# Insights into the cognitive evolution of genus Homo: Eye-tracking analysis of stone tool recognition in trained versus novice modern humans

Chloé Bryche, Giovanni Federico, Arthur Seye, Arnaud Fournel, François Osiurak

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Chloé Bryche, Giovanni Federico, Arthur Seye, Arnaud Fournel, François Osiurak. Insights into the cognitive evolution of genus Homo: Eye-tracking analysis of stone tool recognition in trained versus novice modern humans. Current Research in Behavioral Sciences, 2026, 10, pp.100205. 10.1016/j.crbeha.2026.100205. hal-05504601

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# Insights into the cognitive evolution of genus *Homo*: Eye-tracking analysis of stone tool recognition in trained versus novice modern humans

Chloé Bryche<sup>a, *, D</sup>, Giovanni Federico<sup>b</sup>, Arthur Seye<sup>a</sup>, Arnaud Fournel<sup>a</sup>, François Osiurak<sup>a, c</sup>

## ARTICLE INFO

### Keywords:

- Stone tools

- Eye-tracking

- Cognitive archaeology

- Visual attention

- Technical knowledge

## ABSTRACT

Eye tracking technologies have recently been used to analyze the visual exploration of stone tool stimuli by modern humans to investigate the cognition of our ancestors. As a tooling species, modern humans show remarkable abilities when it comes to understanding and manipulating complex tools, such as a microscope. Nevertheless, our earliest technological steps, at least the first ones for which we have archaeological evidence, were stone tools, which today might appear easier to grasp than a highly sophisticated surgical telemanipulator. However, the complexity and diversity of lithic industries require a certain level of knowledge to be fully understood. Thus, the question remains whether modern humans can truly extract relevant features from stone tools to distinguish them, for example, from stones fractured by uncontrolled percussion (broken stones), which might look like the first lithic industries. Naive participants, divided into two groups with one receiving a training about stone tool features and the other not, were asked to distinguish stone tools from broken stones. Results show that participants needed a minimal level of knowledge to distinguish choppers or handaxes from broken stones. Nevertheless, trained participants were still not able to correctly classify unknown types of stone tools as tools, suggesting that they needed deeper knowledge to transfer their skills. Our findings underline the important role of technical knowledge in our ancestors' expertise in stone knapping.

## 1. Introduction

Tool use refers to the external use of an object in the environment, either to modify the object itself or to modify another object (Shumaker et al., 2011). Archaeological evidence shows that stone tools have accompanied our ancestors for millions of years, with the earliest records dating back at least two to three million years ago (Semaw et al. 1997, 2003; Harmand et al. 2015; Lewis and Harmand 2016). Studying stone tools and fossil remains goes some way to getting around the problem of the non-fossilizable mind (Nowell et al. 2011; Overmann and Coolidge 2019; Overmann and Wynn 2019) when it comes to understand the cognitive evolution of our species (Renfrew and Zubrow, 1994; Renfrew et al. 2008; Coolidge and Wynn 2016). Stone tool material provides insights into the cognitive evolution of our species by showcasing the development of complex thought processes and motor skills along the increasing complexity of lithic industries (Muller et al. 2017;

Wynn et al. 2021). Their production requires advanced cognitive abilities such as planning and working memory (Coolidge and Wynn 2005; Haidle 2010; Vaesen 2012; Stout et al. 2015). Associated fossil remains, especially the skulls, reflect anatomical changes in hominins, such as the cranial expansion evocating a cognition complexification (Neubauer 2014; Bruner 2017; Pestana et al. 2023). However, it has also been argued that the earliest stone tools may not have been fundamentally different from ape tools (Wynn and McGrew, 1989), which underlines the ongoing debate about the cognitive demands of early toolmaking (Toth and Schick, 2018). While skull morphology and lithic remains shed light on cognitive evolution, the anatomy of the hand is also essential for understanding the development of toolmaking capacities (Kivell et al., 2023). Anatomical specializations such as changes in thumb morphology and the development of precision and power grips provided the manipulative capacities required for increasingly complex lithic industries (Napier 1962; Susman 1998; Almécija et al. 2010;

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

<footnote><sup>b</sup> Laboratory of Experimental Psychology and Cognitive Neuroscience, Department of Education, Psychology and Communication, Suor Orsola Benincasa University, Naples, Italy</footnote>

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

<footnote>* Corresponding author at: Laboratory website.</footnote>

<footnote>E-mail address: chloe@bryche.fr (C. Bryche).</footnote>

<footnote>URL: https://emc.univ-lyon2.fr/equipes/memoire-emotion-et-attention-mea/bryche-chloe (C. Bryche).</footnote>
Marzke 2013; Kivell 2015). Tool handling involves haptic feedback on weight, shape, and surface properties, thereby enriching the perception of a tool's functional possibilities (Silva-Gago et al. 2019; Fedato et al. 2020).

While haptics plays an indisputable role in tool use, vision constitutes another fundamental modality. In recent years, the use of eye-tracking technology has brought new approaches to the question of the cognitive processes underlying the progressive complexification of stone tools through the evolution of our species (Silva-Gago et al. 2021). By observing eye movement patterns during the manipulation or observation of stone tools, researchers analyze the visual attentional mechanisms underpinning tool-making activities (Silva-Gago et al. 2021, 2022a, 2022b). Visual attention refers to the cognitive processes that allow individuals to focus on specific visual information while filtering out distractions (Doran and Hoffman 2010; Borji and Itti 2013). Visual attention might have been crucial during the Paleolithic since it is essential for tool production and/or manipulation because of the needed eye-hand coordination (Johansson 2001; Gao et al. 2010; Szabo et al. 2020). The eye-tracking methodology offers a novel perspective into how visual exploration of stone tools may reflect domain-specific knowledge, offering indirect access to the cognitive faculties that underpinned the evolutionary trajectory of the genus *Homo*.

Eye-tracking research based on stone tools tends to focus on the tools' action-related properties used as regions of interest, that is, the knapped parts of stone tools. Thus, a recent study found that during both visual exploration and manipulation of stone tools, the core regions and knapped surfaces attract the most attention (Silva-Gago et al. 2021). Additionally, visual attention patterns are consistent across tool types regardless of whether the tools are physically manipulated, although significant differences are observed in specific regions between tools such as choppers and handaxes. This difference might be explained by the tool elongation, with handaxes being more elongated and choppers more rounded. Visual attention correlates with handaxe morphology, particularly elongation, while chopper attention is influenced mainly by tool weight (Silva-Gago et al. 2022b). However, modern humans are no longer accustomed to using stone tools, and it is known that the cognitive processing of a familiar tool is different compared to an unfamiliar one (Vingerhoets 2008; Reynaud et al., 2016, 2019). Thus, during both visual exploration and manipulation of stone tools, archaeologists (i.e., experts) were shown to focus more on the middle region and knapped surfaces, while naïve individuals (i.e., without prior knowledge) concentrated on the core part of the tool (Silva-Gago et al. 2022a). In broad terms, experts focus more on relevant features to visually extract useful information from the tool.

The main goal of the present study was to provide new insights into the role played by expertise on the visual recognition of stone tools in modern humans. Producing and understanding stone tools clearly requires a certain level of expertise in our current species, underscoring the technical proficiency of our knapper-ancestors. Yet it remains an open question whether a comparable level of expertise is also necessary for mere visual recognition, which engages different processes altogether. Stone tools, especially Oldowan ones, can visually resemble unintentionally fractured stones (Groucutt and Petraglia 2012; Niang, 2014), known as geofacts (e.g., a stone dropped from a cliff). Our objective here is to examine whether naïve modern humans are able to identify stone tools. Thus, images of stones fractured without controlled knapping patterns were presented between stone tool stimuli during an eye-tracking experiment, and participants were asked to judge whether each stimulus was a tool or not. These stones fractured without controlled knapping patterns are hereafter referred to as broken stones for ease of reading; they were included to resemble naturally fractured stones or geofacts, lacking diagnostic features of technical production. We, therefore, formed two groups, one composed of novice participants and the other of participants who had received brief training in stone tool recognition and understanding. We also manipulated the orientation of the visual stimuli, presenting them either in up or down

orientations since the knapped parts of stone are generally opposed to their base.

Our hypotheses were as follows. First, we hypothesized that novice participants would be less efficient than trained participants in differentiating stone tools from broken stones, especially with choppers, due to their potential similar appearance (H1a). Trained individuals should thus demonstrate higher efficiency in distinguishing stone tools from broken stones, exhibiting distinct visual exploration patterns (H1b) and greater accuracy, especially for choppers (H1c). Since stone tools might require expertise, we kept an unknown tool (i.e., not explained or presented to trained participants) to question the possibility of an analogical knowledge transfer between what they had learned and the unknown tool. The capacity of analogical knowledge transfer refers to the idea that prior knowledge acquired from different opportunities and experiences can be applied to a novel situation, especially to resolve a problem (Novick 1988; Bassok 2003; Nokes 2009). This unknown tool is a cleaver from Acheulean tradition. Thus, we secondly hypothesized that participants who received training in the identification of Acheulean handaxes will classify the unknown tool more accurately and exhibit different visual exploration patterns (H2), potentially relying on analogical transfer of knowledge from previously learned tools, namely Acheulean handaxes (Bobrowicz et al., 2020). Third, we also hypothesized that the orientation of the stone tools stimuli would significantly affect both the behavioral response (i.e., judging if it was a broken stone or a stone tool) and visual exploration patterns of novice participants, who were expected to show variations in reaction time (H3a) and accuracy (H3b) based on tool orientation. Conversely, trained participants were expected to exhibit consistent reaction times and accuracy regardless of tool orientation (H3c). With this experiment, we expected to clarify the level of expertise needed for modern participants to identify specific stone tools, and to explore how these results may, when interpreted cautiously, help frame hypotheses about the cognitive demands faced by early hominin tool users.

## 2. Method

### 2.1. Pre-registration

The study design, hypotheses, and analysis plan were pre-registered on OSF (https://osf.io/492de) after the collection of data but prior to the analysis of any data.

### 2.2. Participants

A total of 42 participants (female/male/non-binary gender: 28/14/0) were recruited to take part in the experiment. Inclusion criteria required them to be right-handed with normal vision and aged between 18 and 35, declaring having no prior knowledge of paleoanthropology or lithic industries. This criterion was elaborated based on previous studies that also worked with participants declaring themselves novice and being tested on their baseline knowledge prior to training (Bryche et al., 2025). Since Western participants may already have heard about and/or seen stone tools at some point in their lives, we chose to use the term novice rather than naïve, as it would be unrealistic to recruit adult participants who had genuinely never encountered the idea of stone tools before. Participants were asked to sign a consent form for participation in the study, which the ethics committee of the Lyon University validated (CER-UdL n°2023-06-15-005). All experiments took place under the same experimental conditions (e.g., light conditions) in the same laboratory, room, and equipment at EMC Laboratory.

### 2.3. *Equipment and eye tracking data collection*

Eye-tracking data were collected using the EyeLink 1000 eye tracker (SR Research, Ontario, Canada). The EyeLink 1000 is a high-precision eye-tracking system with a sampling rate of 1000 Hz, allowing for
accurate and reliable measurement of eye movements. Participants had to rest their heads on a fixed chinrest to ensure consistent calibration across all individuals. The distance between the eyes and the eye tracker was 50 cm, and the distance between the eyes and the display screen was 100 cm. Eye movement data were recorded throughout the task. Key metrics collected included the number and durations of fixations. Fixations were defined as periods where the eye remained relatively still, allowing for detailed examination of specific regions of interest. The number of fixations refers to the count of times a participant's gaze stops on specific locations within a visual scene, here real pictures of stone tools or broken stones, while fixation duration measures the amount of time the gaze remains stationary at these points. The data was collected using OpenSesame version 3.3.1.1.

## 2.4. Stimuli

The 64 stimuli were divided into two main groups: stone tools and broken stones. Each stimulus was a standardized photo on a grey background, with each object occupying the same height in the display window. The images were 1280 pixels wide by 720 pixels high. The broken stones group contained eight stones, but there was a total of sixteen images. Each stone was photographed twice: once in an upward orientation and once in a downward orientation, after being randomly mechanically broken with a hammer by striking them in the middle without any intent to shape them. Note that these broken stones do not serve as a control for tool functionality, but as a proxy for naturally broken stones lacking intentional knapping and were included specifically to address Hypotheses 1a, 1b and 1c. The stone tools group contained 48 images, divided into three sub-groups, each containing eight tools for a total of 16 images. Each sub-group represented a specific type of stone tool: chopper, handaxe, and cleaver. To test Hypotheses 3a, 3b and 3c, each tool was modified in both upward and downward orientations, resulting in the 16 images per sub-group. These stimuli were carefully selected from various photos to ensure diverse representation of each tool type (for example see Fig. 1). The stimuli for broken stones can be accessed on OSF (https://osf.io/vde94/), whereas the other stimuli are excluded due to licensing restrictions on the pictures used in their creation.

## 2.5. Procedure

**Training.** Participants were divided into two groups to test all the Hypotheses. The first group, the "novice" group, consisted of participants declaring having no prior knowledge of stone tools (e.g., they had not participated in archaeological studies or visited a museum exhibit about evolution). The second group, known as the "trained" group, consisted of participants who initially declared themselves being novices but received brief training before the experiment to help them understand and identify stone tools. The training consisted of a five-minutes video giving a quick overview of stone tools. Participants were trained on two tools, chopping tools and handaxes, with the cleaver intentionally

excluded to address Hypothesis 2. All the illustrations used in the training exclude those used as stimuli. Following the training, participants were asked to fill in a brief questionnaire to check whether the training had enabled them to acquire sufficient knowledge to distinguish a handaxe from a chopper.

**Experimental protocol.** After providing informed consent, participants were seated in front of a computer screen and keyboard and had to rest their heads on a chinrest to ensure the correct distance of the eyes from the screen and avoid unwanted head movements. The eye tracker was positioned in front of them, and participants were explained how it works. Before starting the experimental task, the eye tracker was calibrated to detect the pupil and track its movements: the participants completed a nine-points calibration procedure. Once calibrated, the experiment could begin. Participants had to place their hands on the keyboard, ready to give their responses. The session began with a practice trial to familiarize participants with the task. Each participant viewed a series of images displayed on a computer screen, with data recorded on eye movements throughout the session. Each participant was asked to evaluate the 64 stimuli randomly presented to them. Both groups of participants were asked to evaluate the same stimuli. The evaluation consisted of choosing whether the stimulus previously observed was a stone tool or not. To do this, participants were given an AZERTY keyboard to record their responses, with the L key for "Yes" and the S key for "No". They were asked to respond as quickly and accurately as possible. The experiment took around 15 min, not including training for the trained group and reading the informed consent or eye-tracker technology explanation.

**Experimental trial.** First, a fixation cross appeared and disappeared when the participant's gaze was centered on it to ensure that visual exploration of the stimuli always started from the same starting point. The stimulus then appeared for five seconds; the 64 stimuli were presented in a random order. A response screen then was displayed, inviting the participant to respond using the keyboard. Then, a blank screen appeared for five seconds, resetting the visual pattern. This design was repeated for 64 trials, not including the practice trials, for each participant.

## 3. Results

We used regression modeling in R (Ihaka and Gentleman, 1996) with the lmerTest package (Kuznetsova et al. 2017) to fit all of our linear models for the four dependent variables ('Accuracy', 'Response time', 'Number of fixations', 'Duration of fixations'), with 'Knowledge' (Novice vs. Trained), 'Stimulus Type' (Broken Stone, Chopper, Cleaver, Handaxe), 'Orientation' (Down vs. Up), and their interactions as fixed effects, and 'Participant's Identity' as a random effect. When significant, we used post-hoc pairwise t-tests with a Holm-Bonferroni correction. To assess the reliability of our effect estimates, we computed bootstrap 95 % confidence intervals (2000 iterations) and considered an effect reliable when its estimate did not cross the interval.

<figure><img src="image_2.png" /><figcaption>**Fig. 1.** Examples of stimuli used in upward orientation: A – Broken Stone; B – Chopper; C – Cleaver; D – Handaxe.</figcaption></figure>
## 3.1. Accuracy

Results are shown in Fig. 2. We found a significant main effect of stimulus type, F(3,56.02) = 54.82, *p* < 0.001 (Fig. 2a). Post-hoc pairwise tests revealed significant differences between all the stimulus types (all *p* < 0.002). The accuracy was the highest for the handaxe (M<sub>Handaxe</sub> = 92.4 %), followed by the broken stone (M<sub>Broken</sub> = 75.1 %) and the chopper (M<sub>Chopper</sub> = 56.5 %), while the cleaver (M<sub>Cleaver</sub> = 46 %) had the lowest accuracy. A main effect of knowledge was also reported, F (1,36.21) = 11.12, *p* = 0.002, trained participants (M = 73.1 %) obtaining better performance than novice participants (M = 60.9 %) (Fig. 2b). Finally, the stimulus type and knowledge interaction reached significance, F(3,2576.41) = 52.15, *p* < 0.001 (Fig. 2c). Post-hoc pairwise t-tests revealed significant differences between trained and novice participants for the chopper (M<sub>Trained</sub> = 76.79 %; M<sub>Novice</sub> = 32.24 %, *p* < 0.001) and the handaxe (M<sub>Trained</sub> = 96.13 %; M<sub>Novice</sub> = 88.39 %; *p* < 0.001) but not for the cleaver (M<sub>Trained</sub> = 43.15 %; M<sub>Novice</sub> = 48.51 %; *p* = 0.66) and broken stones (M<sub>Trained</sub> = 76.42 %; M<sub>Novice</sub> = 74.04 %; *p* = 1). No main effect was found for orientation, nor any interaction with this variable (all *p* > 0.23), providing no evidence for Hypotheses 3a, 3b and 3c. The results for accuracy suggest that training significantly enhances the ability of participants to recognize certain types of stone

tools, particularly choppers and handaxes, compared to novice participants, confirming Hypotheses 1a and 1c. At the same time, no significant differences were observed for cleavers and broken stones, indicating that some tool types may inherently be more challenging to identify regardless of training, thus not supporting Hypothesis 2.

## 3.2. Response time

Results are shown in Fig. 3. We found a significant main effect of stimulus type, F(3,56) = 3.87, *p* = 0.014. Post-hoc pairwise tests revealed significant differences between handaxe and chopper (M<sub>Handaxe</sub> = 826 ms; M<sub>Chopper</sub> = 957, *p* = 0.002) and handaxe and broken stone (M<sub>Handaxe</sub> = 826 ms; M<sub>Broken</sub> = 917 ms, *p* = 0.039), but not between other stimulus types (all *p* > 0.1) such as the cleaver (M<sub>Cleaver</sub> = 920 ms). The main effect of knowledge approached significance, F(1,36.9) = 3.03, *p* = 0.09, with trained participants having a shorter mean response time (M<sub>Trained</sub> = 796 ms) than novice participants (M<sub>Novice</sub> = 1014 ms). The interaction between knowledge, stimulus type, and orientation was marginally significant, F(3,2577.0) = 2.10, *p* = 0.10. No main effect was found for orientation, nor any other interaction involving this variable (all *p* > 0.27). These results indicate that the type of stimulus significantly affected response times, with chopper and broken stone showing

<figure><img src="image_3.png" /><figcaption>**Fig. 2.** Accuracy in percentage by Stimulus Type (a), Knowledge (b), and Stimulus Type and Knowledge (c). Boxplots indicate the upper quartile, median and lower quartile, with whiskers extending to the most extreme data point that is no more than 1.5 times the interquartile range from the edge of the box.</figcaption></figure>
<figure><img src="image_4.png" /><figcaption>Fig. 3. Response time in milliseconds by Stimulus Type and Knowledge. Boxplots indicate the upper quartile, median and lower quartile, with whiskers extending to the most extreme data point that is no more than 1.5 times the interquartile range from the edge of the box.</figcaption></figure>

longer response time compared to handaxe. The effect of training on response time was less clear and could reflect a slightly faster processing in trained participants, providing only partial support for Hypothesis 1c. Orientation did not significantly affect response time, which does not support Hypothesis 3b

### 3.3. Number of fixations

Results are shown in Fig. 4. Note that the number of fixations corresponds to the mean of the two presentations of each stimulus and their defined regions, which explains the presence of decimal values. The analysis revealed a main effect of stimulus type, F(3,14,133.0) = 5.46, *p* < 0.001 (Fig. 4a). The chopper showed higher number of fixations (*M* = 1.18), followed by the cleaver, then the handaxe and finally the broken stone (all *M* ≈ 1.15). Post-hoc pairwise tests revealed significant differences between chopper and broken stone (*p* < 0.001), also between chopper and cleaver (*p* < 0.002) and between chopper and handaxe (*p* < 0.002), but not between the other stimuli (*p* > 0.7). A main effect of knowledge was also found, F(1,39.7) = 4.14, *p* = 0.048, trained participant (*M* = 1.17) showing a higher number of fixations compared to novice participants (*M* = 1.14) (Fig. 4b). However, orientation did not show a significant effect, *p* = 0.2, which is consistent with the lack of support previously found for Hypotheses 3a, 3b and 3c. These results highlight the significant influence of stimulus type and expertise on visual fixation behavior, with trained participants having higher numbers of fixations, in line with Hypothesis 1b

### 3.4. Duration of fixations

Results are shown in Fig. 5. The main effect of stimulus type was significant, F(3,14,131.4) = 20.58, *p* < 0.001 (Fig. 5a). The handaxe shows longer duration of fixations (*M* = 382 ms), followed by the broken stone (*M* = 357 ms), then the chopper (*M* = 354 ms) and finally the

cleaver (*M* = 350 ms). Post-hoc pairwise tests revealed significant differences between handaxe and broken stone (*p* < 0.001), also between handaxe and chopper (*p* < 0.001) and between handaxe and cleaver (*p* < 0.001), but not between the other stimuli (*p* > 0.07). The main effect of knowledge approached significance, F(1,39.7) = 3.02, *p* = 0.090, with novice having longer duration of fixations (*M* = 373 ms) compared to trained participants (*M* = 347 ms). The main effect of orientation was not significant, F(1,14,130.8) = 0.24, *p* = 0.62, but the interaction between knowledge and orientation was, F(1,14,130.8) = 4.63, *p* = 0.031 (Fig. 5b). Post-hoc pairwise *t*-tests revealed significant differences between trained and novice participants for the upward orientation (*M*<sub>Trained</sub> = 349 ms; *M*<sub>Novice</sub> = 369 ms, *p* < 0.001) and the downward orientation (*M*<sub>Trained</sub> = 345 ms; *M*<sub>Novice</sub> = 377 ms, *p* < 0.001). These results suggest that the interaction between knowledge and orientation can be explained by longer duration of fixations in novice participants compared to trained participants, with a higher difference between the two groups for the downward orientation (*M*<sub>Novice-Trained</sub> = 32 ms) than for the upward orientation (*M*<sub>Novice-Trained</sub> = 20 ms). Overall, these results indicate that the type of stimulus, the level of knowledge and the orientation significantly influence the duration of fixations, supporting Hypothesis 1b and to a lesser extent Hypothesis 3c.

## 4. Discussion

Countless tools constantly surround modern humans, but many people may not be familiar with how our ancestors created the firsts of them from stone. This lack of familiarity might make it difficult to distinguish between a stone tool intentionally knapped by our ancestors and a stone that was randomly broken without any specific purpose (e. g., falling from a cliff). To test this, some participants were trained to recognize different types of stone tools, whereas others were not. The results showed that people could learn to identify specific types of stone tools with rapid training, confirming Hypotheses 1a, 1b and 3c.
<figure><img src="image_5.png" /><figcaption>**Fig. 4.** Number of fixations by Stimulus Type (a), and Knowledge (b). Boxplots indicate the upper quartile, median and lower quartile, with whiskers extending to the most extreme data point that is no more than 1.5 times the interquartile range from the edge of the box. Values represent the mean number of fixations per area of interest for each stimulus, as each stimulus was viewed twice by each participant.</figcaption></figure>

However, they still struggled to generalize this knowledge to unfamiliar tool forms, providing no support for Hypothesis 2. We will now discuss the key findings of the present study in turn.

Our first key finding concerns the level of knowledge, influencing both behavioral results and visual exploration. The knowledge level significantly affected accuracy: trained participants were more accurate than novice participants, a result that also validates the efficacy of our training. The level of knowledge might influence experts' ability to focus visually on essential elements, inhibiting unnecessary detail (Antes and Kristjanson 1991; Jarodzka et al. 2010; for a review: Gegenfurtner et al. 2011). Our analysis also revealed an approaching significant difference in response times between novice and trained participants when classifying the stimuli. Trained individuals eventually demonstrated shorter response times across all tool types. This pattern might be consistent with the idea that their training made them more familiar with the stimuli, providing a faster evaluation process (Harel et al. 2011). The analysis might reach significance with longer and fuller training or with real experts (e.g., archaeologists specialized in stone tool knapping) still compared to novices (Tanaka and Gauthier, 1997; Palmeri et al. 2004). The analysis of visual exploration also revealed significant differences between trained participants and novices. The interaction between the

type of stimulus and participant knowledge was particularly insightful. Trained participants showed more fixations when observing the stimuli, specifically choppers, compared to novice participants. This emphasizes the impact of domain-specific knowledge on directing visual attention and supports the idea that experts and novices process visual information differently (Carmichael et al. 2010; Gegenfurtner and Van Merriënboer 2017), which also goes along with the idea that the level of familiarity impacts the visual processing of stimuli (Federico et al., 2021b). Indeed, the discriminating action-related properties of the chopper versus a broken stone can be quite opaque to novice participants. Trainees would, therefore, have had a more sustained visual exploration of this tool to evaluate them (Silva-Gago et al. 2022a), which also corresponds to their much better accuracy in classifying this tool. Moreover, the finding that this better accuracy was not observed for broken stones reinforces the concept that individuals trained to identify choppers display more fixations than novices based on their technical understanding of the tool rather than its overall shape, going along with our hypothesis. It is indeed plausible that the choppers were more difficult to identify for naïve participants because their flake scar negatives are less standardized and less salient. Novices, unfamiliar with the visual cues associated with fractures produced by controlled knapping,
<figure><img src="image_6.png" /><figcaption>Fig. 5. Duration of fixations in milliseconds by Stimulus Type (a), and Knowledge and Orientation (b). Boxplots indicate the upper quartile, median and lower quartile, with whiskers extending to the most extreme data point that is no more than 1.5 times the interquartile range from the edge of the box.</figcaption></figure>

might fail to interpret irregular flake scars as signs of intentional
shaping. In contrast, handaxes often present clearer and more symmet-
rical flake removals, particularly those made from fine-grained materials
such as flint, where the negative scars are more visible and uniform.
Symmetry has long been considered a perceptual cue associated with
intentionality and expertise in tool production (Wynn 1995; Cantalupo
and Hopkins 2001; Hodgson 2015), and its presence likely facilitated
recognition among participants. Moreover, archaeological and neuro-
archaeological research suggests that the visual salience of symmetry in
handaxes may recruit higher-order perceptual and cognitive mecha-
nisms related to aesthetic and technical evaluation (Stout et al. 2011;
Bruner and Iriki 2016; Putt et al. 2017;). Therefore, the combination of
material visibility, clearer flake patterning, and morphological symme-
try probably contributed to the easier identification of handaxes
compared to choppers. However, trained participants outperformed the
naive ones in identifying choppers, illustrating the effectiveness of
theoretical knowledge. What remains unclear, however, is which spe-
cific cues guided their recognition, whether it relied more on perceiving
a global form or on identifying specific knapping negatives typically
associated with Oldowan tools, or perhaps a combination of both. Future
studies should aim to disentangle these possibilities, distinguishing be-
tween knowledge based on overall morphological prototypes and that
derived from an understanding of the knapping processes involved in
stone tool production.

Our second key finding concerns the behavioral and visual

processing of stimuli presenting broken stones. When regarding to the
general classification and visual exploration of broken stones, the results
could testify to an additional need for knowledge. Furthermore, the fact
that broken stones were not visually treated significantly differently
suggests that the question of stone tools' action-related properties may
be subtler. Traditionally, it is assumed that the visual perception of a
tool's action-related properties is based on extracting features related to
its use (Vingerhoets et al. 2009; Silva-Gago et al. 2022b). Participants
could have visually processed broken stones to extract physical and
technical properties common to stone tools. These physical properties
would, therefore, not necessarily be related to the intention to find the
purpose for which the tool is used rather than whether the physical
properties present could be of potential use. Ultimately, it was perhaps
through broken stones that a general transfer of knowledge about an
object's potential use as a tool was achieved without any distinction due
to the level of knowledge about stone tools. Another explanation may lie
in how the broken stones were produced, and future experiments would
benefit from using real geofacts to provide a more realistic comparison.

Our third major finding concerns the treatment of tools deliberately
left unknown to the trained group, which seems to reveal a lack of
analogical transfer of knowledge for cleaver evaluation. Indeed, even
though the trained participants performed better in classifying chop-
pers, testifying to their learning (Viggiano et al. 2006; Robson et al.
2021), they did not outperform the novice in correctly classifying the
cleaver; the unknown tool for both groups. This shows that even after
receiving meaningful information about a tool from the same tech-
nocomplex, here the handaxe belonging to Acheulean, the level of
expertise required to classify all tools correctly cannot be based solely on
a transfer of skills. Although cleavers and handaxes belong to the same
technocomplex, their production schemas differ. As a result, the visual
cues left by knapping tend to be clearer on handaxes than on cleavers,
especially since the handaxes used in the experiment were more sym-
metrical and displayed more salient flake negatives. This may explain
why cleavers were not as well recognized, underlining the need for
broader knowledge about how a stone tool is produced, especially
depending on the raw material, rather than relying solely on the ability
to detect intentionality through more complex prototypical forms. This
leads future research to go further and question what kind of learning
could help participants transfer their knowledge more effectively;
perhaps by focusing on the making process rather than on prototypical
form alone. This points to a certain persistent opacity of stone tools,
requiring further training of modern humans (Roux and David 2005;
Geribás et al. 2010; Nonaka et al. 2010; Pargeter et al. 2019; Muller et al.
2022), which is consistent with the expertise required by specialists to
develop effective classification systems for stone tools (Wright 1992;
Whittaker 1994). This also leads us to reconsider the effectiveness of
visual exploration of poorly classified tools. This last point might suggest
a beginning of knowledge transfer in the trained group, as their fixation
durations were shorter, but the classification remains incorrect. To
better understand the reasons for this specific interaction, it is necessary
to replicate this study by increasing the plurality of familiarity levels
with different levels of expertise. However, confirmed experts (e.g.,
archaeologists) would already know how to identify a cleaver.

Finally, our last main finding concerns the possible influence of a
tool's orientation on its visual evaluation and exploration, but not on
behavioral responses, which is not in line with Hypotheses 3a, 3b, and
3c. Contrary to our expectations, the main effect of orientation on the
number of fixations was not significant. This finding implies that the
orientation of the stimuli did not independently impact the number of
fixations. At first, this might let think that it is due to the participants'
ability to mentally rotate objects (Shepard and Metzler 1971; Tarr and
Pinker 1989), which is linked to higher spatial reasoning skills (Just and
Carpenter 1985; Guillot et al. 2007; Ramful et al. 2017). Since the
orientation did not influence novice or trained participants' number of
fixations, this could mean that this spatial reasoning skill is not sensitive
to the stimulus orientation for stone tools, at least until basic levels of
knowledge. On the other hand, when it comes to the duration of fixa-
tions, there is an effect of orientation interacting with the level of
knowledge with trained participants having shorter fixation durations.
This suggests that the effect of orientation on gaze duration is modulated
by the participant's level of training, contrary to what the number of
fixations' analysis might suggest. This interaction underscores the
importance of considering both cognitive factors (e.g., training) and
perceptual factors (e.g., orientation) in understanding visual attention.
This could be attributed to trainees' ability to better identify relevant
features and efficiently allocate their visual attention. More generally,
the finding that trained participants had shorter gaze durations is
consistent with previous research indicating that experts can process
information more quickly due to their familiarity and expertise
accordingly to their domain of knowledge like chess players (Sheridan
and Reingold 2014, 2017).

It could nevertheless be argued that the absence of a manual
component remains a limitation. Tool perception is not purely visual but
also involves touch, grasp, and manipulation, which provide crucial
feedback on weight, balance, texture, and edge sharpness (Fedato et al.
2020). This kind of brain-hand feedback has been highlighted as central
to human tool selection and use (Marzke 2013; Williams-Hatala et al.
2018) and is further supported by fossil evidence showing that re-
finements in thumb morphology and manual dexterity played a crucial
role in the emergence of efficient tool manipulation (Karakostis et al.,
2021). Such haptic information may be especially important for naïve

participants, who might otherwise struggle to identify the functional
features of stone tools that define their affordances. From this perspec-
tive, stone tool exploration is obviously a multimodal process, even if the
present study has deliberately focused on the visual dimension. This
choice aligns with previous work that has also considered it effective to
separate manipulation and non-manipulation paradigms, since the
relationship between the hand and the tool represents a major variable
component of tool perception (Silva-Gago et al. 2019). Thus, our results
provide a methodological basis from which future work can more
effectively integrate additional modalities, such as manual exploration,
to capture the full range of perceptual and cognitive processes involved
in stone tools recognition and understanding.

Moreover, this call for a multimodal approach resonates with recent
findings in neuroarchaeology, a field at the intersection of neuroscience
and paleoanthropology, which highlight the importance of brain–hand
coordination in tool use. These brain imaging studies, using technologies
such as electroencephalography (EEG) and combined EEG-
electromyography (EMG), have shown that different grasp types (e.g.,
power vs. precision grips) and levels of expertise modulate neural acti-
vation patterns in frontoparietal networks (Affinito et al. 2024, 2025).
While these studies focus on tool execution rather than tool recognition,
they provide valuable context for interpreting how expertise might
shape perceptual as well as motor processes. Additionally, the type of
expertise itself can influence the efficiency of toolmaking actions. While
theoretical knowledge provides a conceptual understanding of stone
tools, only practical expertise developed through active knapping leads
to efficient and controlled actions, characterized by more economical
muscle engagement (Eteson et al. 2024). This further highlights the
importance of combining theoretical and practical dimensions of
expertise when studying tool-related cognition. Integrating these in-
sights with recognition-based paradigms, such as the one employed
here, could therefore open promising avenues for future interdisci-
plinary research.

5. Conclusion

The findings of this study on the visual exploration of stone tools by
modern humans highlight the difficulty modern humans face when
recognizing early tools, potentially reflecting the need for learned
knowledge in past populations. The differentiation in response times and
fixation patterns between trained and novice participants highlights the
complexity of cognitive processing involved in tool recognition and
classification. The increased number of fixations among trained in-
dividuals suggests that expertise in stone tool classification is not just a
matter of learned knowledge but also involves deeper, more nuanced
cognitive engagement. This aligns with theories that propose advanced
cognitive capabilities in early humans, such as enhanced working
memory and abstract reasoning skills, were crucial for the development
and use of complex tools (Wadley 2013; Wynn and Coolidge 2014; Stout
et al. 2015). Moreover, the persistent opacity of certain tools, like the
cleaver, even to trained participants, underscores the idea that tool-
making and tool-use knowledge in our ancestors likely required exten-
sive practice and hands-on experience. This experiential learning
process may have driven the evolution of higher cognitive functions,
including enhanced problem-solving abilities and spatial reasoning
(Bruner and Iriki 2016; Bruner et al. 2018).

The finding that stone tools are opaque to modern participants without prior knowledge leads us to reconsider two points. First, modern humans are not de facto able to interpret stone artifacts as tools through visual inspection alone, indicating limitations in extracting diagnostic technical features required for a tool versus non-tool judgment; even though the stimuli were selected based on modern archaeological typological classifications that may not reflect past emic categories. This leads us to the second point. If modern stone artifact categories, as defined by archaeologists, reflect in part the tool distinctions recognized by past hominin toolmakers, then even some of the oldest forms of lithic
technologies imply a degree of knowledge and expertise. Such expertise was not inherent to visual recognition alone but likely developed over time through repeated interactions with the environment and materials (Malafouris, 2013, 2019, 2023).

This continual improvement of perceptual and motor skills likely played a crucial role in shaping the cognitive abilities of early humans, ultimately leading to the advanced tool cultures seen in the archaeological record. The capacity to visually and mentally manipulate objects, differentiate functional attributes from non-functional ones, and transfer knowledge across different types of tools would have provided a significant adaptive advantage, contributing to the survival and success of our species. Therefore, the examination of visual exploration in the context of stone tools not only enhances our understanding of current cognitive processes, but also provides valuable insights into the evolutionary pathways that have influenced human cognition. It sheds light on the development of specific brain areas during the Paleolithic period and the role they may have played in the evolution of our species. This aligns with the idea that a tool-hand-brain relationship has influenced the biological evolution of our species (Washburn 1960; Calvin 1982; Stout and Chaminade, 2007; Iriki and Taoka 2012; Ko, 2016), as evidenced by the development of complex stone tools thereby driving anatomical changes like increased brain size and enhanced neural connectivity (Stout et al. 2008; Coolidge and Wynn 2016; Bruner, 2018).

In conclusion, our findings demonstrate that both the nature of the visual stimuli and the expertise of the participants significantly influence visual attention patterns in terms of the number of fixations and their durations. These results contribute to better define how knowledge and cognitive processing interact in visual recognition tasks, particularly in the context of distinguishing between functional tools and non-functional objects. One possibility is that the acquisition of knowledge, whether semantic or technical (Osiurak et al. 2017), modifies visual exploration through a top-down process. Thus, expert individuals do not become better to detect, strictly speaking, the action-related properties of (stone) tools but to recognize them as useful for tool-use actions. Recent studies have provided support for this possibility (Enge et al. 2023; Federico and Brandimonte 2020; Federico et al., 2021a). Obviously, in considering how knowledge shapes visual exploration, it is also important to acknowledge the role of manipulation and the specific properties of the hand-tool interaction, which likely work in concert with vision to influence tool recognition and use. Future research should explore these interactions further, perhaps by incorporating neuroimaging techniques to elucidate the underlying neural mechanisms. In this way, future research integrating both visual and manual dimensions may clarify how the perception of stone tools contributed to the emergence of multimodal expertise that shaped human cognitive evolution. Moreover, it should be kept in mind that this kind of study is carried out on modern humans; a species that has developed technologically to the point of needing specific categories to classify past civilizations and their tools (Holdaway and Douglass 2012). While this classification is necessary for archaeology, it could be argued that what we now separate into distinct types for analytical purposes may simply have been perceived as "sharp tools" by our ancestors (Eren et al. 2025), especially during the earliest lithic industries.

If future hypotheses must integrate manipulation, which we strongly believe they should, then we also need to move beyond dichotomies based purely on prototypical form and instead focus on how a stone can be knapped in different ways to produce sharp edges. Recognizing the "form" of a tool is largely a modern concern (Dibble et al. 2017); for early hominins, what probably mattered more was the availability and production of tools. Focusing on this perspective may guide future research toward integrating both manipulation and technical knowledge, shedding clearer light on the technological evolution of our species and the cognitive abilities that supported it.

## Code and data availability

The codes and data that support the findings of this study are available at https://osf.io/vb8hu/.

## Declaration of competing interest

The authors declare that there are no financial or non-financial competing interests.

## Acknowledgements

We thank all the volunteers who participated in the experiment. This work was supported by grants from the French National Research Agency (ANR; Project TECHNITION: ANR-21-CE28-0023-01; F.O.) and the Région Auvergne-Rhône-Alpes (NUMERICOG-2017-900-EA 3082 EMC-R-2075; F.O.).

## Data availability

There is an OSF link in the main document.

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