner, leading them to select appropriate elements to be demonstrated, track the learner's belief, and provide appropriate feedback (Corriveau et al., 2018; Ronfard and Corriveau, 2016). Consistent with this, a recent neuroimaging
Alone > Delegation

<figure><img src="image_6.png" /><figcaption>**Fig. 4.** Whole-brain univariate results for the Alone > Delegation contrast and the Delegation > Alone contrast. The Alone > Delegation contrast shows greater activation in brain regions associated with technical reasoning (left area PF and right dorsolateral prefrontal cortex), whereas the Delegation > Alone contrast revealed stronger activation in brain regions linked to social reasoning (bilateral temporal pole, bilateral temporo-parietal junction, and anterior medial prefrontal cortex). PF: Parietal F.</figcaption></figure>

Delegation > Alone

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

<figure><img src="image_8.png" /><figcaption>**Fig. 5.** Functional connectivity results within the 6 ROIs for each experimental condition. The analysis revealed two primary clusters across all four conditions, one involving positive connections between the brain areas associated with technical reasoning (left area PF, left inferior frontal gyrus, and left dorsolateral prefrontal cortex) and another involving positive connections between the brain areas linked to social reasoning (left temporal pole, left temporo-parietal junction, and anterior medial prefrontal cortex). The presence of these two clusters, even in the Alone and Delegation conditions, suggests that the brain regions in each cluster functioned synchronously, regardless of their level of activation. A third cluster emerged in the Tutorial and Teaching conditions, but not in the Alone and Delegation conditions. This cluster mainly comprised negative connectivity between brain regions involved in technical reasoning and those involved in social reasoning. The color gradient of edges represents connectivity strength, ranging from negative (dark blue) to positive values (dark red). Only significant connectivity values are shown, with p values corrected using False Discovery Rate (FDR).</figcaption></figure>
study reported the activation of the anterior medial prefrontal cortex in participants placed in a teaching situation (Vélez et al., 2023). Theory of mind might operate not only from the teacher's side but also from the learner's side, by allowing the learner to evaluate the knowledge gap that separates them from the teacher. This evaluation, which is essential to anticipate the success of a teaching episode, cannot be based only on social reasoning, as it requires the learner to integrate information about their own skills. Thus, it is the interaction of social and technical reasoning that enables the learner to produce a representation of the potential future teaching episode, as revealed in our study by the functional connectivity of the related networks in the Teaching condition. These findings open new avenues for understanding the cognitive bases of social learning strategies. Evidence indicates that people are subject to social learning biases such as the prestige bias or the conformist bias to decide whom to learn from (Henrich and Gil-White, 2001; Muthukrishna et al., 2016). Some have suggested that these strategies might be more explicit than we think (Heyes, 2016). They might be more metacognitive than heuristic. Our experimental paradigm was not designed to test social learning strategies such as the 'who' strategy. In our study, the participants estimated how successful the activity would be if they were taught by the expert. There was only one expert, so the participants did not have to form several representations of experts with different levels of expertise depending on the information available. Nevertheless, if we consider that adopting a 'who' strategy can also consist of estimating the success associated with a set of potential models before comparing them, then our experimental task starts to resemble a "whom to learn from" task minus the decision-making process. Our findings indicate that people recruit metacognitive processes to estimate the success of a teaching episode, supporting the metacognitive approach to social learning strategies. Neuroscientific research is still needed to directly explore the neural correlates of social learning strategies based on a priori hypotheses about the cerebral networks engaged when people adopt a heuristic versus a metacognitive social learning strategy.

The fourth key finding is that the participants seemed to behave in the Tutorial condition as they did in the Teaching condition, by recruiting the cerebral networks associated with social and technical reasoning in an integrative way. The Tutorial condition differed from the Teaching condition because of the absence of information about the nature and quality of the Web tutorial. This lack of information may explain why their estimation of success was lower in the Tutorial condition than in the Teaching condition. This difference could also result from the fact that the participants considered that following a Web tutorial was less effective than being taught by a physical teacher, who could adapt their teaching to them by providing appropriate feedback, for instance. This behavioral pattern could have been interpreted as the use of a simple heuristics consisting of estimating their own success, as in the Alone condition, and adding a gain due to the use of a Web tutorial. If so, the participants would have exhibited a pattern where technical reasoning seems more strongly recruited, without a real integration with social reasoning. Our fMRI results do not support this interpretation, as they indicate an integrative contribution of both technical and social reasoning networks in the Tutorial condition. This suggests that using a Web tutorial was processed by the participants as a potential teaching situation, even if it is mediated by technology. As in the Teaching condition, they seemed to estimate the knowledge gap that separated them from a potential tutorial found on the Internet. We did not debrief the participants on what kind of representation they had formed when imagining the use of a Web tutorial. Previous studies have indicated that people might represent the Internet as a supernormal or ideal partner, as its breadth and immediacy surpass any social partner (Fisher et al., 2015; Ward, 2013). Our findings stress that the representation people have about the Internet might occupy the same place as any social partner in our social reasoning system [for similar results on modern technologies, see (Lin et al., 2019)], which corroborates that transactive cognitive processes might be similar whether the partner be

social or technological, human or nonhuman.

Our understanding on how people reason about other people's minds in collaborative tasks requires answering the crucial question of what we *exactly* reason about. The present work sheds light on this question, particularly if we focus on the Teaching condition, which describes a potential direct collaboration between two partners. In this condition, we found that participants tended to use cognitive processes that allow them to obtain information about their own (technical reasoning) and the partner's competence (social reasoning). These findings corroborate a recent experimental and computational modeling study that showed collaborative decision making to be based on representations of our own and the partner's competence and effort (Xiang et al., 2023). Our work taken, together with this recent study, might offer new perspectives on testing whether specific networks are involved in specific aspects of collaborative reasoning (i.e., our own and the partner's competence and effort). It is also noteworthy that, in the Teaching condition, participants had to a priori estimate the success of the task if taught by the expert. However, this a priori estimation could recruit cognitive processes that differ from those involved during the teaching episode itself, which requires both the learner and the teacher to reason about each other's mind and update their beliefs (Shafto et al., 2014). Future research is needed to explore whether the cerebral network identified here in the Teaching condition is the same when learners are being taught by an expert.

Before concluding, we would like to address some limitations of our study. We did not debrief participants on their task strategies, which could have provided more direct and detailed information about participants' thoughts, helping to confirm our behavioral findings and the distinction between technical and social reasoning across conditions. Additionally, given the close correspondence between the technical and social reasoning networks and the task-positive and default-mode networks, respectively (Jack et al., 2013), an alternative interpretation is that differences in cognitive effort may account for our results. Specifically, the Alone condition may require greater effort to estimate task success, whereas the Delegation condition may be simpler, with the Tutorial and Teaching conditions falling in between. This logic could explain the apparent increase in engagement of the social reasoning network and the decrease in engagement of the technical reasoning network, insofar as this pattern might in fact reflect greater activation of the default-mode network and reduced activation of the task-positive network as cognitive effort diminishes. Although we lack fMRI evidence to directly test this hypothesis, response times can serve as a useful behavioral proxy of cognitive effort. Thus, one would expect longer response times in the Alone condition, somewhat shorter times in the Tutorial and Teaching conditions, and the shortest times in the Delegation condition. However, analyses of response times revealed no significant differences across conditions (see Fig. S1), providing partial support against an effort-based explanation.

Cumulative culture, the accumulation of modifications, improvements, and innovations over generations through social learning (Boyd et al., 2011; Paige and Perreault, 2024; Tomasello et al., 1993), has precipitated the expansion of more and more opaque artifacts and the complexification of societal organization, with individuals becoming more and more specialized in technical and non-technical domains (Ben-Oren et al., 2023; Migliano and Vinicius, 2022; Smolla and Akçay, 2019). This context could have favored the selection of specific cognitive processes that are useful to identify the knowledgeable individuals, if any, able to offer their help either by conducting a given activity alone or by teaching us how to perform the activity. Thus, some social reasoning components, such as person knowledge or semantic memory, more generally, could have developed as a by-product of cumulative culture, as a means of retrieving functional information about persons and artifacts [for a somewhat similar view, see (Csibra and Gergely, 2011)]. As illustrated by a recent report of an amputation surgery case dating to at least 31,000 years ago (Maloney et al., 2022), our ancestors were already equipped with such cognitive predispositions, allowing some of them to become experts in some specialized domains and to be solicited
by members of their group for this expertise. In fact, we know that early
lithic industries, such as the Acheulean dating from 1.6 million years
ago, were already complex and opaque in their manufacture (Shipton
and Nielsen, 2015), so that they could have marked the beginning of
specialization-based organization and elaborated forms of transmission
such as proto-gestural teaching (Gärdenfors and Högberg, 2017). If so,
an early version of the transactive technical cognition system described
in the present article could already have worked in our ancestors. Future
research in neuro-archaeology and cognitive archaeology is needed to
explore this avenue.

# 4. Materials and methods

## 4.1. Ethics

The study was conducted in accordance with the Declaration of Helsinki and was approved by a French Ethics Committee (N°ID-RCB: 2020-A02115-34). Informed consent was obtained from all participants after the nature and possible consequences of the study were explained.

## 4.2. Participants

The study was conducted in the Laboratory for the Study of Cognitive Mechanisms at the University of Lyon (Lyon, France) and in the Lyon Neuroimaging Department (CERMEP, Lyon, France). For both experiments, participants were randomly recruited through advertisements posted on social media websites. One week before the neuroimaging session (fMRI experiment), the participants were interviewed by a medical doctor to confirm their eligibility for the neuroimaging session. In both experiments, all the participants were right-handed, had a normal or corrected-to-normal vision, and reported no history of neurological or psychiatric disorder. The participants in fMRI experiment were offered a monetary incentive for their time (60€). Thirty participants ($M_{age} = 26.40, SD = 9.91$, range: 20 to 53; female gender, $n = 19$, male gender, $n = 11$) took part in the behavioral experiment and 29 new participants ($M_{age} = 20.86, SD = 5.01$, range: 18 to 40; female gender, $n = 20$, male gender, $n = 9$) in the fMRI experiment. Inclusion in the final sample required that head motion during scanning did not exceed 0.5 mm displacement (i.e., framewise displacement) between consecutive volumes on 90 % of volumes. No participants were excluded based on this criterion. The sample size of both experiments was estimated based on a pilot study ($n = 10$) using pwr package (Champely et al., 2018) in R (R Development Core Team, 2011). The analysis is available at https://osf.io/vebpj/. The pilot study was similar to the behavioral experiment (see below). We found that the effect size of ‘condition’ (Alone versus Tutorial versus Teaching versus Delegation) on ‘Estimation’ was $\eta^2 = 0.76$. Accepting a maximum type I error of $\alpha = 5 \%$ and a power of (1-$\beta$) = 80 %, the required sample size was $n = 8$. Our monetary constraints for the fMRI experiment allowed us to reach a sample size of $n = 30$. Therefore, we decided to recruit 30 participants in the behavioral experiment and 30 participants in the fMRI experiment, with the rationale that these samples should be large enough to replicate the effect of ‘condition’ on ‘estimation’ at a behavioral level in both experiments. Nevertheless, we acknowledge that a statistical power analysis for the neuroimaging study would have been preferable, as behavioral effect sizes are not reliable predictors of neuroimaging effect sizes. The absence of such an analysis represents a limitation of our study.

## 4.3. Methods and procedure

### 4.3.1. Behavioral experiment

The experiment was conducted using Open Sesame software (Mathôt et al., 2012) and is available at https://osf.io/vebpj/. As shown in Fig. 1, the participants had to estimate the probability of success in achieving technical tasks (DIY home improvement or gardening) in four

conditions: Alone, Tutorial (i.e., following a Web tutorial), Teaching (i. e., under the supervision of an expert – the expert does not perform the task), and Delegation (i.e., the expert performs the task alone). A detailed description of the expert was provided to the participants before the experiment. This description emphasized that the expert was self-taught, highly skilled in DIY home improvement (expert-compatible stimuli) but with a dislike for gardening (expert-incompatible stimuli). This description along with the instructions is available at https://osf.io/vebpj/. There were 12 blocks, with 3 blocks for each condition (3 blocks x 4 conditions = 12 blocks). Each block comprised 4 expert-compatible stimuli and 1 expert-incompatible stimulus, which were presented randomly. Thus, during the experiment, the participants had to make their judgment on the same 12 expert-compatible stimuli and 3 expert-incompatible stimuli for each condition. The stimuli represented two pictures “before/after” depicting a DIY home improvement or a gardening scene (Fig. 1). The procedure was as follows. Before each block, the participants were informed about the condition with a symbol representing it (Fig. 1). This symbol remained on the screen for 15 s. Then, the 5 stimuli of the block were shown. For each stimulus, a bar with 101 discrete unnumbered graduations was present below the two pictures “before/after” (from 0 [left] to 100 [right]). The participants had to make their response by clicking with a computer mouse on the graduation of their choice. There was a 1-second response-stimulus interval (a black screen). The order of conditions was not random. There were two different orders: Alone, Tutorial, Teaching, and Delegation; Delegation, Teaching, Tutorial, and Alone. Four blocks (one for each condition) of two stimuli (one expert-compatible and one expert-incompatible) were given as training examples to be sure that the participants understood the task before starting it. The stimuli used for this training differed from those of the experiment. After the experimental task, participants’ technical reasoning skills were measured with a psychotechnical test, which is one of the subtests of the NV7 battery (Fig. 1). This test comprises 24 items. Each item corresponds to 4 pictures depicting 4 physical situations. The participants had to select the picture representing the most physically effective situation among the four pictures. For each item, a brief sentence described the problem to be solved (see Fig. 1; e.g., for the wheelbarrow item: “Which picture shows the least effortful way of transporting the sack?”; for the boat item: “Which picture shows the tractor-boat coupling allowing the boat to go straight?”). The participants had 10 min to complete the greatest number of items as possible. As this battery is commercialized (Pearson website), we did not provide the items in an open-access repository. Nevertheless, the items can be available on request. As the original items of the NV7 battery, the items used in the study were in black and white. The pictures shown in Fig. 1 are nevertheless deliberately in color so as to move further away from the original items.

### 4.3.2. fMRI experiment

The behavioral task performed by the participants in the scanner (Fig. 1) was similar to the one completed by the participants in the behavioral experiment, with the exception of a few differences that are stressed below. There were 15 and not 12 blocks because we added a control condition called “camera” in which the participants had to estimate the similarity of viewpoint between the two pictures, i.e., the greater the two photographs seemed to be taken with the same camera’s angle, the more they were considered as similar. As for the behavioral experiment, each block comprised 4 expert-compatible stimuli and 1 expert-incompatible stimulus, which were presented randomly. Thus, the participants made their judgment on the same 12 expert-compatible stimuli and 3 expert-incompatible stimuli for each condition. The stimuli as well as the training examples were the same as those used in the behavioral experiment. As for the behavioral experiment, before each block, the participants were informed about the condition with a symbol representing it. This symbol remained on the screen for 15 s. Then, the 5 stimuli of the block were shown. Each trial started with a 2–3-second blank screen, followed by the stimulus for 12 s, then a new
0.5–1.5-second blank screen, and the 3-second response screen. The response screen remained on the screen for 3 s even if the participants gave their response before the time limit. The participants answered by pressing either the left (≈ 0 % of success or no success), middle (≈ 50 % of success or partial success) or right button (≈ 100 % of success or full success) of the button box. Contrary to the behavioral experiment, the order of conditions was random. After the experimental task, participants' technical reasoning skills were measured with the same psychotechnical test as described above (Fig. 1). However, contrary to the behavioral experiment, the participants had 5 and not 10 min to complete the greatest number of items as possible.

## 4.4. Behavioral analyses

Missing data were coded as NA. For the behavioral experiment (n = 30), we used regression modeling in R (R Development Core Team, 2011) [lmerTest package (Kuznetsova et al., 2017)] to fit a linear model with 'estimation' as outcome variable, 'condition' (Alone versus Tutorial versus Teaching versus Delegation) as fixed effect, and 'participant's identity' as random effect. We fitted this linear model first with the expert-compatible stimuli and then with the expert-incompatible stimuli. For both analyses, the 'condition' variable was treated as a qualitative variable. Post-hoc pairwise t-tests were computed with a Holm-Bonferroni correction. For the fMRI experiment (n = 29), we also fitted a linear model with 'estimation' as outcome variable, 'condition' (Alone versus Tutorial versus Teaching versus Delegation) as fixed effect, and 'participant's identity' as random effect. However, for the expert-compatible stimuli, we treated the 'condition' variable as a numeric variable to test the linear effect Alone < Tutorial < Teaching < Delegation. Thus, Alone, Tutorial, Teaching, and Delegation conditions were recoded 1, 2, 3, and 4, respectively. Participants of both experiments were pooled together (n = 59) for correlational analyses, which were performed using Pearson correlation coefficients. These analyses included the estimation given in the four conditions (Alone, Tutorial, Teaching, and Delegation) as well as the performance on the psychotechnical test. As explained above, the time limit for the psychotechnical test was not the same for each experiment (Behavioral: 10 min; fMRI: 5 min). Therefore, we transformed each set of data by using the z-score and created a standard score with a mean of 100 and a standard deviation of 15. Finally, for each experiment, we also fitted a linear model with 'Response time' as outcome variable, 'condition' (Alone versus Tutorial versus Teaching versus Delegation) as fixed effect, and 'participant's identity' as random effect. Post-hoc pairwise t-tests were computed with a Holm-Bonferroni correction. Statistical significance was set at *p* < .05.

## 4.5. fMRI analyses

### 4.5.1. fMRI data acquisition and preprocessing

Neuroimaging data were obtained using a 3T Siemens Prisma Scanner (Siemens, Erlangen, Germany) equipped with a 64-channel head coil. Blood-Oxygen Level Dependent (BOLD) images were captured using T₂\*-weighted echo-planar imaging (EPI) with a multi-band sequence. Functional images were acquired as oblique-axial scans aligned with the anterior commissure-posterior commissure (AC-PC) line, utilizing the following parameters: 57 slices, TR/TE = 1400/30 ms, flip angle = 70°, field of view = 96×96 mm², slice thickness = 2.3 mm, voxel size = 2.3 × 2.3 × 2.3 mm³, multiband factor = 2. Structural T₁-weighted images were collected using an MPRAGE sequence (224 sagittal slices, TR/TE = 3000/2.93 ms, inversion time = 1100 ms, flip angle = 8°, 224×256 mm FOV, slice thickness = 0.8 mm, voxel size = 0.8 × 0.8 × 0.8 mm³). To optimize analytical accuracy, we preprocessed fMRI data twice: Once for the univariate analysis to enhance voxel-wise statistical testing, and once for functional connectivity analysis to accurately assess time-series correlations and reduce physiological noise. This dual approach aimed at ensuring maximal

sensitivity and specificity for detecting distinct neural activities pertinent to each analysis type (see below).

### 4.5.2. ROI selection

Nine spherical ROIs with a radius of 10 mm were created in the MNI standard space (Table S4). We used literature-defined ROIs to ensure the independence of ROI selection. All of them were based on fMRI studies (activation peaks). Four ROIs were associated with the technical reasoning network, i.e., the left area PF, the left inferior frontal gyrus, and the bilateral dorsolateral prefrontal cortex. Five were associated with the social reasoning network, i.e., bilateral temporal pole for person knowledge, and bilateral temporo-parietal junction and anterior medial prefrontal cortex for theory of mind. To reduce the number of potential correlations in the functional connectivity analysis, we restricted our analysis to the ROIs of the left hemisphere when the brain region of interest was bilateral, i.e., dorsolateral prefrontal cortex, temporal pole, and temporo-parietal junction. As a result, our functional connectivity analysis focused only on ROIs of the left hemisphere except for the anterior medial prefrontal cortex for which the ROI was bilateral given its medial position (x = 4) and the diameter of the sphere (10 mm).

### 4.5.3. fMRI univariate analyses

Univariate analyses were conducted using the fMRI Expert Analysis Tool (FEAT) version 6.00, part of FSL (FMRIB's Software Library). Preprocessing included slice timing correction, linear motion correction (Jenkinson et al., 2002), high-pass temporal filtering (sigma = 25 s), brain extraction (Jenkinson et al., 2002), and linear co-registration to the corresponding T₁-weighted image followed by linear registration to MNI152 standard space (Jenkinson and Smith, 2001). Spatial smoothing was performed using a Gaussian kernel with a full-width at half-maximum (FWHM) of 6 mm, and grand-mean intensity normalization of the entire 4D dataset was achieved using a single multiplicative factor. The preprocessed time series data were modeled using FMRIB's Improved Linear Model (FILM) with local autocorrelation correction (Woolrich et al., 2001), incorporating all the experimental conditions of interest (i.e., Camera, Alone, Tutorial, Teaching, and Delegation) and confounds, such as motion parameters. The data from both expert-compatible and expert-incompatible stimuli were analyzed together. To extract mean BOLD parameter estimates from the ROIs (see ROI selection above) at the participant level, a custom-developed Python script was employed. This script interfaced with featquery, a component of the FMRIB Software Library, to process data coming from the participant-level FSL analyses. For each participant, the script executed commands to retrieve data from predefined ROIs. The extraction was tailored to specific contrasts of parameter estimates corresponding to different experimental conditions (i.e., Alone, Tutorial, Teaching, and Delegation). Then, a mixed-effects linear regression model was applied to the extracted data. The dependent variable in the model was the "mean parameter estimate" for each ROI. Fixed effects included the experimental "condition", coded numerically to reflect hypothesized linear relationships between conditions (Alone = 1, Tutorial = 2, Teaching = 3, Delegation = 4), allowing us to test the progression effect from Alone through Delegation. The "participant's identity" was included as a random effect to account for intra-individual variations across the experimental conditions. Moreover, we also conducted two whole-brain univariate analyses that targeted two contrasts: Alone > Delegation and Delegation > Alone. The analyses involved thresholding the Z (Gaussianised T) statistic images at a primary threshold of *p* = .01 at the participant level. At the group level, a fixed effects model was implemented using FMRIB's Local Analysis of Mixed Effects (FLAME), with the random effects variance set to zero (Beckmann et al., 2003). Both stages 1 and 2 of FLAME were implemented. Group-level analyses were cluster-corrected using a Z-statistic threshold of 2.4 to define contiguous clusters, with multiple comparisons controlled using Gaussian Random Field Theory at a threshold of *p* < .001.
### 4.5.4. Functional connectivity analyses

Functional connectivity analyses were performed using the Functional Connectivity Toolbox version 22a in MATLAB version R2024a (Nieto-Castanon, 2020; Whitfield-Gabrieli and Nieto-Castanon, 2012). Preprocessing was conducted using the standard CONN preprocessing pipeline, which encompassed several steps: (i) functional realignment and unwarp; (ii) slice-timing correction; (iii) outlier detection through ART-based scrubbing; (iv) direct segmentation and normalization to the Montreal Neurological Institute (MNI) reference space; (v) Gaussian smoothing with an 8-mm full-width at half-maximum (FWHM). Subsequently, the data underwent denoising via CONN's default procedures, including linear detrending, de-spiking, and bandpass filtering within a range of 0.008 Hz to 0.09 Hz. ROI-to-ROI connectivity matrices were computed to evaluate functional connectivity among predefined ROIs (see *ROI selection* above), using Fisher-transformed bivariate correlation coefficients from a weighted GLM. This model accounted for task-specific effects, modeled with a canonical hemodynamic response function. At the group level, connectivity measures were analyzed using a GLM, treating connection-level measures as dependent variables and group identifiers as independent variables. Multivariate parametric statistics were applied with random effects to account for inter-subject variability and repeated measures. Inferences were made on individual clusters identified using a hierarchical clustering method, based on anatomical proximity and functional similarity. Results were thresholded using a connection-level *p*-value of < 0.05 and a cluster-level familywise error rate controlled at *p*-FDR < 0.05.

## Data and code availability

The data that support the findings of this study and the codes used in this study are available at https://osf.io/vebpj/.

## CRediT authorship contribution statement

**François Osiurak:** Writing – original draft, Visualization, Validation, Supervision, Resources, Project administration, Methodology, Investigation, Funding acquisition, Formal analysis, Data curation, Conceptualization.
**Chloé Bryche:** Writing – review & editing, Methodology, Investigation.
**Maximilien Metaireau:** Writing – review & editing, Methodology, Investigation.
**Alexandre Bluet:** Writing – review & editing, Methodology, Investigation.
**Camille Gramaje:** Writing – review & editing, Methodology, Investigation.
**Josselin Baumard:** Writing – review & editing, Methodology.
**Yves Rossetti:** Writing – review & editing, Methodology, Investigation.
**Mathieu Lesourd:** Writing – review & editing, Methodology, Conceptualization.
**Giovanni Federico:** Writing – original draft, Visualization, Validation, Methodology, Formal analysis, Data curation, Conceptualization.

## Declaration of competing interest

The authors report no potential financial conflicts of interest.

## Acknowledgments

This work was supported by grants from the French National Research Agency (ANR; Project TECHNITION: ANR-21-CE28-0023-01; F.O., Y.R., and M.L.) and the Région Auvergne-Rhône-Alpes (NUMERICOG-2017-900-EA 3082 EMC-R-2075; F.O.).

## Supplementary materials

Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.neuroimage.2025.121527.

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