# Acting Like an Expert: Analyzing Eye and Movement Behavior when Exploring Archaeological Artifacts

Maxime Dumonteil, Théophane Nicolas, Marc J.-M. Macé, Valérie Gouranton, Yan Leguedois, Ronan Gaugne

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Maxime Dumonteil, Théophane Nicolas, Marc J.-M. Macé, Valérie Gouranton, Yan Leguedois, et al.. Acting Like an Expert: Analyzing Eye and Movement Behavior when Exploring Archaeological Artifacts. DH 2025 - Digital Heritage International Congress, Sep 2025, Sienne, Italy. pp.1-10, 10.2312/dh.20253200. hal-05267741

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# Acting Like an Expert: Analyzing Eye and Movement Behavior when Exploring Archaeological Artifacts

Maxime Dumonteil<fnref n="1" /> ID, Théophane Nicolas<fnref n="4" /> ID, Marc J.-M. Macé<fnref n="2" /> ID, Valérie Gouranton<fnref n="3" /> ID, Yan Leguedois<fnref n="1" />, Ronan Gaugne<fnref n="1" /> ID

## Abstract

This study proposes a novel methodological framework for investigating the visual exploration of archaeological artifacts in both real and virtual environments, integrating eye-tracking and movement data to analyze user behavior. Focused on three vases of varying shapes and decorative registers, the research examines the influence of object characteristics and user expertise on exploration strategies. Our results reveal that while both expert and non-expert participants identify structural and diagnostic features, such as geometric patterns and manufacturing traces, experts engage in more integrative, analytical exploration, focusing on both technical and decorative aspects. In contrast, non-experts prioritize prominent visual cues, often without full comprehension of their significance. These findings demonstrate that visual behaviors are influenced both by the physical characteristics of the artifacts and by the observer's level of expertise. They also show that certain visual cues—such as shape transitions or manufacturing marks—are particularly effective in triggering recognition, even among non-experts. This study contributes to neuro-archaeology by advancing eye-tracking and users' movements analysis as tools for analyzing cognitive processes in archaeological interpretation, offering a robust, reproducible methodology that can be applied to diverse archaeological contexts, enhancing our understanding of how artifacts reveal archaeologically meaningful information through visual perception.

## CCS Concepts

* Applied computing → Archaeology; * Human-centered computing → Virtual reality; * Computing methodologies → Virtual reality;

## 1. Introduction

This article contributes to the neuro-archaeological approach within digital heritage research [RA11, CBMB*23, FWSW22], which seeks to investigate user behavior to assess the cognitive impact of archaeological artifacts—whether physical or digitally reconstructed—and of tasks associated with archaeological practice. Virtual Reality (VR) and Extended Reality (XR) have significantly transformed archaeological research by offering immersive, interactive tools that support hypothesis testing, spatial analysis, and the reconstruction of ancient sites and practices. These technologies allow archaeologists to explore virtual reconstitutions, simulate past environments, and collaboratively evaluate interpretations of archaeological evidence within dynamic, manipulable digital contexts. XR environments also enable both researchers and the general public to engage with artifacts, sites, and historical narratives in a controlled, non-invasive manner, preserving the integrity of physical objects.

By embedding archaeological use cases into virtual environments, XR fosters new modes of interpretation and interaction—whether contextualizing artifacts within their original settings

[GBL*22], or enhancing excavation and restoration workflows through the integration of assistive tools such as photogrammetry and 3D scanning [PSA22, HBD*24].

In recent years, the widespread use of virtual reality headsets has accelerated this process. As a result, conventional methodologies employed in archaeological investigation and interpretation are undergoing a significant transformation.

In this article, we present an extension of the method proposed in [DMG*25], aimed at analyzing user behavior during the visual exploration of archaeological artifacts, whether in real or virtual environments. This approach offers a structured workflow for generating consistent and comparable data across both modalities, despite the use of different devices. A key component of the methodology lies in the analysis of eye-tracking metrics, gaze patterns, and participant movements within the scene.

While the original method focused on comparing user behavior across real and virtual contexts, our work shifts the emphasis toward identifying behavioral patterns distinguishing expert and non-expert users. These differences are then interpreted in relation to the formal and contextual characteristics of three archaeological

<footnote n="1">¹Univ. Rennes, Inria, CNRS, IRISA, France</footnote>

<footnote n="2">²CNRS, Univ. Rennes, Inria, IRISA, France</footnote>

<footnote n="3">³Univ. Rennes, INSA Rennes, Inria, CNRS, IRISA, France</footnote>

<footnote n="4">⁴Inrap, UMR Trajectoires, France</footnote>
pottery artifacts. The analysis and presentation of the experimen-
tal results illustrate the potential of our behavioral data processing
approach to support and extend archaeological research.

The remainder of this paper is organized as follows. Section 2
reviews related work, focusing on the growing intersection of neu-
roscience and extended reality (XR) in the study of archaeological
perception and interaction. Section 3 details the proposed method-
ology, including participant recruitment—with particular attention
to the expert panel—, the selected artifact corpus, and the data visu-
alization approach. Section 4 presents the results of the user study,
while Section 5 discusses the main findings in light of current re-
search, outlining both contributions and limitations. Finally, Sec-
tion 6 concludes the paper and suggests avenues for future work.

## 2. Related Works

Virtual reality has become an increasingly valuable tool in archae-
ology, enabling new forms of visualization, interpretation, and en-
gagement with the past. For instance, VR has been employed for
the immersive reconstruction of archaeological sites [RA11], and
for the development of experiences incorporating 3D replicas of
artifacts to support the exploration of multiple interpretive hypothe-
ses through both virtual and physical interactions [GSN*18]. Other
applications include the representation of uncertainty in archaeo-
logical reconstructions, allowing users to visualize and reflect on
gaps or ambiguities in the data [CCGMMCTT23], and the inte-
gration of heterogeneous 3D models of various archaeological el-
ements into unified VR environments. Such approaches promote
inclusive and dynamic frameworks for digital heritage interpreta-
tion [PNG*19]. The experts can test and analyze hypotheses, us-
ing 3D modeling in VR, demonstrating that high quality models
are significant tools in this domain, and allowing some interactions
with the simulated artifact to illustrate its different usages.

Exposure to virtual representations of some elements or envi-
ronment may induce different knowledge, information and behav-
iors than direct confrontation with the real thing [MCM18, Rus22,
TIMC21, MM18]. This question is becoming particularly critical in
digital heritage as recent studies link behavioral studies of the way
people look at artifacts with archaeological interpretations of these
artifacts. For example, some articles [CBMB*23, CBMB*24] seek
to demonstrate that our visual reactions to a series of artifacts re-
flect the historic changes in the social and cultural complexity of
the society. Developing a methodology to understand the links be-
tween perceptual behavior, material culture and social complexity,
they demonstrate that different patterns of visual exploration are de-
termined by the material characteristics of each ceramic style and
provide new insights to better understand the relationship between
the mind and the material world.

Another study [BFSG*19] points out the cognitive and percep-
tual aspect through a co-evolution between body and prehistorical
tools (not only in terms of anatomical correspondence but also in
terms of cognitive integration), using electrodermal activity to re-
veal differences between tools and between subjects. In a similar
way, a study [SGIF*22] about stone tools suggests that visual ex-
ploration induced by observed objects is influenced by their affor-
dance and salience.

These studies demonstrate that eye-tracking analysis is a pow-
erful tool for archaeological research, offering valuable insights
into cognitive processes related to historical and cultural narratives.
They also highlight the importance of considering the technologi-
cal factors that can influence the interpretation of these narratives
in archaeological contexts. Most of these studies, however, rely on
high-resolution photos displayed on screens, a common approach
in eye-tracking research. While this approach is effective, many
modern applications require the use of 3D environments to better
support ecological observation and manipulation of objects, allow-
ing for more immersive and contextually accurate analyses.

Focusing more specifically on our use case, we can look at a sur-
vey which deals with analytical techniques for the archaeological
pottery field, focusing on computational applications which ben-
efit these analysis on documentation, classification and retrieval
[KHL*22]. These methods for digital analysis can help archaeol-
ogists, supporting certain tasks in this specific field of archaeology.

Another solution to assist archaeologists in accomplishing these
specific tasks is to analyze further the behavior of users in an ar-
chaeanological context, by bringing together neuroscience and ar-
chaeology. For example a series of studies in the Venice Arsenal
[CDF*23] use electroencephalography and eye-tracking to exam-
ine the perception of lions statues, both in virtual and real context.
They show differences in the perception of the artifacts, but also
an effect on learning and different levels of feedback. This study is
an example of progress in virtual archaeology with interactions and
immersion in the virtual environment.

This mixed approach was also used to verify the impact of VR
on the user’s behavior during a visual exploration of an archaeo-
logical artifact [DGM*24]. The authors compare the same task be-
tween real and virtual modalities, implementing the methodology
proposed in [DMG*25], to generate and analyze standardized eye-
tracking and behavioral data. Their results suggest that VR can be
effectively employed in an archaeological context for artifact anal-
ysis, although they did not specifically identify potential biases in
the experimental conditions used for observing both real and accu-
rately modeled virtual artifacts. Besides, the study does not specif-
ically address the expertise of the participant in archaeology, thus
missing the impact on exploratory behavior. Furthermore, the anal-
ysis of trajectory and movement in the environment could benefit
from further investigation and visualizations.

In addition, eye-tracking is also used to teach visual analysis of
artifacts [Mic24]. Based on analyzed recordings of experts’ eye
movements while looking at artifacts, they have shown that it is
a beneficial and promising tool for students, boosting the accessi-
bility to the knowledge and analytical skills for various types of
archaeological data. However, the authors focus on video represen-
tation of the eye-tracking recording in a real modality, not in an
immersive virtual environment, and therefore without considering
the motion around the object.

Another part of neuro-archaeological researches is to study how
multisensory experiences, like engaging smell, taste, touch, sight,
and occasionally sound, impact the interpretation. For example
some researchers [Lor24] investigate interactions between mind
and culture by studying how people contributed to the manufactur-
ing and construction of earthen architecture, or some others [CS25]
combine archaeological and historical evidence with sensory feedbacks to classify the sensory appeal of artifacts. They illustrate that mainly tactile perceptions are relevant to the task, evoking stronger sensory responses and confirming that hapticity is a significant part of how we, as humans, experience the built environment.

### 3. Method

The goal of our method is to analyze user behavior during the visual exploration of archaeological artifacts. To achieve this, we have developed a set of processes and visualizations designed to support the investigation of behavioral data, enabling a more detailed understanding of the relationships between user actions—such as gaze and movement—and the morphological and contextual characteristics of the observed objects.

This work builds upon the methodological framework established in [DMG*25], which is summarized in Fig. 1. That previous study introduced a workflow enabling the same archaeological observation task to be conducted across both real and virtual modalities. We reuse and adapt this framework to compare the behavioral patterns of expert and non-expert participants in relation to the properties of a corpus of archaeological ceramics. Importantly, the apparatus, data acquisition, and processing phases are identical to those detailed in [DMG*25], allowing us to focus here on the extension of the analysis toward expertise-based differentiation.

Our main contribution lies in adapting the analysis stage to highlight expertise-related behavioral differences. We do so by reaplying the standardized data processing pipeline and incorporating new visualizations and metrics specifically designed to capture divergences between expert and non-expert observation strategies.

The experimental protocol is also aligned with the one used in the previous study, with one essential modification: the inclusion of a panel of five domain experts. This extension aims to identify distinctive behavioral markers that may be linked to disciplinary knowledge or manual expertise. The expert group consists of two academic archaeologists—a ceramologist and a paleontologist—and three professional potters who collaborate regularly with archaeologists to reproduce ancient ceramics using traditional fabrication techniques. Their combined academic and hands-on perspectives provide a rich basis for evaluating how expertise influences visual and spatial engagement with archaeological material.

#### 3.1. Participant groups: experts and non-experts

To investigate the influence of expertise on visual exploration behaviors, we designed the study around two distinct participant panels: a group of domain experts and a group of non-experts.

The expert panel includes two academic archaeologists: one specialist in ceramic typologies with extensive experience in cataloging ancient pottery; and one anthropologist whose work focuses on cremation urns, providing a nuanced understanding of both form and funerary context. In addition, three professional potters were recruited. These artisans regularly collaborate with archaeologists to reconstruct facsimile ceramics using historically informed methods. Their deep familiarity with the materiality, construction techniques, and functional aspects of pottery offers a distinct, practice-based form of expertise.

This diverse composition ensures a rich and multifaceted perspective on the artifacts under observation, allowing us to investigate how different types of expertise—academic and craft-based—manifest in visual attention and exploratory behavior. The integration of such a panel not only strengthens the ecological validity of the study but also contributes to a more comprehensive understanding of expert cognition in archaeological interpretation.

In parallel, a control group of 16 non-expert participants was included in the study. They have no formal training or professional experience in archaeology, material culture, or related domains. Their selection was intended to represent a general audience likely to engage with archaeological content in educational or museological contexts. The inclusion of this group enables a comparative analysis aimed at identifying how intuitive visual strategies differ from those informed by disciplinary knowledge or technical skill.

The expert group is notably smaller than the non-expert group. Participation in this experiment requires physical presence at a designated location to use the various devices according to the different modalities. This requirement presents a significant constraint for many participants, particularly for those who are harder to recruit, such as archaeologists.

#### 3.2. Corpus

The corpus used in this study is the same as the one employed in the work presented in [DMG*25]. However, we provide here a more detailed description of the three pottery artifacts to better characterize the formal features that will guide the interpretation of observed user behaviors. This expanded presentation aims to clarify how specific object attributes—such as form, surface treatment, and decorative motifs—may influence visual exploration patterns. The corpus consists of three ceramic vessels (Fig. 2), selected to reflect a diversity of typological features, including variations in shape, size, surface finish, and ornamentation. This variability was intended to elicit a wide range of visual behaviors from participants.

* Vessel 1 is a closed shape, taller than it is wide, with a "pitcher"-like form. The profile is sinuous, with a pronounced foot. The vessel has a single handle. The walls are beige-colored, with a burnished surface. It is decorated with painted black lines forming a geometric pattern. The decorative register consists of a band of hatched triangles and panels of oblique crosshatching.

* Vessel 2 is an open, low form similar to a "bowl". Its profile is ovoid, and it features a lug on the upper part of the body. The wall color presents a color gradient from beige to black. The surface is simply smoothed and decorated with an incised frieze of vertically interlocking triangles. Notably, inside the vessel, clay-coil joints remain visible and were not erased during the smoothing process highlighting the construction technique.

* Vessel 3 is a closed form, as tall as it is wide, with a carinated biconical profile (a "pot"-like form). The walls range in color from beige to black. Both the inner and outer surfaces are simply smoothed, revealing features associated with the coil-building technique used in its construction.

To avoid any risk to the original artifacts, facsimiles were crafted by professional potters using traditional methods. These physical replicas were used in the real-condition experiments, while
<figure><img src="image_2.png" /><figcaption>**Figure 1:** Workflow for behavior analysis, from [DMG*25]</figcaption></figure>

<figure><img src="image_3.png" /><figcaption>**Figure 2:** Corpus of copies of archaeological potteries (top) and their virtual version (bottom)</figcaption></figure>

<figure><img src="image_4.png" /><figcaption>**Figure 3:** Areas of Interest (AOI) for each artifact (red: top, blue: body, yellow: foot, green: inside, pink: raised parts)</figcaption></figure>

their digital counterparts, generated via photogrammetry, were integrated into the virtual environment for cross-modal comparison.

In addition, we reuse the semantic breakdown framework introduced in [DMG*25], and presented in Fig. 3, which allows for the abstract interpretation of user attention based on the structural logic of the pottery. For each artifact in the corpus, we define distinct semantic areas, "AOIs" corresponding to commonly recognized morphological zones used by archaeologists and ceramologists. These zones are delimited using inflection points in the object's profile—an approach grounded in expert typological analysis. The

segmentation includes: (1) the upper part, "top", encompassing the lip and neck; (2) the "body", representing the central and most voluminous section; (3) the lower part, "foot" including the base or foot; (4) the interior of the vessel, "inside"; and (5) "raised parts" such as handles or lugs. This structured decomposition facilitates the comparison of gaze and movement data by associating visual attention with functionally and typologically meaningful regions, supporting a more precise interpretation of behavioral differences between experts and non-experts.

### 3.3. Analysis

To comprehensively analyze user behavior during the observation of artifacts, we examine both eye-tracking metrics and head movement dynamics. These two aspects provide valuable insights into how participants engage with the artifacts, highlighting their attentional focus as well as their physical positioning and interaction within the environment.

#### 3.3.1. Eye-tracking

In line with the original methodology presented in [DMG*25], we apply an algorithm based on the I-VDT (Velocity and Dispersion Threshold Identification) method [DKV*22] to segment and classify gaze events. This approach distinguishes fixations from saccades by analyzing eye movement velocity–lower velocities typically indicate fixations, while higher velocities are associated with saccades. Additionally, it incorporates dispersion measures, assessing the spatial proximity of gaze points within a time window, which tend to be more tightly clustered during fixations.

For the extraction of eye-tracking metrics, we focus on key indicators relevant to visual attention: the fixation duration, the duration of the first fixation—which offers insight into the initial attentional anchoring [CL20]—and fixation frequency, defined as the total number of fixations on the artifact normalized by the duration of the observation phase. These metrics collectively support a nuanced analysis of user engagement and visual exploration strategies across participant groups.

#### 3.3.2. User Activity Analysis

User behavior can also be assessed by analyzing head movements within the environment, leveraging metrics such as position, rotation, and their derivatives, including trajectory, speed, and velocity.
<figure><img src="image_5.png" /><figcaption>**Figure 4:** User interface of the prototype application for gaze heatmap processing</figcaption></figure>

<figure><img src="image_6.png" /><figcaption>**Figure 5:** User interface of the prototype application for trajectory visualization processing</figcaption></figure>

As outlined in the methodology described in [DMG*25], it is particularly insightful to examine the spatial relationship between the user and the artifact, focusing on parameters like distance and relative positioning. These metrics are straightforward to compute and are especially relevant for the observation task at hand.

In this study, we focus on two key spatial measures: the distance between the user's head and the center of the artifact, and the vertical angle, which quantifies the degree to which the user is positioned above the artifact during the observation. These measurements help us better understand how users interact with and orient themselves towards the objects they are observing.

### 3.4. Interactive Tools for Behavioral Data Analysis

As a notable contribution of this work, we implemented two interactive graphical interfaces to support the processing and visualization of behavioral data: one for gaze fixation heatmaps (Fig. 4) and one for user trajectories (Fig. 5). These tools are designed to assist researchers in navigating and interpreting large volumes of data generated during the experimental protocol. These tools will subsequently be made available so that other researchers can use them and build on them.

Both interfaces share a common design and are organized into

functional zones. In the top-right section, users can select the subset of data to be processed through four key filters: participant ID, processing mode (individual or group), observation modality (real or virtual), and the artifact under examination. These filters ensure targeted analysis and facilitate comparisons across conditions.

The bottom-right corner contains controls to launch or stop the computation, while the top-left panel displays real-time information about the current processing state, including participant identifier, frame index, and a progress indicator showing how many participants have been processed.

The lower-left area is dedicated to visualization output. For the heatmap interface, two sliders enable real-time adjustment of key rendering parameters (e.g., intensity or spread), with immediate feedback via the “Heatmap Update” button. A “Save Heatmap File” option allows users to export the current visualization, though this step is also automated during batch processing.

These tools provide researchers with a flexible and accessible means to inspect spatial and visual engagement with artifacts, supporting both quantitative analysis and qualitative interpretation. They also demonstrate the practical utility of our methodology in real-world research contexts.

### 4. Results

The 21 participants were recruited on a voluntary basis, aged between 19 and 42 years ($M = 26.22, SD = 7.1$) with 10 females and 11 males. They were divided into two groups —expert and non-expert— based on their prior knowledge and experience, as detailed in section 3.1.

Following the methodology established in [DMG*25], our analysis of user behavior is structured in two complementary parts: first, a qualitative exploration based on visualization data and second, a quantitative examination of gaze and movement metrics. Accordingly, the results are presented in two distinct subsections to reflect this dual approach.

#### 4.1. Visualization-Based Analysis of User Behavior

Fig. 6 presents representative examples of individual participant behavior visualizations. These include spatial trajectories, head-position heatmaps, and gaze fixation distributions. From this set of examples, emerging patterns and group differences between experts and non-experts can be observed and inform our interpretation.

For vessel 1, non-expert users generally adopt a linear, peripheral path around the object, which is sometimes incomplete or repeated, with occasional localized circlings around specific zones (Fig. 7). Visual attention (Fig. 8 and Fig. 9) is mainly focused on the painted decorative register, particularly on structuring elements (intersections, changes in orientation), reflecting an intuitive reading of the graphic composition. The interior of the vase, as well as morphological features (base, carination, wall inflections), is rarely explored, except for the raised handle. Experts, on the other hand, follow complex trajectories involving backtracking and positional changes. Their observation surface is broader, covering the decorative register as well as morphological components (rim, handle, base) and the vase's interior, which is systematically examined.
<table><caption>**Figure 6:** Example visualizations of participant behavior for selected individuals (identified by "ID" in the leftmost column). For each vessel, three representations are shown: the trajectory of the participant's head relative to the artifact, a heatmap of head positions in the surrounding space, and a fixation heatmap projected onto the 3D model of the artifact. These examples are both from the non-experts (with ID in the 1XX range) and the experts group (with ID in 2XX range, in the bold table).</caption><thead><tr><th>ID</th><th colspan="3">Vessel 1</th><th colspan="3">Vessel 2</th><th colspan="3">Vessel 3</th></tr></thead><tbody><tr><td>103</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>107</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>112</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>116</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>201</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>204</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr></tbody></table>

Like non-experts, they target diagnostic elements of the decoration, but in a more exhaustive manner, integrating them into a broader analysis of the object.

For vessel 2, non-experts again tend toward linear exploration, with occasional circling and shifts in position such as backtracking or approaching (Fig. 7). The object's low, open shape naturally leads to systematic observation of the interior, including the base and body. Surface irregularities—such as visible coil joints—are noticed even by non-experts, despite their lack of technical understanding. On the exterior, attention is drawn to incised decorative elements (especially beginnings and junctions of lines) (Fig. 9) and raised tenons, which function as visual attractors. Experts follow complex, dynamic trajectories similar to those seen with vessel 1, involving circlings, backtracking, and distance variations. Their focus spans both the interior and exterior. Key points of attention include morphological components (rim, base, tenons), incised decoration, and technical marks from the manufacturing process (coil joints, compression and stretching traces). These are interpreted as informative signs of fabrication techniques. Their analysis of the

decorative register is particularly fine-grained, with attention to motif junctions and structures.

For vessel 3, non-experts continue to favor linear paths, but with increased circlings and positional changes compared to the previous artifacts. The trajectory visualizations show a circular distribution of attention points, reflecting more homogeneous exploration. The biconical shape and lack of decoration shift visual attention toward salient morphological features (carinations, inflections) and the interior, where surface irregularities such as coil joints or compression marks are visually identified though not conceptually recognized (Fig. 9). In contrast to the other vases, experts simplify their paths, adopting more linear trajectories, though they consistently circle the vase multiple times. Circlings and position shifts are significantly reduced. Observation zones are evenly distributed along the path. As with non-experts, their attention focuses on structuring profile features and visible internal fabrication traces.
<figure><img src="image_7.png" /><figcaption>**Figure 7:** Heatmap of the head's position in the scene around the observed artifacts, for each vessel, with data grouped by experts (first line) and non-experts among participants (second line).</figcaption></figure>

<figure><img src="image_8.png" /><figcaption>**Figure 8:** Fixation heatmap for each vessel, with data grouped by experts (first line) and non-experts among participants (second line).</figcaption></figure>

## 4.2. Quantitative Analysis of Eye-Tracking and Movement Metrics

Regarding **eye-tracking metrics**, we focus on fixation-related measures: the duration of the first fixation, mean fixation duration, fixation frequency over the entire exploration sequence, fixation dispersion, and the distribution of fixations across the defined Areas of Interest (AOIs, see Section 3.3.1). However, none of these metrics reveal statistically significant differences across the three artifacts in our corpus, based on Friedman and Wilcoxon tests with Benjamini-Hochberg correction. For the saccade movements, we analyzed their duration and the AOIs involved at each step. As with fixation metrics, no significant differences were found according to the same statistical tests.

We also examined the sequence in which AOIs were fixated, represented in Fig. 10. This figure uses a color gradient to reflect the proportion of total fixation time spent on each AOI. A Friedman test on the ranked order of AOI observations across the three vessels revealed a statistically significant difference ($\chi^2 = 13.7, p < 0.001$).

<figure><img src="image_9.png" /><figcaption>**Figure 9:** Example of differences in the gaze fixations, represented with fixation heatmaps on the vessels. The column "Expert" and "Non-expert" contain visualization from one participant in each respective group. The last column is the original model of the object to clearly identify the interesting elements. We focus on some painted decorations for vessel 1, incised decorations for vessel 2 and fabrication traces for vessel 3</figcaption></figure>

<figure><img src="image_10.png" /><figcaption>**Figure 10:** Percentage of fixation time of the different AOI, for each vessel, considering all participant together</figcaption></figure>

A post-hoc Nemenyi test identified a significant difference specifically between Vessel 1 and Vessel 3 ($p = 0.02$).

The first AOI fixated is also of interest, as it provides insight into initial attentional focus. These occurrences are summarized in Fig. 11 via a bar plot distribution. A Friedman test reveals a significant difference ($\chi^2 = 11.74, p < 0.005$) between the artifacts. A pairwise Wilcoxon test with Benjamini-Hochberg correction indicates a significant difference between Vessel 1 and 2 ($p = 0.03$), then between Vessel 1 and 3 ($p = 0.02$), with small and medium size effect (respectively coefficient $r = 0.26$ and $r = 0.30$).

In terms of **spatial behavior**, the head-to-object distance metric (Fig. 12) shows a statistically significant difference across the
<figure><img src="image_11.png" /><figcaption>**Figure 11:** Proportion of the first looked area for each AOI, considering the three vessels, and specifying the expert and non-expert groups</figcaption></figure>

three artifacts, based on ANOVA (p = 0.04). A pairwise T-test with Benjamini-Hochberg correction indicates a significant difference between Vessel 1 and Vessel 3 (p = 0.04), then between Vessel 2 and Vessel 3 (p = 0.02), with a medium Cohen size effect (respectively d = 0.50 and d = 0.65). In contrast, the vertical viewing angle (VA) shows no significant variation across the vessels. Similar non-significant results were observed for the standard deviation of both distance and VA metrics.

Finally, no statistically significant difference was found in the total duration of the visual exploration phase across all artifacts.

When splitting participants into expert and non-expert groups, we observed significant differences in several metrics. The mean fixation dispersion, which reflects how broad or focused the visual attention is on the object, shows a statistically significant difference, based on the Mann-Whitney-Wilcoxon test, for Vessel 1 (p = 0.002) and Vessel 2 (p = 0.01), with large size effect for both (respectively r = 0.68 and r = 0.53). No significant difference was found for Vessel 3 in this regard.

For Vessel 2, we also found that the total duration of the visual exploration task (with m = 145.44, SD = 109.27 for the experts and m = 53.28, SD = 33.34 for non-experts, in seconds) differs significantly between experts and non-experts (p = 0.02), with a large size effect (coefficient r = 0.51).

Lastly, a significant difference was observed for Vessel 3 in the average distance (m = 0.49, SD = 0.08 for the expert and m = 0.63, SD = 0.11 for non-experts, in meter) between the participant's head and the artifact during the task (p = 0.02), again using the Mann-Whitney-Wilcoxon test, with a large size effect (coefficient r = 0.51).

**5. Discussion**

The study of visual trajectories, participant's positions around the observed object, and fixation points among both experts and non-experts when observing three archaeological vessels of differing shapes and decorative registers, highlights both distinct observation dynamics and notable convergences.

Some trends emerge in the results: regardless of expertise level,

<figure><img src="image_12.png" /><figcaption>**Figure 12:** Average distance between user's head and the observed object, per artifact and per participant group</figcaption></figure>

all participants systematically identify structural elements of the decoration, particularly recurrent geometric patterns, intersections, and directional breaks. This suggests a shared capacity to visually recognize diagnostic cues that help understanding the overall decorative structure, even without explicit technical knowledge. This finding extends to the identification of artifact profiles and manufacturing traces, even if non-experts cannot define these marks.

Furthermore, we also identify dynamics in the results for each artifact individually:

* Vessel 1: Non-experts follow linear, continuous paths, whereas experts actively adjust their trajectories, pausing on specific zones, especially on distinctive decorative motifs. These adjustments indicate an expert-level reading of the graphic parts and a segmentation of the object into analytically meaningful zones. Expertise is thus reflected in enhanced visual coverage, a greater number of focus points, and a combined integration of decorative, morphological, and technical dimensions. Non-experts, by contrast, concentrate on visually salient decorative elements, often without consideration of the object's overall structure.

* Vessel 2: The open shape induces observation of both the interior and exterior. Expert users focus on decorative and technical elements, particularly visible internal coil joints. Notably, these marks are also perceived by non-experts, even if their significance is not understood. Experts adopt a more integrative reading, combining technical, morphological, and decorative dimensions. Non-experts remain primarily focused on salient visual features (Fig. 8 on column 2). The fact that non-experts still perceive fabrication traces underscores the visual efficiency of certain technical markers, even without specialized knowledge. This highlights the relevance of gaze-based studies in archaeology, as they can reveal implicit perceptual mechanisms shared across expertise levels, and help better understand how visual information is processed and interpreted.

* Vessel 3: In the absence of decoration, attention shifts to the vessel's profile (especially carinations) and manufacturing traces. While non-experts intensify their exploration to compensate for the lack of salient visual elements, experts optimize their paths (Fig. 7), selectively focusing on informative aspects of the profile and interior surface (Fig. 8). This contrast reveals a differentiated adaptive strategies in visual behavior based on both expertise level and the inherent characteristics of the object.
Overall, the analysis reveals a shared ability to identify certain visual markers, while highlighting qualitative differences in the depth and intent of observation. Experts employ an analytical, targeted reading guided by technical or typological knowledge, while non-experts focus on the most visually prominent elements without necessarily completely understanding their interpretation and meaning. Visual strategies are thus modulated by both expertise level and the formal characteristics of the object.

Eye-tracking is generating growing interest in the human and social sciences, and more recently in the field of archaeology. This method, which allows for the precise recording of observers' eye movements when confronted with objects, images, or environments, provides valuable access to the cognitive processes involved in the perception, analysis, and interpretation of artifacts. This research field lies at the intersection of cognitive science and archaeology, which is illustrated by several recent studies [FWSW22,Mic24,CBMB*24,CL24] These contributions converge on several key points:

* Visual perception is never neutral; it is shaped by skills, cultural frameworks, and material affordances.

* Visual trajectories and micro eye movements reveal intentions, interpretations, and often implicit analytical strategies.

* The comparison between expert and non-expert viewers helps identify the markers of visual expertise, while also highlighting the evocative power of archaeological objects, even for those without specialized training.

Our approach aligns with this dynamic by implementing a rigorous 3D analysis protocol (trajectories, positions, points of focus, gaze plots) to examine the interactions between form, decoration, and expertise in the visual strategies deployed when observing three archaeological vases. This study extends previous work by shedding light on how the morpho-technical characteristics of objects, or their absence, differently influence visual attention depending on the level of expertise. It also confirms that certain visual markers (such as junctions, inflections, or manufacturing traces) trigger a shared recognition, illustrating the cognitive efficiency of archaeological materially.

Modes of perception directly influence the understanding of archaeological objects and contexts by guiding attention, structuring observation, and shaping interpretation. In other words, the challenge is no longer merely to describe what people look at, but to understand how and why certain elements draw their attention, depending on their cultural, formal, or cognitive significance. This raises key methodological questions: which eye-tracking setups are appropriate for which types of objects? How should visual data be interpreted? And most importantly, how can these data be meaningfully integrated with the cognitive and interpretive frameworks of researchers, users, or past societies?

Therefore in this case the analysis of visual trajectories, related to the three artifacts, fully aligns with this research approach. The very nature of the observed object (form, decoration, or visibility of manufacturing traces) directly shapes the visual strategies adopted. To this is added the factor of expertise, which modulates the precision, scope, and analytical depth of the gaze. By crossing these two variables (expertise level and formal characteristics of the object), our study is aligned with Michalik's conclusions [Mic24]: visual

behaviors are not random, but constructed, shaped by both the subject's skills and the object's affordances.

These findings invite us to consider our protocol not only as a recording method, but also as an epistemological tool, capable of revealing the underlying logic of perception, interpretation, and learning embedded in archaeological practices. While most of eye-tracking analyses are based on 2D representations, such as images on a screen or photos, our solution facilitates a more effective consideration of behavioral factors. A more comprehensive and ecological analysis is facilitated by this multifaceted approach, encompassing not only eye-tracking but also position tracking around the observed object from any angle, and a combination of the two types of data. This allows us to obtain analyses such as the heatmap of the gaze transferred to the 3D model of the object, as well as to consider more advanced representations such as the gaze plot. Notably, all of these analyses can be calculated in real time during the analysis process. Moreover, this behavioral study is possible in a real environment and also in different virtual modality, also permitting the comparison of the modalities in a homogeneous environment. Moreover, our methodology is broadly applicable to any scenario involving the visual exploration of an object. It is complemented by a comprehensive workflow that spans from protocol design and data acquisition to analysis and effective results presentation, for both real and virtual modalities.

## 6. Conclusion

This study advances the field of neuro-archaeology by offering an innovative methodology for analyzing the visual exploration of archaeological artifacts, both physical and digitally reconstructed, in immersive 3D environments. By examining the impact of object characteristics and expertise on visual behaviors, we highlight how cognitive strategies are shaped by both the material properties of the artifacts and the observer's knowledge.

The results emphasize that while all participants, regardless of expertise, recognize certain diagnostic features—such as geometric patterns, intersections, and manufacturing traces—the depth of their analysis differs significantly. Expert users exhibit a more systematic, integrative approach to visual exploration, focusing on both decorative and technical aspects of the objects, while non-experts prioritize salient visual cues. This contrast is particularly evident across the three vases, with different artifact shapes and decorations influencing the observers' gaze strategies.

In line with the growing interest in gaze-based studies within archaeology, our findings underscore the importance of visual perception as a tool for understanding how objects convey meaning to their viewers. The recognition of material affordances, such as junctions or coil joints, by both experts and non-experts, illustrates the efficiency of certain visual cues and highlights how these cues may facilitate a shared understanding of archaeological objects, even without specialized knowledge.

By integrating eye-tracking and movement data, our methodology provides a more comprehensive view of how individuals engage with artifacts. It also offers an epistemological framework for exploring not just what people look at, but why and how certain
elements capture attention, based on their formal and cognitive sig-
nificance. As a result, this study provides valuable insights into how
visual strategies evolve in response to both the inherent qualities of
objects and the expertise of their observers.

In conclusion, this research contributes to the growing intersec-
tion of cognitive science and archaeology, demonstrating the poten-
tial of immersive 3D environments and eye-tracking / head-tracking
technologies to refine our understanding of perceptual strategies
in archaeological contexts. Moving forward, this approach can be
applied to a range of archaeological use cases, providing deeper
insights into the cognitive processes behind the interpretation and
reception of material culture, whether by experts or the general
public. In particular, future research could explore how the visual
representation of missing or hypothetical parts in virtually restored
artifacts influences users' understanding, interpretation, and per-
ceived authenticity—highlighting the impact of restoration choices
on meaning-making in heritage contexts.

ACKNOWLEDGEMENTS

This work was partially funded by Equipex+ Continuum ANR-21-
ESRE-0030 and CNRS MITI AAP Digital Twin AJAX.

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