This section details the step-by-step process for adding the gaze movement into an observed c3d file, starting from the records of edf files by EyeLink (all necessary files are available for download at the OSF link).

The prerequisites are:

To facilitate this step, below we provide the procedure and the functions needed to create the stimulus videos and the ICT stimulus.

First, gather all the c3d files that will be used during the experiment into a single folder (called for example “Original C3D”) and create two other folders called for example: “Projected C3D” and “Experimental C3D and videos.”

For each one (c3d files), identify the 2D viewing angle (camera azimuth and elevation) at which you want to generate the stimuli, then use the “Project_C3D” function for each file, which will create a projected C3D in the camera view. Then place these files into the folder “Projected C3D.”

Finally, call the function “Generate_ICT_and_Stimulus_Videos_and_c3d.” You will need to provide the projected C3D folder, the backup folder for newly created files (for example, “Experimental C3D and videos”), the BTK library folder and the following information (see Figure 7 below):

Margins represent the minimum distance (in mm) between the edges of the image and the PLD dots. By adjusting the “Point’s velocity of the ICT file,” you can manage how fast (or slow) the target point moves during the inter-referential consistency test.

Optionally, you can request the creation of the ICT stimulus and experimental c3d and MPEG4 files with the two additional points added in order to verify the spatial concordance between the experimental stimuli and the ICT stimulus (same bounding box). The function outputs the bounding box limits in the Matlab command prompt and creates the video associated with each c3d file, as well as a c3d file and the ICT stimulus video, if requested (see Figure 8 below).

At this stage, you have a Pos.txt file containing the coordinates (in pixels) of the two additional point-lights of the ICT stimulus, two edf files (one for the ICT stimulus called afterwards ICT.edf and the other for the observed stimulus called Stim.edf) and two c3d files corresponding to ICT stimulus and to the observed stimulus. For the rest of this tutorial, we assume that all these files are located in the folder called File_Path afterwards.

With Matlab, prepare the Input variable (type struct) required to call the function AddEye_From_EDF.m as follow:

Once the Inputs variable is created, just call AddEye_From_EDF(Inputs) in the command Window of Matlab (see Figure 9 below).

Note that if you do not specify the Inputs variable, the function will ask for files with uigetfile dialog controls, thus just call AddEye_From_EDF() in the command window of Matlab.

The following input dialog box will be displayed and you will have to fill in all fields with your appropriate settings (here are the default settings corresponding to the dataset provided in this tutorial; see Figure 10).

After clicking on the OK button, two new c3d files will be created with the suffix “_With eye:” one for the ICT file and one for the observed PLD. A marker represented the movement of the eye has been added into these files (see Figure 11).

For the past five decades, researchers have been interested in and have deepened their knowledge of the perception of biological movement. By investigating the reason for this great sensitivity to the biological movement of human (Bidet-Ildei et al., 2014; Craighero et al., 2016) or non-human animals (Blake, 1993; Lorenzi et al., 2024; Regolin et al., 2000), the scientific community has continued to propose new questions. However, to date, the inability to locate where gaze is directed at during observation represents an obstacle for deeper understanding of biological movement perception, a problem exacerbated by the lack of tools to define areas of interest (AOI) that move. As mentioned in the introduction, the PLD is a sequence of images with multiple labelled AOIs. The second challenge is that the PLD consists of a cloud of points whose meaning emerges only once the participant interprets it based on their own perception. For example, the kinematics (the PLD) of walking in orthogonal projection (not in perspective) and in the transverse plane of the screen (i.e. walking in the direction of the observer) could just be easily perceived as walking forwards or backwards. These particularities make the selection of AOIs more problematic. Using walking kinematics as an example, walking can be interpreted as either a forward movement or a backward movement, thus constituting an ambiguous stimulus. Participants can adopt either interpretation depending on perceptual cues. When the stimuli is perceived as walking forward, attention is probably focused on the lower limbs. In contrast, when the stimuli is perceived walking backward, attention may shift toward more salient features such as trunk or head movements. This example illustrates the inherent difficulty in accurately predicting areas of visual interest (AOI) in perceptual tasks. Here, we propose an initial approach to address this challenge by accounting for the specific nature of PLD stimuli. Our method involves directly reconstructing eye kinematics — represented by a new point — within the PLD’s own kinematics using the stimulus points. It is important to note that this approach was developed based on data from an EyeLink eye tracker and a final output file of digital data. However, the process requires only a file containing the 2D [x,y] coordinates (in centimeters) of the eye on the screen. Additionally, while the present study records the kinematics of a single eye, the same method could be applied to binocular recordings by integrating the coordinate table of the second eye in the same manner. Before reconstructing eye movements within the stimulus, we propose, as a precaution, a specific ICT procedure consisting of a smooth tracking of a single dot (close to a smooth-tracking calibration task, one approach provides a more natural and efficient calibration, while maintaining good accuracy (Hassoumi et al., 2019; Li et al., 2025). The primary purpose of this ICT is to counteract potential participant-specific drift in tracking, which differs from the standard calibration using a fixation grid (Hooge et al., 2024). Indeed, calibration by eye smooth pursuit felt less intrusive and more pleasant for participants, in the sense that it allows calibration without explicit cooperation. Moreover it is functionally relevant because it is based on the correlation between eye movement and target movement (Pfeuffer et al., 2013). However, the real motivation for ICT was to ensure the correct integration of the homographic coordinate in the stimulus coordinate file itself. While optimizing the calibration is not mandatory, even a small offset can become problematic during the conversion and integration of PLD coordinates and frame rate. Therefore, an optimization step could be implemented to correct any offset, ensuring that this correction is applied to all subsequent conversions and eye movement reconstructions for the participant.

The present proposal for eye reconstruction in the PLD stimuli, like we introduce above, takes advantage of the particularity of the stimuli. Indeed, a PLD is initially a c3d file. This kind of file contains the 3D coordinates of each point in addition to their individual labels. It should be noted that each point is identifiable and locatable over time, thus each point can be considered as its own AOI. The added new points, corresponding to X and Y coordinates of gaze, can then be directly compared to the other points at each moment. Moreover, the methodology exposed in this paper which allows to insert the eye kinematic data into the c3d file, will be affiliated to a new plug-in of PLAViMoP software (Decatoire et al., 2018; PLAViMoP, 2025). Importantly, on the PLAViMoP platform (Bidet-Ildei, Francisco et al., 2022), we provide the algorithm that allows the eye motion reconstruction within the PLD stimulus. In the future, we plan to develop a more user-friendly interface enabling to characterize ocular behaviour such a fixation or saccade, and draw confidence ellipses around a given point using sliders to select thresholds. Indeed, in its current version, you need first to manually identify fixation/saccade in order to visualise it as a new point in the stimulus. In the PLAViMoP tool, it would then be sufficient to colour the two points to visualise the eye, for example blue for fixation and red for saccade, as a single point changing colour. In addition to the visualisation aspect, it would also be very easy to measure the distance of the “fixation” point and the “saccade” point from the other markers.

Finally, our approach enables further investigation into what captures the eye’s attention during action observation, depending on the nature of the task. This issue is important to subsequently explore additional aspects such as the salience features of BM and moments of recognition. One promising approach is to compile a dataset of eye movements across observers in order to identify distinguishing features of gaze in biological motion, in a manner analogous to approaches in face perception. For instance, Caldara et al. (2010) showed that people from different cultural backgrounds may have different fixation strategies (e.g., looking at the eyes or the nose) when faced with limited visual conditions. This suggests that eye movement patterns can reflect different perceptual strategies when processing ambiguous stimuli. Moreover, in the context of biological movement, the question of action observation in motor learning (Rizzolatti et al., 2021; Vogt & Thomaschke, 2007), particularly in sports, arises (D’Innocenzo et al., 2016; Horn et al., 2002) and understanding where the gaze is directed during observation could provide new and interesting insights. Notably in a recent study (Francisco et al., 2022), we demonstrated that action observation combined with physical training could guide the observer’s attentional focus, suggesting that certain regions or points of the PLD might be more informative than others. However, a key missing element is the ability to determine what actually captured the observer’s gaze to support this conclusion. It is possible that the perceived importance of certain parameters is influenced by different kinematic cues, making gaze localization a valuable area of investigation. In the same way, identifying the observer’s gaze location could help optimize action observation training for the rehabilitation of central and peripheral motor disorders (Ryan et al., 2021; Sarasso et al., 2015).

In summary, the proposed gaze localization methodology allows for the study of gaze behavior during perception, offering insights into the mechanisms involved in processing biological motion.

To conclude, although the analysis of eye movements is believed to enhance the relevance of PLDs for both theoretical and applied research, there is, to date, a lack of methods that align eye movements with PLD. The technique we propose here, which combines 2D eye-tracking data with the PLD’s c3d file within the same reference frame, provides a solution to this gap. This approach opens new avenues for understanding biological movement perception and offers potential applications in fields such as health, sports, and occupational studies.