# The technical-reasoning network is recruited when people observe others make or teach how to make tools: An fMRI study

Alexandre Bluet, Emanuelle Reynaud, Giovanni Federico, Chloé Bryche, Mathieu Lesourd, Arnaud Fournel, Franck Lamberton, Danielle Ibarrola, Yves Rossetti, François Osiurak

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## To cite this version:

Alexandre Bluet, Emanuelle Reynaud, Giovanni Federico, Chloé Bryche, Mathieu Lesourd, et al. The technical-reasoning network is recruited when people observe others make or teach how to make tools: An fMRI study. iScience, 2025, 28 (2), pp.111870. 10.1016/j.isci.2025.111870. hal-04998615

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# The technical-reasoning network is recruited when people observe others make or teach how to make tools: An fMRI study

## Graphical abstract

<figure><img src="image_3.png" /></figure>

## Authors

Alexandre Bluet, Emanuelle Reynaud, Giovanni Federico, ..., Danielle Ibarrola, Yves Rossetti, François Osiurak

## Correspondence

alexandre.bluet@gmail.com

## In brief

Neuroscience; Cognitive neuroscience; Social sciences

## Highlights

* Tool-making observation recruits the technical reasoning network

* Part of the mentalizing network is implicated in teaching how to make a tool

* Teaching enhances focus on key steps, boosting technical-reasoning efficiency

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## Article

# The technical-reasoning network is recruited when people observe others make or teach how to make tools: An fMRI study

Alexandre Bluet,<fnref n="1" />,<fnref n="2" />,<fnref n="9" />,* Emanuelle Reynaud,<fnref n="1" /> Giovanni Federico,<fnref n="3" /> Chloé Bryche,<fnref n="1" /> Mathieu Lesourd,<fnref n="4" /> Arnaud Fournel,<fnref n="1" /> Franck Lamberton,<fnref n="5" /> Danielle Ibarrola,<fnref n="5" /> Yves Rossetti,<fnref n="6" />,<fnref n="7" /> and François Osiurak<fnref n="1" />,<fnref n="8" />

## SUMMARY

Cumulative technological culture is defined as the increase in efficiency and complexity of tools over generations. The role of social cognitive skills in cultural transmission has been long acknowledged. However, recent accounts emphasized the importance of non-social cognitive skills during the social transmission of technical content with a focus on technical reasoning. Here, we contribute to this double process approach by reporting an fMRI study about the neurocognitive origins of social learning. Participants watched videos depicting tool-making episodes in three social-learning conditions: reverse engineering, observation, and teaching. Our results showed that the technical-reasoning network, centered around the area PF of the left inferior parietal cortex, was preferentially activated when watching tool-making episodes. Additionally, teaching elicited the right middle temporal gyrus. This study suggests that technical reasoning underpins technological culture, while social cognition enhances learners' technical reasoning by guiding attention to key aspects of the technology.

## INTRODUCTION

The increasing efficiency and complexity of tools and toolmaking techniques over generations (i.e., cumulative technological culture, hereafter shortened as CTC) enable humans to be successful ecologically and demographically, allowing them to expand all over the world, even beyond Earth.<fnref n="1" />⁻<fnref n="3" /> CTC depends on the transmission of technical knowledge, which is supported by social learning. Social learning refers to learning about conspecifics or the physical world that is influenced by observation of, or interaction with, another conspecific or its products.<fnref n="4" /> The crucial aspect of CTC is its cumulative component, which results in products that could not have been invented by a single individual.<fnref n="5" /> This aspect is the cornerstone of our cultural evolution and has led researchers over the years to investigate the origins (i.e., the basis) of CTC.

CTC is driven by two engines, namely, high-fidelity transmission and innovation.<fnref n="6" /> The “ratcheting” hypothesis places heavy emphasis on the high-fidelity transmission component.<fnref n="3" />,<fnref n="5" />,<fnref n="7" /> It as-

sumes that cumulative culture can emerge only because high-fidelity transmission maintains cultural traits in place between innovative events. This hypothesis has led researchers for the last decades to turn their attention to the social part of CTC and to make fundamental discoveries (e.g., over-imitation,<fnref n="8" />⁻¹⁰ distinct forms of teaching,¹¹ and social-learning strategies¹²) about the social cognitive skills, namely mentalizing, the ability to detect as well as attribute intentions and mental states in others<fnref n="3" /> that can support high-fidelity transmission. Without denying the role of mentalizing, recent accounts have perceived an over-emphasis on the social dimension of the phenomenon and have argued that substantial effort must also be devoted to understanding the non-social cognitive skills at work in both the social and asocial episodes of transmission.¹³⁻¹⁸

The technical-reasoning hypothesis is consistent with these accounts.¹⁴,¹⁹ Technical reasoning is a specific form of causal reasoning oriented toward the physical world. Technical reasoning is based on mechanical knowledge that is acquired through experience (i.e., asocial and social learning). It allows humans to

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<footnote n="1">¹Laboratoire d'Étude des Mécanismes Cognitifs, Université de Lyon, Bron, France</footnote>

<footnote n="2">²Karolinska Institutet, Stockholm, Sweden</footnote>

<footnote n="3">³Laboratory of Experimental and Cognitive Neuroscience, Suor Orsola Benincasa University, Naples, Italy</footnote>

<footnote n="4">⁴Université Marie et Louis Pasteur, INSERM, UMR 1322 LINC, F-25000 Besançon, France</footnote>

<footnote n="5">⁵CERMEP-Imagerie du vivant, MRI Department and CNRS UMS3453, Lyon, France</footnote>

<footnote n="6">⁶Centre de Recherche en Neurosciences de Lyon (CRNL), Trajectoires Team (Inserm UMR_S 1028-CNRS-UMR 5292-Université de Lyon), Bron, France</footnote>

<footnote n="7">⁷Mouvement et Handicap and Neuro-Immersion, Hospices Civils de Lyon et Centre de Recherche en Neurosciences de Lyon, Hôpital Henry Gabrielle, Saint-Genis-Laval, France</footnote>

<footnote n="8">⁸Institut Universitaire de France, Paris, France</footnote>

<footnote n="9">⁹Lead contact</footnote>

<footnote>*Correspondence: alexandre.bluet@gmail.com
https://doi.org/10.1016/j.isci.2025.111870</footnote>
<figure><img src="image_6.png" /><figcaption>Figure 1. Time course of the task presented to the participant inside the scanner</figcaption></figure>

The four conditions are represented with screenshots from the videos shown to the participant and screenshots of the corresponding end-product/tool.

understand how tools are used but also how they are built, and thus increases the fidelity of transmitting such information to another peer. This hypothesis is supported by experimental work using microsociety paradigms (e.g., experiments where chains of individuals are asked to improve a physical system), which have shown that technical-reasoning skills are a key predictor of cumulative performance<sup>20-22</sup> or that the improvement of a physical system over generations is accompanied by an increased understanding of it<sup>13,23</sup> (but see the studies by Derex M et al.<sup>24</sup> and Kenndal R.L.<sup>25</sup>; for a review see the study by Osiurak F. et al.<sup>19</sup>). According to this hypothesis,<sup>14,26</sup> mentalizing is primarily involved in teaching episodes, that is, when the model modifies their behavior to facilitate learning in others.<sup>11,27</sup> In these episodes, mentalizing acts to strengthen and improve reasoning, the teacher pointing to where the focus of reasoning should be put. Neuropsychological and neuroimaging evidence<sup>28,29</sup> has indicated that the human technical-reasoning network is primarily comprised of a specific region located in the left inferior parietal lobe (IPL), named area PF (parietal F), and of the left inferior frontal gyrus (IFG). This network is recruited not only when people are reasoning about tool-use<sup>29</sup> or physical events<sup>30</sup> but also when they are watching others use tools,<sup>31</sup> which confirms that technical reasoning could be involved in both asocial and social learning.

The goal of the present study was to investigate the neurocogn-
itive origins of social learning during tool-making episodes. In this
paper, we define a tool as an object from the environment that is
used to perform a goal-directed action.³² This definition encom-
passes both what Shumaker et al.³³ called tool and construction.
This study was motivated by two reasons. First, CTC, strictly
speaking, describes the transmission of tool-making techniques
(e.g., a bow) and not of tool-use techniques. Tool-making is gener-
ally an instance of tool use. Therefore, the involvement of the tech-
nical-reasoning network when people observe others using tools

should also be reported when they observe others making
tools. However, this remains to be demonstrated. Thus, investi-
gating the neurocognitive bases of the “tool-making observation
network” may help us draw a more direct link between technical
reasoning, tool-making, social learning and, as a result, CTC. Sec-
ond, the technical-reasoning hypothesis predicts that technical
reasoning is recruited in all social-learning episodes in which tech-
nical content is transmitted, whereas mentalizing is specifically
recruited in teaching episodes. Again, investigating the neurocog-
nitive bases of the “tool-making observation network” may be
useful to test this prediction.

To do so, we presented participants with videos depicting
tool-making episodes (three experimental conditions) or item-
transport episodes (i.e., control condition; Figure 1). The partic-
ipants had to watch the video and respond to whether the video
depicted a tool-making episode or not. The three experimental
social-learning conditions were as follows. The first was reverse
engineering, where you watch a tool and try, by yourself, to infer
how it was made. In the present study, participants watched a
model showing the tool at three different steps of its making.
The second was observation, where participants watched a
model make a tool, but the model did not modify his behavior
to facilitate learning in participants. The third was teaching,
where participants watched a model make a tool and the model
modified his behavior to facilitate learning in participants (e.g.,
pointing out a crucial part of the tool). Our predictions were as
follows. Our first prediction was that when someone watches
others make tools, technical reasoning is at work. So, its network
(i.e., left area PF and left IFG) should be activated in the three
social-learning conditions. Our second prediction was that
mentalizing should be at play only in the Teaching condition.
Mentalizing skills involve a network<sup>34</sup> (see also the studies
by Molenberghs P. et al.<sup>35</sup> and Van Overwalle F. and Baetens
K.<sup>36</sup>) constituted of the anterior cingulate cortex (ACC; perspec-
tive taking), the temporal pole (TP; knowledge about persons),
and the superior temporal sulcus (STS; biological motion). Addi-
tionally, several studies have revealed the importance of the right
middle temporal gyrus (MTG) in the detection and understanding
of communicative gestures<sup>37</sup> as well as communicative intent,<sup>38</sup>
which is deemed crucial for mentalizing skills.<sup>39</sup> Besides, the
right MTG is part of the mentalizing network.<sup>40</sup> Hence, we pre-
dicted that the right MTG should be preferentially activated in
the teaching condition. Finally, we also expected that our so-
cial-learning conditions recruited additional brain areas involved
in the tool-use observation network but also more generally in
the action observation network (i.e., tool-use∩action observa-
tion network<sup>31</sup>), namely the intraparietal sulcus (IPS), the inferior
temporal gyrus (ITG), and the left middle frontal gyrus (MFG).

RESULTS

Participants performed the task correctly as evidenced by
the number of correct responses (M = 97.59, SD = 3.15,
Min = 87.50, Max = 100).

Contrast analysis

For the fMRI results, we first computed the teaching > control
(Figure 2A), observation > control (Figure 2B), and reverse
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<figure><img src="image_8.png" /><figcaption>**Figure 2. Statistical maps for contrasts for comparisons across the whole brain at the cluster level**</figcaption></figure>

(A) teaching over control, (B) observation over control, (C) reverse engineering over control and (D) teaching over observation. If not specified, all contrasts are against the control condition. IFG, inferior frontal gyrus; IPS, inferior parietal sulcus; IOG, inferior occipital gyrus; ITG, inferior temporal gyrus; MTG, middle temporal gyrus; STS, superior temporal sulcus; MTG, middle temporal gyrus; IPL, inferior parietal lobule; MFG, middle frontal gyrus; SPL, superior parietal lobule. The color scale indicates the activation t-score (p < 0.05) with FWE-correction.

engineering > control (Figure 2C) contrasts. The teaching > control and the observation > control contrasts but not the reverse engineering > control contrast, showed activations in the brain regions associated with the technical-reasoning network (i.e., the left area PF in both contrasts and the left IFG in the teaching > control contrast). The brain regions associated with the mentalizing network were recruited neither in the reverse engineering > control contrast nor in the observation > control contrast, but we found an activation of the right MTG in the teaching > control contrast. The involvement of the right MTG in the teaching > control contrast was reported in the teaching > observation contrast (Figure 2D). We also found activation in the brain areas that support the tool-use action observation network in the three experimental conditions (i.e., bilateral ITG and IPS). Finally, we obtained not foreseen activations of the right area PF in both the teaching > control contrast and the observation > control contrast.

ROI analysis

To go deeper into the analysis, we computed a repeated-measures ANOVA based on the individual maps of parameter estimates for the three conditions (teaching, observation, and reverse engineering) with condition as a within-subject factor. The ANOVA showed several regions that differed between the three conditions, namely bilateral occipital cortex, bilateral IPL, left IFG, bilateral SPL, right STG, bilateral MTG, bilateral ITG, left supramarginal gyrus (SMG), and right IFG. Based on these results, we computed posthoc comparisons between conditions in regions of interest (ROIs) that showed activation in the ANOVA. As can be seen in Figure 3A, we defined the ROI as aforementioned brain regions associated with the technical-reasoning network (i.e., left area PF and left IFG), the mentalizing network (i.e., ACC, TP, STS, and right MTG) and the tool-use action observation network (i.e., IPS, ITG, and left MTG). The list and coordinates of these ROIs are given in Table 1. Here, we focused on the left area PF,
LEFT HEMISPHERE

RIGHT HEMISPHERE

<figure><img src="image_9.png" /><figcaption>**Figure 3. Statistical maps for repeated measure ANOVA and post-hoc t-test analysis**</figcaption></figure>

(A) Statistical map for the post-hoc ANOVA. Spheres represent our ROIs (for more information, see SI3), with the technical-reasoning network in green, the mentalizing network in purple and the tool-use∩action observation in blue. The right PF is shown in black. It was not a predetermined ROI. The activation intensity is represented by the color scale indicating the t-score (*p* < 0.05) with FWE-correction.

(B–H) Posthoc t-tests on mean activations inside the ROI between the three social-learning conditions inside the ROI that showed activation in the ANOVA. t-test results are presented as follows: n.s., not significant.

the left IFG, the right MTG, the left IPS, the left ITG and the right
ITG as they elicited activation in the ANOVA. Our post hoc tests
consisted of t-tests on mean activations inside the ROI between
the three social-learning conditions (Figures 3B–3G). The ROI
derived from left PF teaching (*p* < 0.001) and observation

(p < 0.05) showed greater activation than reverse engineering.
No difference was observed between teaching and observation
(Figure 3B). The same pattern was found in the left IFG ROI
(both p < 0.001; Figure 3C). For the right MTG ROI (Figure 3D),
teaching elicited greater activation than observation (p < 0.001)

<footnote>*p* < 0.05; **p** < 0.01; ***p** < 0.001. Error bars represent the standard errors.</footnote>
<table><caption>Table 1. Seed-region locations</caption><thead><tr><th rowspan="2">Seed name</th><th rowspan="2">Hemisphere</th><th colspan="3">MNI coordinates</th><th rowspan="2">Studies</th></tr><tr><th>x</th><th>y</th><th>z</th></tr></thead><tbody><tr><td colspan="6"><em>Technical-reasoning network</em></td></tr><tr><td>PF</td><td>Left</td><td>-59</td><td>-31</td><td>40</td><td>Reynaud et al.<sup>29</sup></td></tr><tr><td>IFG</td><td>Left</td><td>-52</td><td>5</td><td>29</td><td>Reynaud et al.<sup>29</sup></td></tr><tr><td colspan="6"><em>Mentalizing network</em></td></tr><tr><td>ACC</td><td>Left<sup>a</sup></td><td>-2</td><td>52</td><td>-8</td><td>Molenberghs et al.<sup>35</sup></td></tr><tr><td>right TP</td><td>Right</td><td>60</td><td>-10</td><td>-16</td><td>Molenberghs et al.<sup>35</sup></td></tr><tr><td>left TP</td><td>Left</td><td>-56</td><td>-12</td><td>-14</td><td>Molenberghs et al.<sup>35</sup></td></tr><tr><td>STS</td><td>Left</td><td>-52</td><td>-58</td><td>22</td><td>Hodgson et al.<sup>40</sup></td></tr><tr><td>MTG</td><td>Right</td><td>50</td><td>-70</td><td>8</td><td>Hodgson et al.<sup>40</sup></td></tr><tr><td colspan="6"><em>Tool-use action observation</em></td></tr><tr><td>IPS</td><td>Left</td><td>-34</td><td>-49</td><td>52</td><td>Reynaud et al.<sup>31</sup></td></tr><tr><td>left ITG</td><td>Left</td><td>-50</td><td>-64</td><td>-3</td><td>Reynaud et al.<sup>31</sup></td></tr><tr><td>right ITG</td><td>Right</td><td>44</td><td>-60</td><td>-6</td><td>Reynaud et al.<sup>31</sup></td></tr><tr><td>MFG</td><td>Left</td><td>-24</td><td>-16</td><td>62</td><td>Reynaud et al.<sup>31</sup></td></tr></tbody></table>

and reverse engineering (*p* < 0.001), and observation greater activation than reverse engineering (*p* < 0.001). Teaching preferentially activated the left IPS (Figure 3E) compared to reverse engineering (*p* < 0.01). No other statistical difference was reported for the left IPS. Finally, for the left ITG ROI (Figure 3F) and the right ITG ROI (Figure 3G), teaching showed greater activation than observation (both *p* < 0.05) and reverse engineering (both *p* < 0.001), and observation greater activation than reverse engineering (both *p* < 0.05). Finally, even if the right area PF was not one of our ROI, we conducted post-hoc t-tests to examine the influence of social-learning conditions on its level of activation (Figure 3H). We found that teaching elicited greater activation than observation (*p* < 0.05) and reverse engineering (*p* < 0.001), and observation greater activation than reverse engineering (*p* < 0.001).

DISCUSSION

The present study aimed to investigate the neurocognitive origins of social learning during tool-making episodes. To do so, we presented participants with tool-making videos in three social-learning conditions, namely, reverse engineering, observation and teaching. According to the technical-reasoning hypothesis,¹⁴,¹⁹,²⁶ the technical-reasoning network should be recruited in the three social-learning conditions and the mentalizing network only in the teaching condition. These predictions were partly confirmed by our results. We did find the involvement of the technical-reasoning network in the teaching condition and its partial involvement in the observation condition, and we reported the activation of the right MTG, which is part of the mentalizing network, only in the teaching condition. However, the technical-reasoning network was not activated in the reverse engineering condition.

We will discuss these findings in turn in the next sections.

The first key finding is the recruitment of the technical-reasoning network (i.e., the left area PF and the left IFG) in both the teaching condition and the observation condition. Previous findings have indicated that this network is activated when people watch others use tools. We extend these findings by demonstrating that this network is also recruited during the observation of someone making or teaching how to make a tool, thereby drawing a link between technical reasoning, tool-making, social learning and, as a result, CTC. As mentioned above, recent accounts have perceived an over-emphasis on the social dimension of the phenomenon,¹⁸ which has led some to minimize the role played by causal understanding/technical reasoning in CTC.²⁴,⁴¹,⁴² Here, we show that the ability to reason about our physical world is spontaneously recruited when someone observes a model making or teaching how to make a tool. In these social-learning episodes, technical reasoning might allow us to infer and predict the goal of each step used to make it. These inferences are essential for the reliable transmission of knowledge related to tool-making. In a nutshell, technical reasoning might contribute not only to the innovation component of CTC but also to its high-fidelity transmission component.¹⁹ Future research is nevertheless needed to test whether the activation of the technical-reasoning network as reported here can directly be associated with an increased understanding of the technical task observed.

An unexpected, interesting result is the bilateral activation of the area PF. This bilateral activation is not consistent with the tool-use observation network, in which only the left area PF is involved.³¹ It is more consistent with the action observation network, which is distributed bilaterally.³⁶ A possibility is that both tool use and tool-making need reasoning about nonspatial, physical object properties (e.g., solidity), which are crucial for generating physical forces (e.g., lever) or mechanical actions (e.g., lever). This aspect might be more left-lateralized and very specific to technical reasoning. By contrast, tool-making might need reasoning more about spatial properties than tool use, because it involves assembling objects based on pure spatial dimensions (e.g., orientation). Support for this possibility comes from a recent study that showed that the cortical thickness of the left area PF predicted physical-reasoning performance whereas visuospatial-reasoning performance was predicted by the cortical thickness of both left and right areas PF.⁴³ Further studies should try to explore this question.

The second key finding is that isolating the pure teaching component (i.e., teaching > observation contrast) led us to find preferential activation of the right MTG, which is part of the mentalizing network. The right MTG is known for its involvement in the perception of biological motion⁴⁰ and, more importantly, has been reported to support our understanding of the intention to communicate, also called communicative intent,³⁷ which is crucial for teaching. When someone tries to transmit the understanding of a physical phenomenon to another individual, they must first make it clear that they intend to communicate. In our teaching condition, this intention was transmitted through non-verbal cues (i.e., pointing and gestures), which helped the model focus on important elements and steps of the tool-making process.

<footnote><sup>a</sup>Center of ROI for the ACC is on the left hemisphere but expands to the right hemisphere.</footnote>
We posit that these cues amplified the learner's technical reasoning by showing directly where the attention needed to be put. Indeed, focusing attention on the crucial parts of the process means spending more time reasoning about them, but it also means not spending time and resources on irrelevant aspects. Thus, the key role of teaching in CTC might be to improve learners' technical-reasoning skills. The specific role of the right MTG in communicative intent is also following the concept of intentional teaching,³⁹ a supposedly unique human form of teaching that allows us to share knowledge more efficiently. This finding also echoes the concept of natural pedagogy,⁴⁴,⁴⁵ which describes an “innate” ability for teaching (and being taught), and, therefore, an ancient ability in our evolutionary history. According to the natural pedagogy hypothesis, infants at an early age can capture ostensive signals that indicate that another individual is communicating with them. We propose that this capacity is rooted in the right MTG, which corroborates studies showing that children suffering from autistic disorder both meet difficulties in understanding communicative intent and have abnormal activation of the right MTG.⁴⁶ Taken together with our results, these findings suggest an adaptive role of the right MTG for teaching and encourage future research to investigate this link.

It is noteworthy that we did not find any activation of the other brain regions engaged in the mentalizing network (notably ACC and TP) in the teaching condition. ACC is known to contribute to perspective-taking and TP to the storage of semantic information about persons. Both cognitive components characterize “theory of mind” and might be recruited when people teach instead of when they are taught. This can explain why no activation was found here for these regions in the teaching condition. Future studies should explore this question by investigating the brain network at work during teaching. We hypothesize that activation of ACC and TP should be found when people teach.

The third finding is that the reverse engineering condition did not elicit any regions of the technical-reasoning network. This surprising result can be understood in two ways. First, learning via reverse engineering does not require technical reasoning. This is contradictory to several previous studies that have stressed the role of causal understanding/technical reasoning in reverse engineering conditions.¹³,⁴⁷ In broad terms, this interpretation is plausible but unlikely. Second, it could indicate that we failed to create an experimental condition engaging reverse engineering inside the scanner. Reverse engineering is defined as watching a technology and trying to understand how it was made, potentially by manipulating it or by scrutinizing its aspects.¹⁴ In our experiment, we showed different steps of the making process to the participants for a few seconds at a time, which could be different from a true reverse engineering condition. Moreover, reverse engineering usually implies deconstructing the technology to understand how it was made (e.g., unfolding a paper plane to grasp how it was built), which was not possible for our participants inside the scanner. Furthermore, albeit they knew the tools being built beforehand, the lack of a clear end goal in the video might have made it hard for participants to understand the reason behind the difference in each step shown in the video, and overall make the condition confusing. Finally, the task was to watch the video and respond to whether it depicted a tool-making episode or not. Hence,

participants were not forced to understand the making process. Because the setup for the videos in the control condition was very distinct from the three experimental conditions, participants did not need to pay attention to the whole video to respond, as only the first few seconds of the video were sufficient to do the task. To control for this issue, we ran another analysis where only the first half of the videos was considered. No significant changes were found with this analysis. A solution for studying reverse engineering inside a scanner can be to project a 3D image of a technology and to allow the participant to rotate and zoom on the image. Another solution could be to also investigate reverse engineering using 3D videos of a tool being deconstructed step by step. Otherwise, an alternative would be to change the task, by asking the participant to watch closely the video so that he could make the technology after the scanning session for example. Future research is needed to design a reverse engineering condition suited to neuroimaging conditions.

The origin of our technological culture is still a hotly debated topic. In the past four decades, important insights have been gained from disciplines such as evolutionary biology, mathematics, anthropology, archeology, economics, or some psychological sub-disciplines such as social, developmental, or comparative psychology. This contrasts with the smaller contribution of cognitive sciences and particularly cognitive neuroscience.⁴⁸,⁴⁹ Here, we contribute to fill this lack by reporting an fMRI study about social learning of tool-making techniques. Our results converge with behavioral findings by showing that technical reasoning and social cognitive skills are both involved in the social learning of tool-making techniques. This suggests that cognitive neuroscience can provide new insights into the origins of CTC.

## Limitations of the study

This study had some limitations. First, as stated in the discussion, we were unable to create an experimental condition engaging reverse engineering inside the scanner. Further studies should aim to recreate proper reverse engineering conditions inside the scanner to study the brain networks involved in this social-learning form. Second, participants in our study were not asked to reproduce the tool they watched in the video. Future studies should include a tool-making phase after the scanning session to examine how much participants learned the tool-making technique. This could not only improve the engagement of participants in the task but also extend our results by correlating the success in tool-making and the activations when participants watched the tool-making episodes. Finally, our study only included non-verbal teaching. Further study should investigate the role of verbal teaching.

## RESOURCE AVAILABILITY

### Lead contact

Further information and requests for resources should be directed to and will be fulfilled by the lead contact, Alexandre Bluet (alexandre.bluet@gmail.com).

### Materials availability

Materials are available from the corresponding author upon reasonable request.
## Data and code availability

* All data reported in this paper has been deposited and is publicly available. OSF: https://osf.io/6rsqf/?view_only=7871b5971ed14b359bf7f4ab096879ef.

* All original code has been deposited and is publicly available. OSF: https://osf.io/6rsqf/?view_only=7871b5971ed14b359bf7f4ab096879ef.

* Any additional information required to reanalyze the data reported in this paper is available from the lead contact upon request.

## ACKNOWLEDGMENTS

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

## AUTHOR CONTRIBUTIONS

A.B., E.R., and F.O. designed the research; A.B., G.F., C.B. F.L., D.I., and Y.R. performed the research. A.B., A.F., and E.R. analyzed data; A.B. wrote the manuscript; E.R., G.F., C.B., M.L., Y.R., and F.O. reviewed and edited the manuscript; F.O., E.R., M.L., and Y.R. acquired the financial support for the project.

## DECLARATION OF INTERESTS

The authors declare no competing interests.

## STAR★METHODS

Detailed methods are provided in the online version of this paper and include the following:

* KEY RESOURCES TABLE

* EXPERIMENTAL MODEL AND STUDY PARTICIPANT DETAILS

* Participants

* Stimuli

* fMRI experiment

* Task

* METHOD DETAILS

* fMRI data acquisition

* Preprocessing of fMRI data

* QUANTIFICATION AND STATISTICAL ANALYSIS

* Group analysis

* Seed selection and analysis

## SUPPLEMENTAL INFORMATION

Supplemental information can be found online at https://doi.org/10.1016/j.isci.2025.111870.

Received: May 8, 2024

Revised: October 18, 2024

Accepted: January 20, 2025

Published: January 22, 2025

## REFERENCES

1. Boyd, R., and Richerson, P.J. (1985). Culture and the Evolutionary Process (Paperback ed) (University of Chicago Press).

2. Derex, M. (2022). Human cumulative culture and the exploitation of natural phenomena. Philos. Trans. R. Soc. Lond. B Biol. Sci. 377, 20200311. https://doi.org/10.1098/rstb.2020.0311.

3. Tomasello, M., Kruger, A.C., and Ratner, H.H. (1993). Cultural learning. Behav. Brain Sci. 16, 495–511. https://doi.org/10.1017/S0140525X0003123X.

4. Heyes, C.M. (1994). Social learning in animals : categories and mechanisms. Biol. Rev. 69, 207–231.

5. Tomasello, M. (1999). The human adaptation for culture. Annu. Rev. Anthropol. 28, 509–529. https://doi.org/10.1146/annurev.anthro.28.1.509.

6. Legare, C.H., and Nielsen, M. (2015). Imitation and innovation : The dual engines of cultural learning. Trends Cognit. Sci. 19, 688–699. https://doi.org/10.1016/j.tics.2015.08.005.

7. Tennie, C., Call, J., and Tomasello, M. (2009). Ratcheting up the ratchet: On the evolution of cumulative culture. Philos. Trans. R. Soc. Lond. B Biol. Sci. 364, 2405–2415. https://doi.org/10.1098/rstb.2009.0052.

8. Hoehl, S., Keupp, S., Schleihauf, H., McGuigan, N., Buttelmann, D., and Whiten, A. (2019). ‘Over-imitation’ : A review and appraisal of a decade of research. Dev. Rev. 51, 90–108. https://doi.org/10.1016/j.dr.2018.12.002.

9. Horner, V., and Whiten, A. (2005). Causal knowledge and imitation/emulation switching in chimpanzees (Pan troglodytes) and children (Homo sapiens). Anim. Cognit. 8, 164–181. https://doi.org/10.1007/s10071-004-0239-6.

10. Lyons, D.E., Young, A.G., and Keil, F.C. (2007). The hidden structure of overimitation. Proc. Natl. Acad. Sci. USA 104, 19751–19756. https://doi.org/10.1073/pnas.0704452104.

11. Kline, M.A. (2015). How to learn about teaching : An evolutionary framework for the study of teaching behavior in humans and other animals. Behav. Brain Sci. 38, e31. https://doi.org/10.1017/S0140525X14000090.

12. Kendal, R.L., Boogert, N.J., Rendell, L., Laland, K.N., Webster, M., and Jones, P.L. (2018). Social Learning Strategies : Bridge-building between fields. Trends Cognit. Sci. 22, 651–665. https://doi.org/10.1016/j.tics.2018.04.003.

13. Osiurak, F., Claidière, N., Bluet, A., Brognartj, J., Lasserre, S., Bonhoure, T., Di Rollo, L., Gorry, N., Polette, Y., Saude, A., et al. (2022). Technical reasoning bolsters cumulative technological culture through convergent transformations. Sci. Adv. 8, eab17446. https://doi.org/10.1126/sciadv.abl7446.

14. Osiurak, F., and Reynaud, E. (2019). The elephant in the room: What matters cognitively in cumulative technological culture. Behav. Brain Sci. 43, e156. https://doi.org/10.1017/S0140525X19003236.

15. Perry, S., Carter, A., Smolla, M., Akçay, E., Nöbel, S., Foster, J.G., and Healy, S.D. (2021). Not by transmission alone: The role of invention in cultural evolution. Philos. Trans. R. Soc. Lond. B Biol. Sci. 376, 20200049. https://doi.org/10.1098/rstb.2020.0049.

16. Singh, M., Acerbi, A., Caldwell, C.A., Danchin, É., Isabel, G., Molleman, L., Scott-Phillips, T., Tamariz, M., van den Berg, P., van Leeuwen, E.J.C., and Derex, M. (2021). Beyond social learning. Philos. Trans. R. Soc. Lond. B Biol. Sci. 376, 20200050. https://doi.org/10.1098/rstb.2020.0050.

17. Vale, G.L., McGuigan, N., Burdett, E., Lambeth, S.P., Lucas, A., Rawlings, B., Schapiro, S.J., Watson, S.K., and Whiten, A. (2021). Why do chimpanzees have diverse behavioral repertoires yet lack more complex cultures? Invention and social information use in a cumulative task. Evol. Hum. Behav. 42, 247–258. https://doi.org/10.1016/j.evolhumbehav.2020.11.003.

18. Whiten, A., Biro, D., Bredeche, N., Garland, E.C., and Kirby, S. (2022). The emergence of collective knowledge and cumulative culture in animals, humans and machines. Philos. Trans. R. Soc. Lond. B Biol. Sci. 377, 20200306. https://doi.org/10.1098/rstb.2020.0306.

19. Osiurak, F., Claidière, N., and Federico, G. (2022). Bringing cumulative technological culture beyond copying versus reasoning. Trends Cognit. Sci. 27, 30–42. https://doi.org/10.1016/j.tics.2022.09.024.

20. De Oliveira, E., Reynaud, E., and Osiurak, F. (2019). Roles of technical reasoning, theory of mind, creativity, and fluid cognition in cumulative technological culture. Hum. Nat. 30, 326–340. https://doi.org/10.1007/s12110-019-09349-1.

21. Osiurak, F., De Oliveira, E., Navarro, J., Lesourd, M., Claidière, N., and Reynaud, E. (2016). Physical intelligence does matter to cumulative technological culture. J. Exp. Psychol. Gen. 145, 941–948. https://doi.org/10.1037/xge0000189.
22. Osiurak, F., De Oliveira, E., Navarro, J., and Reynaud, E. (2020). The cast-away island: Distinct roles of theory of mind and technical reasoning in cumulative technological culture. J. Exp. Psychol. Gen. 149, 58–66. https://doi.org/10.1037/xge0000614.

23. Osiurak, F., Lasserre, S., Arbanti, J., Brogniart, J., Bluet, A., Navarro, J., and Reynaud, E. (2021). Technical reasoning is important for cumulative technological culture. Nat. Human Behav. 5, 1643–1651. https://doi.org/10.1038/s41562-021-01159-9.

24. Derex, M., Bonnefon, J.F., Boyd, R., and Mesoudi, A. (2019). Causal understanding is not necessary for the improvement of culturally evolving technology. Nat. Human Behav. 3, 446–452. https://doi.org/10.1038/s41562-019-0567-9.

25. Kendal, R.L. (2019). Explaining human technology. Nat. Human Behav. 3, 422–423.

26. Bluet, A., Osiurak, F., Claidière, N., and Reynaud, E. (2022). Impact of technical reasoning and theory of mind on cumulative technological culture: Insights from a model of micro-societies. Humanit. Soc. Sci. Commun. 9, 231. https://doi.org/10.1057/s41599-022-01251-z.

27. Caro, T.M., and Hauser, M.D. (1992). Is there teaching in nonhuman animals? Q. Rev. Biol. 67, 151–174. https://doi.org/10.1086/417553.

28. Goldenberg, G., and Spatt, J. (2009). The neural basis of tool use. Brain 132, 1645–1655. https://doi.org/10.1093/brain/awp080.

29. Reynaud, E., Lesourd, M., Navarro, J., and Osiurak, F. (2016). On the neurocognitive origins of human tool use: A critical review of neuroimaging data. Neurosci. Biobehav. Rev. 64, 421–437. https://doi.org/10.1016/j.neubiorev.2016.03.009.

30. Fischer, J., Mikhael, J.G., Tenenbaum, J.B., and Kanwisher, N. (2016). Functional neuroanatomy of intuitive physical inference. Proc. Natl. Acad. Sci. USA 113, E5072–E5081. https://doi.org/10.1073/pnas.1610344113.

31. Reynaud, E., Navarro, J., Lesourd, M., and Osiurak, F. (2019). To watch is to work: A review of neuroimaging data on tool use observation network. Neuropsychol. Rev. 29, 484–497. https://doi.org/10.1007/s11065-019-09418-3.

32. Mangalam, M., Fragaszy, D.M., Wagman, J.B., Day, B.M., Kelty-Stephen, D.G., Bongers, R.M., Stout, D.W., and Osiurak, F. (2022). On the psychological origins of tool use. Neurosci. Biobehav. Rev. 134, 104521. https://doi.org/10.1016/j.neubiorev.2022.104521.

33. Shumaker, R.W., Walkup, K.R., and Beck, B.B. (2011). *Animal tool Behavio: The use and Manufacture of Tools by Animals* (Rev. And Updated Ed) (Johns Hopkins University Press).

34. Gallagher, H.L., and Frith, C.D. (2003). Functional imaging of ‘theory of mind’. Trends Cognit. Sci. 7, 77–83. https://doi.org/10.1016/S1364-6613(02)00025-6.

35. Molenberghs, P., Johnson, H., Henry, J.D., and Mattingley, J.B. (2016). Understanding the minds of others : A neuroimaging meta-analysis. Neurosci. Biobehav. Rev. 65, 276–291. https://doi.org/10.1016/j.neubiorev.2016.03.020.

36. Van Overwalle, F., and Baetens, K. (2009). Understanding others’ actions and goals by mirror and mentalizing systems: A meta-analysis. Neuroimage 48, 564–584. https://doi.org/10.1016/j.neuroimage.2009.06.009.

37. Holler, J., Kokal, I., Toni, I., Hagoort, P., Kelly, S.D., and Özyürek, A. (2015). Eye’m talking to you : Speakers’ gaze direction modulates co-speech gesture processing in the right MTG. Soc. Cognit. Affect Neurosci. 10, 255–261. https://doi.org/10.1093/scan/nsu047.

38. Redcay, E., Velnoskey, K.R., and Rowe, M.L. (2016). Perceived communicative intent in gesture and language modulates the superior temporal sulcus: Shared Neural Systems for Gesture and Language. Hum. Brain Mapp. 37, 3444–3461. https://doi.org/10.1002/hbm.23251.

39. O’Madagain, C., and Tomasello, M. (2022). Shared intentionality, reasoning and the evolution of human culture. Philos. Trans. R. Soc. Lond. B Biol. Sci. 377, 20200320. https://doi.org/10.1098/rstb.2020.0320.

40. Hodgson, V.J., Lambon Ralph, M.A., and Jackson, R.L. (2023). The cross-domain functional organization of posterior lateral temporal cortex : Insights from ALE meta-analyses of 7 cognitive domains spanning 12,000 participants. Cerebr. Cortex 33, 4990–5006. https://doi.org/10.1093/cercor/bhc394.

41. Boyd, R., Richerson, P.J., and Henrich, J. (2011). The cultural niche : Why social learning is essential for human adaptation. Proc. Natl. Acad. Sci. USA 108, 10918–10925. https://doi.org/10.1073/pnas.1100290108.

42. Henrich, J. (2016). *The Secret of Our Success : How Culture Is Driving Human Evolution, Domesticating Our Species, and Making Us Smarter* (Princeton University Press).

43. Federico, G., Reynaud, E., Navarro, J., Lesourd, M., Gaujoux, V., Lamberton, F., Ibarrola, D., Cavaliere, C., Alfano, V., Aiello, M., et al. (2022). The cortical thickness of the area PF of the left inferior parietal cortex mediates technical-reasoning skills. Sci. Rep. 12, 11840. https://doi.org/10.1038/s41598-022-15587-8.

44. Csibra, G., and Gergely, G. (2006). Social learning and social cognition : The case for pedagogy. In *Processes of Change in Brain and Cognitive Development*, Y. Munakata and M.H. Johnson, eds. (Oxford University PressOxford), pp. 249–274. https://doi.org/10.1093/oso/9780198568742.003.0011.

45. Csibra, G., and Gergely, G. (2009). Natural pedagogy. Trends Cognit. Sci. 13, 148–153. https://doi.org/10.1016/j.tics.2009.01.005.

46. Zilbovicius, M., Meresse, I., Chabane, N., Brunelle, F., Samson, Y., and Boddaert, N. (2006). Autism, the superior temporal sulcus and social perception. Trends Neurosci. 29, 359–366. https://doi.org/10.1016/j.tins.2006.06.004.

47. Zwriner, E., and Thornton, A. (2015). Cognitive requirements of cumulative culture: Teaching is useful but not essential. Sci. Rep. 5, 16781. https://doi.org/10.1038/srep16781.

48. Heyes, C. (2018). Cognitive Gadgets : The Cultural Evolution of Thinking (Harvard University Press). https://doi.org/10.4159/9780674985155.

49. Heyes, C. (2018). Enquire within : Cultural evolution and cognitive science. Philos. Trans. R. Soc. Lond. B Biol. Sci. 373, 20170051. https://doi.org/10.1098/rstb.2017.0051.

50. Kao, M.-H., Mandal, A., Lazar, N., and Stufken, J. (2009). Multi-objective optimal experimental designs for event-related fMRI studies. Neuroimage 44, 849–856. https://doi.org/10.1016/j.neuroimage.2008.09.025.

51. Jeannerod, M. (1994). The representing brain : Neural correlates of motor intention and imagery. Behav. Brain Sci. 17, 187–202. https://doi.org/10.1017/S0140525X00034026.

52. Culham, J.C., Brandt, S.A., Cavanagh, P., Kanwisher, N.G., Dale, A.M., and Tootell, R.B. (1998). Cortical fMRI activation produced by attentive tracking of moving targets. J. Neurophysiol. 80, 2657–2670. https://doi.org/10.1152/jn.1998.80.5.2657.

53. Tootell, R.B., Reppas, J.B., Kwong, K.K., Malach, R., Born, R.T., Brady, T.J., Rosen, B.R., and Belliveau, J.W. (1995). Functional analysis of human MT and related visual cortical areas using magnetic resonance imaging. J. Neurosci. 15, 3215–3230. https://doi.org/10.1523/JNEUROSCI.15-04-03215.1995.

54. Watson, J.D.G., Myers, R., Frackowiak, R.S.J., Hajnal, J.V., Woods, R.P., Mazziotta, J.C., Shipp, S., and Zeki, S. (1993). Area V5 of the human brain: evidence from a combined study using positron emission tomography and magnetic resonance imaging. Cerebr. Cortex 3, 79–94. https://doi.org/10.1093/cercor/3.2.79.
## STAR★METHODS

### KEY RESOURCES TABLE

<table><thead><tr><th>REAGENT or RESOURCE</th><th>SOURCE</th><th>IDENTIFIER</th></tr></thead><tbody><tr><td colspan="3">Deposited data</td></tr><tr><td>Current study data</td><td>Study cohort</td><td>OSF: https://osf.io/6rsqf/?view_only=7871b5971ed14b359bf7f4ab096879ef<br>https://doi.org/10.17605/OSF.IO/6RSQF</td></tr><tr><td colspan="3">Software and algorithms</td></tr><tr><td>MATLAB R2019a</td><td>MathWorks</td><td>RRID: SCR 001622</td></tr><tr><td>SPM12</td><td>University College</td><td>RRID: SCR 007037</td></tr><tr><td>R</td><td>Version 4.1.1</td><td>R Foundation https://www.r-project.org/</td></tr><tr><td>PsychoPy</td><td>PsychoPy 3.2</td><td>https://www.psychopy.org/index.html</td></tr></tbody></table>

### EXPERIMENTAL MODEL AND STUDY PARTICIPANT DETAILS

#### Participants

Thirty healthy participants were enrolled in the study. Inclusion in the final sample required that head motion during scanning did not exceed 0.5mm displacement (i.e., framewise displacement) between consecutive volumes on 90% of volume, resulting in one male participant being excluded on this criterion. Another participant was not scanned due to COVID-related complications. Overall, twenty-height participants ($M_{age} = 20.3$, $SD = 2.48$; 23 females; all right-handers) completed the study. All participants were voluntary and signed written consent. The study was in line with the Declaration of Helsinki and was approved by the French Ethics Committee (N° ID-RCB: 2020-102115-34). The participants in the fMRI experiment received a monetary compensation of 60€ for their participation.

#### Stimuli

Three experimental conditions were used in the present study, namely, Reverse engineering, Observation and Teaching. In each condition, participants saw a video representing a scene in which an actor made a tool. Figure 1 shows the time course of the experiment along with a screenshot from a video stimulus for each condition. In the Teaching condition, the actor made a tool and communicated non-verbally to explain the process. The same goes for the Observation condition, but without any communication. For the Reverse engineering condition, the actor sequentially presented three different stages of the tool-making process, starting from the first step to the completed tool. After each step was demonstrated to the participant, it was discarded off-screen to simulate the progressive development of the tool. We also created a control condition, where the actor just moved around the tools and materials available from the top to the bottom of the table. These tools and materials were matched to the ones used to make the tools. All the videos are available at https://osf.io/6rsqf/.

In total, they were 10 different tools being made across all three conditions, resulting in 30 videos. The scenes were filmed using a Panasonic Lumix G80 camera. The camera was at the back of the table, 50 cm above and tilted at a 30° angle to film from a high-angle spot. Only the arms, hands and torso of the actor were visible, along with the table upon which they were placed. The tools and materials were always placed on the opposite side of the table from the hands of the actor, at the furthest possible distance to still be visible and on the screen. At the beginning of all videos, the hands of the actor were already on the table just in front of him. After some delays that varied between videos, the actor started moving. In the Observation and Teaching conditions, the actor's hands and arms moved towards the various materials and started the making process. After finishing it, the actor placed the tool on the table so that it was clearly visible to the camera, and then placed his hands in the same way as they were at the beginning of the video. For the Reverse engineering condition, the first step of the making process was already on screen when the video started, and the actor simply moved it aside after 5 seconds while bringing the next step and so forth until the last step was on screen. We directly show the first step to minimize the manipulation of tools and materials in this condition. While the actor was doing nothing, his hands were always in the starting position (i.e., firmly put on the table in front of him).

All videos had a duration of about 15 seconds ($M_{duration} = 15.15$, $SD = 0.65$, $min = 15$, $max = 17.05$; with 59 frames per second) and a resolution of 1280 x 720 pixels. All video editing was performed using the iMovie software (available from https://www.apple.com/fr/imovie/). Editing consisted only of cutting the video so that it started and ended with the actor's hands on the table. We played with the duration of these start and end positions so that the video was at least 15 seconds long (e.g., when the tool-making process was shorter than 15 seconds, the videos had longer start and end positions). All videos were presented without sound to the participants. All videos were tested in a separate experiment to make sure participants understood the different conditions. For this experiment, we recruited 5 participants ($M_{age} = 26.2$, $SD = 1.48$; 2 females; all right-handers). All participants were told beforehand about the four conditions and were also shown a set of four dummy videos (one per condition) to familiarize themselves with them. Their task was to recognize the condition of the videos. As expected, all participants had a 100% recognition rate.
## fMRI experiment

Each participant was scanned in a single session with: (i) a first functional run, (ii) a T1-weighted anatomical scan, and (iii) a second functional run. There were two functional runs because there were two experiments. The order of experiments was counterbalanced between participants. The functional run for the present experiment contained 40 trials, 10 trials per condition, the control condition included. Each trial started with a video (~15 s) followed by the task screen (3 s) and ended with a rest screen (15 s), consisting of a white fixation cross (15 s) over a black screen. The task screen consisted of a black screen with “MAKING” written on the left and “TRANSPORT” on the right, in the same position as the response buttons. There was no inter-trial interval. A genetic algorithm was used to optimize the experimental design with regards to contrast estimation⁵⁰ using the toolbox NeuroDesign (https://neurodesign.readthedocs.io/en/latest/index.html).

## Task

Participants were asked to carefully watch each video. After the video, the response screen was shown. They had to indicate if they saw a tool being made or someone moving items around in the videos. To answer, they had a response box with 4 buttons, where the left one was associated with “tool making” and the right one was “item transport”. All three social learning conditions were considered as “tool making”, even Reverse engineering. This task was implemented to maintain the participants’ attention during the viewing of the videos. To ensure the participants understood the instructions during the fMRI sessions, we instructed them before entering the scanner. Because the function of the tool being made in the video was sometimes opaque, we added a familiarization step in the instructions. The instructions consisted of a presentation of the fMRI scanner, the task and instructions. In the familiarization step, the participant saw 10 videos, one per tool, where a demonstrator used the tool in an everyday-life context. They were asked to name the tool or its function and instructed that there was no wrong answer. After the participant was comfortable with the material, the scanning session began.

## METHOD DETAILS

### fMRI data acquisition

Imaging data were acquired on a 3T Siemens Prisma Scanner (Siemens, Erlangen, Germany) at CERMEP (Lyon, France). A 64-channel head coil was used. Blood-Oxygen Level Dependent (BOLD) images were recorded with T2*-weighted echo-planar images (EPI) acquired with the multi-band sequence. Functional images were all collected as oblique-axial scans aligned with the anterior commissure-posterior commissure (AC-PC) line with the following parameters: 960 volumes per run, 57 slices, TR/TE = 1400 ms / 30 ms, flip angle = 70°, the field of view = 96 x 96 mm², slice thickness = 2.3 mm, voxel size = 2.3 x 2.3 x 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 x 256 mm FOV, slice thickness = 0.8 mm, voxel size = 0.8 x 0.8 x 0.8 mm³).

### Preprocessing of fMRI data

Structural T1-weighted images were segmented into tissue type (GM: grey matter, WM: white matter and CSF: cerebrospinal fluid tissues) using the Computational Anatomy Toolbox (CAT12; http://dbm.neuro.uni-jena.de/cat12/) segmentation tool, to facilitate the normalization step. Functional data were analyzed using SPM12 (Wellcome Department of Cognitive Neurology, http://www.fil.ion.ucl.ac.uk/spm) implemented in MATLAB (Mathworks, Sherborn, MA). Preprocessing for univariate analyses included the following steps: (1) realignment to the mean EPI image with 6-head motion correction parameters and unwarping using the FieldMap toolbox from SPM12; (2) co-registration of the individual functional and anatomical images; (3) normalization towards MNI template; and (4) spatial smoothing of functional images (Gaussian kernel with 5 mm FWHM).

## QUANTIFICATION AND STATISTICAL ANALYSIS

### Group analysis

A General Linear Model was created using design matrices containing one regressor (explanatory variable) for each condition (i.e., Teaching, Observation, Reverse engineering, Control) modelled as a boxcar function (with onsets and durations corresponding to the start of each stimulus of that condition) convolved with the canonical hemodynamic response function (HRF) as well as its temporal and dispersion derivatives. Six regressors of non-interest resulting from 3D head motion estimation (x, y, z translation and three axes of rotation) were added to the design matrix. The model was estimated for each participant, also taking into account the average signal in the run. After model estimation, we computed the three simple contrasts at the first level (i.e., social-learning conditions against control condition) that were transferred to a second-level group analysis (one sample t-tests) to obtain the brain regions activated in the Teaching condition (Teaching>Control), in the Observation condition (Observation>Control), in the Reverse engineering condition (Reverse engineering>Control) and between the Teaching and Observation condition (Teaching>Observation) to isolate the pure teaching component. Afterwards, we entered maps of parameter estimates in a one-way ANOVA, with Social-learning (Teaching, Observation, and Reverse engineering) as a within-subject factor, to identify brain regions where BOLD activity significantly differed across the three conditions. We present results maps with a significance threshold set at *p* < .05 with family-wise error (FWE) correction.
<figure><img src="image_10.png" /></figure>

Seed selection and analysis

A total of 11 spherical seed ROIs (radius = 5mm) were created in the MNI standard space (see Table 1). We used literature-defined ROIs to ensure the independence of ROI selection. All our seeds were based on fMRI studies (activation peaks). First, we defined each spherical seed involved in the technical-reasoning network: The left area PF, which has been demonstrated central for technical reasoning<sup>14,31</sup> and the left IFG, which has been repeatedly identified as part of the network but whose role in this network remains unclear.<sup>23</sup> Second, we defined each spherical seed involved in the mentalizing network<sup>34</sup>: ACC, which supports the perspective-taking component<sup>34</sup> (localizer in Molenberghs et al.<sup>35</sup>), bilateral temporal poles, which are associated with knowledge about persons<sup>34</sup> (localizer in Molenberghs et al.<sup>35</sup>), the left STS, which is associated with intention understanding<sup>34</sup> (localizer in Hodgson et al.<sup>40</sup>). And the right MTG (localizer in Hodgson et al.<sup>40</sup>), which is known to be involved in communicative gestures<sup>37</sup> and communicative intent.<sup>38</sup> Finally, the tool-use∩action observation network (all taken from Reynaud et al.<sup>31</sup>): The IPS, which is involved in the motor simulation of hand movements and more precisely grasping preparation,<sup>51</sup> the bilateral ITG, which is involved in biological motion perception<sup>52-54</sup> and the left MFG, which might be involved in action-goal perception.<sup>31</sup>