
==== Front
Soc Cogn Affect Neurosci
Soc Cogn Affect Neurosci
scan
Social Cognitive and Affective Neuroscience
1749-5016
1749-5024
Oxford University Press UK

39167473
10.1093/scan/nsae055
nsae055
Original Research – Neuroscience
AcademicSubjects/SCI01880
Pupil dilation reflects the social and motion content of faces
Ricou Camille Université de Tours, INSERM, Imaging Brain & Neuropsychiatry iBraiN U1253, Tours 37032, France

Rabadan Vivien Université de Tours, INSERM, Imaging Brain & Neuropsychiatry iBraiN U1253, Tours 37032, France

Mofid Yassine Université de Tours, INSERM, Imaging Brain & Neuropsychiatry iBraiN U1253, Tours 37032, France

Aguillon-Hernandez Nadia Université de Tours, INSERM, Imaging Brain & Neuropsychiatry iBraiN U1253, Tours 37032, France

https://orcid.org/0000-0001-8847-1859
Wardak Claire Université de Tours, INSERM, Imaging Brain & Neuropsychiatry iBraiN U1253, Tours 37032, France

*Corresponding author. Inserm U1253 iBraiN, 1er étage Bâtiment B1A, CHRU Bretonneau, 2 boulevard Tonnellé, TOURS CEDEX 09 37044, France. E-mail: claire.wardak@univ-tours.fr
2024
21 8 2024
21 8 2024
19 1 nsae05507 2 2024
15 7 2024
19 8 2024
06 8 2024
16 9 2024
© The Author(s) 2024. Published by Oxford University Press.
2024
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Abstract

Human facial features (eyes, nose, and mouth) allow us to communicate with others. Observing faces triggers physiological responses, including pupil dilation. Still, the relative influence of social and motion content of a visual stimulus on pupillary reactivity has never been elucidated. A total of 30 adults aged 18–33 years old were recorded with an eye tracker. We analysed the event-related pupil dilation in response to stimuli distributed along a gradient of social salience (non-social to social, going from objects to avatars to real faces) and dynamism (static to micro- to macro-motion). Pupil dilation was larger in response to social (faces and avatars) compared to non-social stimuli (objects), with surprisingly a larger response for avatars. Pupil dilation was also larger in response to macro-motion compared to static. After quantifying each stimulus’ real quantity of motion, we found that the higher the quantity of motion, the larger the pupil dilated. However, the slope of this relationship was not higher for social stimuli. Overall, pupil dilation was more sensitive to the real quantity of motion than to the social component of motion, highlighting the relevance of ecological stimulations. Physiological response to faces results from specific contributions of both motion and social processing.

eye-tracking
pupillometry
human
avatar
object
biological motion
Agence Nationale de la Recherche 10.13039/501100001665 ANR-21-CE17-0045 Agence Nationale de la Recherche 10.13039/501100001665 ANR-21-CE17-0045
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pmcIntroduction

From birth, human beings are particularly attracted to biologically relevant stimuli, such as faces, which capture and mobilize our attention (Pascalis et al. 2011, Annaz et al. 2012). These observations have led to the formulation of the social motivation hypothesis (Chevallier et al. 2012), which describes a powerful attentional priority for human faces. Faces contain highly relevant information pertaining to identity, emotional state, and intention of others. As human beings, we find intrinsically beneficial or motivational value in social interactions so that salient information from a face is prioritized (Chevallier et al. 2012). This information is conveyed in particular by the relative positions and motion of the eyes, nose, and mouth, especially during emotional expressions. Faces in movement embed critical information allowing us to communicate, understand each other, and react appropriately. Face movement is part of the repertoire of biological motion, and it has been shown that human beings are particularly attracted by biological movement (New et al. 2007).

The pupil, the orifice delimited by the iris muscles, is currently the topic of considerable research. It has long been known that pupil size varies according to the amount of light, but also reflects a wide range of sensory and cognitive processes including attention and emotion processing (Hess and Polt 1964, Beatty and Lucero-Wagoner 2000, Bradley et al. 2008). Pupil diameter is controlled by the Autonomic Nervous System (ANS), and its dilation is considered a peripheral index of the activity of the locus coeruleus (LC) and the superior colliculus (SC) (Joshi et al. 2016, Strauch et al. 2022). Any salient stimulus would induce a physiological response orchestrated by a complex interaction between the ANS, the SC, and the LC, preparing the body to respond to the stimulus, increasing arousal and orienting attention, triggering changes in pupil size (Bouret and Sara 2004, Aston-Jones and Cohen 2005, Sara and Bouret 2012, Murphy et al. 2014, Wang and Munoz 2015, Joshi et al. 2016, Bast et al. 2018, Joshi and Gold 2020, Strauch et al. 2022 for a complete review; Zhang et al. 2023).

Previous studies have already explored pupil reactivity in response to faces. They have shown a sensitivity to the human and/or biological aspect of the stimulus, with a larger pupil dilation in response to social stimuli (faces) compared to non-social stimuli (objects) (Martineau et al. 2011, Sepeta et al. 2012, Aguillon-Hernandez et al. 2020). To explore the realism and social content of human faces, two studies have investigated the pupil reactivity to avatars (Martineau et al. 2011, Aguillon-Hernandez et al. 2020). Avatars can simulate several features of the human faces, allowing to study some aspects of the social information processing. In both studies, authors used virtual faces and showed in neurotypical children a smaller pupil dilation in response to avatars than real faces, following an initial pupil constriction due to stimulus onset. Closeness between avatars and real faces is of utmost importance to trigger the pupil response, as in another interesting study by Reuten et al. (2018), the pupil dilated less for the emotional facial expressions of robots judged uncanny compared to more human-like robots. In their study, the authors used images of static robots ranging from cartoonish to human-like appearance, sharing many features with human faces as avatars in the studies cited above. Overall, these studies show that, although the stimuli have characteristics similar to human faces, their perception differs from that of human faces. Even if the stimuli are not the same (virtual faces versus cartoon), these previous results are in line with the study by Rosset et al. (2010), which showed that the perceptual treatment of real faces is different from cartoon faces in typically developing children. In their study, Rosset et al. (2010) showed that typically developing children discriminated real facial features displayed in upright faces more accurately than in inverted faces, while no such effect of inversion was present for cartoons. This suggests that avatars and cartoons do not recruit the holistic face processing network in the same fashion as real faces (see also imaging studies, e.g. James et al. 2015, Kegel et al. 2020). Considering all this literature, disrupting ecological validity of faces or natural scenes (like in the inversion effect, Conway et al. 2008, Falck-Ytter 2008, Van Helden and Naber 2023) could affect the interpretation of pupil dilation response. Furthermore, the study from Aguillon-Hernandez et al. (2020) pointed out the pupil sensitivity to human dynamism. In their study, the authors used photographs as well as videos, either of neutral faces or of faces expressing an emotion (happiness and sadness). They showed that pupil dilation in neurotypical children was larger for dynamic stimuli, whether expressing emotion or not, than for static stimuli. These results highlight a sensitivity to facial motion induced by postural adjustments, as well as emotional expression. In their results, it seems that the more ecological the stimulus, the more the pupil dilates, consistent with the animate monitoring hypothesis (New et al. 2007), which states that biological dynamism captures human attention. However, New et al. (2007) used static images in a change-detection paradigm and did not control for real dynamism, raising the question of the relative influence of human realism and its social content, or of the amount of movement in the stimulus, on attentional capture.

We inhabit a dynamic world, constantly perceiving the movements around us. These movements, whether stemming from the environment’s natural dynamics or the dynamics of social interactions (e.g. facial expressions, body language), are crucial for our understanding of the world, our adaptation, and our survival. Motion perception involves several interconnected brain regions, each providing specialized functions for detecting (primary visual cortex—V1) and analyzing (medial temporal area—MT/V5 and medial superior temporal area—MST) information relating to motion. Social motion also recruits specific regions (body: extrastriate body area—EBA; face: posterior-superior temporal sulcus—STSp, occipital and fusiform face areas—OFA and FFA; amygdala for emotional aspects) (Oram and Perrett 1994, Grossman et al. 2000, 2005, Haxby et al. 2000, Downing et al. 2001, 2006, Vaina et al. 2001, Grossman and Blake 2002, Giese and Poggio 2003, Bernstein and Yovel 2015, Bernstein et al. 2018). Some studies have explored the link between pupil reactivity and biological motion sensitivity (Williams et al. 2019, Castellotti et al. 2021, Cheng et al. 2021, 2024). Using human-like and machine-like motion embodied in human bodies or mechanical bodies, both without any facial information, Williams et al. (2019) showed that human-like motion induced a higher physiological response than mechanical motion, whether the agent was human or not. Also, the study by Castellotti et al. (2021) showed that real motion (videos of moving objects, animals, and people) induced greater pupillary dilation than photos of dynamic subjects (implicit motion) and photos of static scenes. Moreover, studies by Cheng et al. (2021, 2024) observed larger pupil dilation for biological motion compared to non-biological motion. The pupil appears to be sensitive to the nature of the motion, particularly for human but also real and dynamic movements. However, reactivity to facial and object motion, by controlling the influence of social, natural, and the real quantity aspects has never been described.

The relative influence of human faces’ realism and social content on pupillary reactivity, as well as the effect motion (related to social and the real quantity aspect), has never been elucidated. In the present work, we aim to characterize all these aspects using ecological and naturalistic stimulus set combining a gradient of human aspect (going from objects, to avatars and real faces) and a gradient of dynamism (going from static to dynamic stimuli, with micro- and macro-motion). This set of natural stimuli will enable us to confirm previous results (Aguillon-Hernandez et al. 2020) but also to answer our questions about dynamism by distinguishing motion dynamics relating to the social aspect and motion dynamics relating to the real quantity of motion. Human faces were already used in a previous study (Aguillon-Hernandez et al. 2020), and the avatars were created from this human faces in order to respect the norms of human faces as far as possible. The avatars were designed to avoid entering the uncanny valley, the theory of which states that humanoid robots that are very realistic but have visible imperfections can arouse feelings of strangeness, anxiety, or unease because they call human identity into question (Mori et al. 2012). For the objects, we created kites from scrambled images of the human faces also used in the same previous study (Aguillon-Hernandez et al. 2020). The advantage of kites is the symmetry of the object, which can be compared to faces, but also the fact that kites exhibit a natural motion without social or biological content, i.e. independently of any human influence. Indeed, studies of object motion have often used static or dynamic object stimuli, such as a ball or a tool, which require the action of a human (Beauchamp et al. 2002, 2003, Castellotti et al. 2021).

We hypothesized that physiological mobilization will depend on the stimulus gradient: the highest degree of social salience and dynamism will attract more attention and therefore elicit a larger physiological response than non-social static stimuli. To dissociate the relative weight of dynamism and social components, we quantified the amount of the real quantity of motion in each of our stimuli to test its influence on pupil reactivity. In the context of the social motivation theory (Chevallier et al. 2012) and the animate monitoring hypothesis (New et al. 2007), we hypothesized that, while the real quantity of motion in itself would increase the amount of pupil dilation, social motion would be more effective than non-social motion. With respect to previous studies using objects and avatars (Martineau et al. 2011, Aguillon-Hernandez et al. 2020), we expected a lower pupil dilation for objects than for avatars and real faces, and a lower pupil dilation for avatars compared to real faces, whether static or dynamic.

Method

Participants

The study group consisted of 30 participants (13 females) aged 19–28 (mean 23.86 ± 2.14 years). Non-inclusion criteria were abnormal or uncorrected vision and a history of psychiatric, neurological, or neurodevelopmental disorders. Written informed consent was obtained from each adult before the experiment. The study was approved by an Ethics Committee (CPP, protocol PROSCEA 2017-A00756-47) and conformed with the Declaration of Helsinki Ethical Principles for Medical Research Involving Human Subjects.

Materials

All participants were comfortably seated in a chair inside a sensory tent (HOPTOYS HT2731, 180 × 120 × 120 cm), which provides isolation from outside lights and a quiet space for the subjects. To control and harmonize the overall brightness inside the tent, an additional light bar adjustable with a remote control (XIAOMI Mi computer Monitor Light Bar) was placed behind the presentation screen (Samsung Professional monitor, 27ʹ, Full HD resolution of 1920px*1080px). The overall brightness was monitored and kept constant (20 lux) using a lux meter. Eye tracking data were recorded by a Tobii® Pro Fusion portable eye tracker at 250 Hz (Tobii, Stockholm, Sweden).

Stimuli

Videos of faces used in a previous study (Aguillon-Hernandez et al. 2020) were presented. Based on these faces, new social and non-social stimuli were created to obtain a social and motion gradient with three social saliency levels stimuli (objects, avatars, and faces) each separated in three motion levels (static, micro, and macro-motion). The videos of faces featured four actors, from which written informed consent was obtained for experimental purposes and dissemination in scientific productions. The avatars stimuli were created from the faces footage of the four actors using the LoomAi Avatar Creator® application. To ensure that the avatars did not fall into the uncanny valley and that their motion was close to the real faces motion, we assessed the subjective evaluation of at least 10 members of our team. The unanimous feedback was that our avatars resemble real faces without looking strange or eerie. For the object stimuli, we created kites that we designed from the scrambled images of the four faces. The static motion condition consisted of stationary videos of the objects or photographs expressing a neutral emotion for the avatars and faces. The micro-motion condition consisted of videos with small amplitude movements of the objects, or videos expressing a neutral emotion (containing small postural adjustments) for the avatars and faces. The macro-motion consisted of videos with large amplitude movements for the objects and facial expressions expressing happiness or sadness for the avatars and faces (Fig. 1a).

Figure 1. Stimuli gradient and protocol. (a) Stimuli gradient: stimuli combining a social gradient (going from objects to avatars and real faces) and a motion gradient (going from static to dynamic stimuli, with micro- and macro-motion). (b) Protocol: each stimulus lasted 4 s with a 2- to 3-s interstimulus which was a uniform image with a black cross. The order of presentation was randomized.

Each motion level included one photograph or video per actor for each social saliency level, except the macro-motion condition. Indeed, in this condition, the avatars and faces expressed either happiness or sadness, and each actor was presented twice (two emotions per actor). In total, our eye-tracking protocol included 44 stimuli: 4 actors*3 social saliency levels (objects, avatars, faces) *3 motion levels (static, micro-motion, macro-motion*2 for avatars and faces). All stimuli lasted 4 s, except for the face videos which had different durations (2 s, 3 s, 4 s, and 6 s). As the previous study (Aguillon-Hernandez et al. 2020) already evaluated the response to the same videos of faces lasting 4s, this duration manipulation allowed us to check the effect of presentation time on the response to faces. Each stimulus was separated by an inter-stimulus interval of 2 s to 3 s with a central cross. Two consecutive sequences of the 44 stimuli and inter-stimuli were presented randomly. Stimuli were matched in terms of luminosity (10 lux), colour (Red Green Blue values = 230, 231, 208), position, and size (1920*1080) (Fig. 1b).

Procedure

Once the subject was comfortable in the sensory tent, at ∼66 cm from the screen (distance calculated by the eye tracker: 59.1–73.1 cm), a five-point calibration procedure was performed. The total duration of the eye-tracking recording was 10 min maximum per subject. No instructions were given to the participants, except to pay attention to the screen and to remain silent.

Eye-tracking parameters and data preprocessing

Oculometric parameters

The proportion of fixation time spent on the screen, objects, avatars, and faces was extracted using AOIs (Areas of Interest) previously created in the Tobii Pro Lab interface. The first initial AOI (Set 1) covered the entire screen (1920*1080px) to check that all videos were watched equally. We then created another set of AOIs (Set 2) using rectangular shapes (920*1021px) surrounding all categories of stimuli (object, avatar, and face) (See Supplementary Fig. S1).

All AOIs within each set (the whole screen and the second set) had the same surface for all categories of stimuli. We analysed the proportion of fixation time spent within those AOIs, relative to the total fixation time spent on the screen, to control for potential confounding effects. The mean proportion of fixation time spent within the AOIs varied between 0.995 and 0.998 along the social gradient (See Supplementary S1).

Local pixel value

To ensure that the local luminance value in HSV (Hue Saturation Value) colour-space did not affect our results (Derksen et al. 2018), an average of the V dimension pixel value around fixation for each stimulus and each participant was calculated. For each sampled time, we identified a 3-degree region around the fixation point and averaged the value of the pixels within this region. The average pixel value was calculated by converting each image/frame into HSV and averaging the pixel value (between 0 and 1: 0 corresponding to the darkest and 1 to the brightest). The pixel value was then averaged over all fixations for each stimulus. We included this value as a covariate in our analyses to control for potential confounding effects. The difference in the average pixel value as a function of the social and the motion gradients is presented in Supplementary Information (See Supplementary S2 and Supplementary Fig. S2).

Pupil diameter

For the analysis of pupil diameter variations, we first extracted the raw data via the Tobii Pro Lab interface. The signal was then processed using in-house MATLAB scripts (MATLAB® R2018a). We first identified and removed artefacts from the signal (dropout, blink) by excluding samples that exceeded a velocity threshold. Lost samples were replaced by interpolated values if the signal loss was less than the duration of a blink (200–300 ms). When the signal loss was longer than this duration, trials were removed. For blinks, the pupil diameter values were replaced by the median values of a 30-ms interval before and after the blink. After this identification and removal of signal artefacts, a median filter was applied to smooth the signal. We then calculated the variation of the pupil diameter. For each participant and each trial (stimulus), we defined a baseline pupil value by calculating the median pupil diameter during the last 200 ms before the start of the trial. This baseline value was then subtracted from the pupil diameter recorded for the entire stimulus presentation. For each participant, an average time course was calculated for each of the nine categories (3 social saliency levels × 3 motion levels). We then extracted the median value of the pupil diameter variation plateau between 1.8 and 4 s (mm) (Fig. 2). For the real face videos, the median values were extracted in the same way, but considering each video’s respective duration. Pupil response amplitude in response to real faces videos was not impacted by the duration of the videos.

Figure 2. Descriptive time course of pupillary variation for each stimulus categories. Each line represents the temporal evolution of the pupil in adults as a function of social gradient represented by colours (blue for objects, green for avatars, and red for faces) and motion gradient defined by different types of lines (dotted line for static, dashed line for micro-movement and solid line for macro-movement). The grey line represents the pupil variation plateau (1.8–4 s). For faces with macro- and micro-motion, as different video lengths were tested, the global pupil time course corresponds to the mean of all videos aligned on the onset (before //) and on the offset (after //) of the videos.

Motion coefficient

On top of the motion gradient defined by the design of the stimuli, we also assessed the real quantity of motion present in each stimulus, to test its influence on the variation in pupil diameter. We characterized and quantified the amount of motion in each of our videos using the Farneback algorithm method (MATLAB® R2018a, computer vision Toolbox). This algorithm decomposes each frame of one video into a vector of movements, also known as optical flow. For each video, we calculated an average quantity of motion (in arbitrary units), corresponding to a mean motion coefficient, from the coordinates of the optical flow vectors over all the video frames. As our protocol included 44 stimuli, we therefore obtained 44 motion coefficients (See Supplementary Fig. S3 for more details).

Statistical analysis

Statistical analyses were performed with R Statistical Software (v4.1.1; R Core Team 2021). The data were analysed using linear mixed models (LMMs). We included the social gradient (categorical factor with three levels: objects, avatars, and faces), the motion gradient (categorical factor with three levels: static, micro-motion, and macro-motion), and the motion coefficient (continuous factor) as fixed effects. Interactions between our fixed effects were allowed in our LMMs if they were relevant to our hypotheses. We applied random intercept for participants to control for interindividual differences. We also controlled for potential confounding factors by adding fixed-effect covariates: the proportion of fixation and the local average pixel value. To ensure that our models were the most parsimonious and best fitted our data, we performed backward elimination of non-significant effects of LMMs using the step function (STAT package—Bolar 2019).

To explore whether the social gradient and the motion gradient influence pupil dilation in our stimuli, we designed an initial model: M1_all = pupil dilation ∼ social gradient*motion gradient + proportion of fixation + local average pixel value + (1 | participant). Backward elimination of non-significant effects determined the optimal model as follows: M1_optimal = pupil dilation ∼ social gradient + motion gradient + local average pixel value + (1 | participant).

To explore whether the real quantity of motion, as well as the social gradient, influence pupil dilation in our stimuli, we designed a second model: M2 = pupil dilation ∼ motion coefficient*social gradient + proportion of fixation + local average pixel value + (1 | participant). Backward elimination of non-significant effects determined the optimal model as follows: M2_optimal = pupil dilation ∼ motion coefficient*social gradient + local average pixel value + (1 | participant).

To go further in the understanding of the relative influence of social and the real quantity of motion components on pupil dilation, we designed two supplementary models:

To explore the social/non-social contribution within the motion quantity, we tested both the motion gradient and motion coefficient in the same model. This approach aims to account for the real quantity of motion within each category of motion: natural motion for the objects and social motion for the avatars and faces. M3_all = pupil dilation ∼ social gradient * motion gradient + motion coefficient + proportion of fixation + local average pixel value + (1 | participant). Backward elimination of non-significant effects determined the optimal model as follows: M3_optimal = pupil dilation ∼ social gradient + motion coefficient + local average pixel value + (1 | participant).

To explore the emotional content of social motion, we reproduced M3 but without the object category and adding the emotional valence (neutral for the static and micro-motion, as well as happiness and sadness for macro-motion) for avatars and faces. This model includes social gradient (categorical variable with two levels: avatars and faces), motion gradient (categorical variable with four levels: static, micro-motion, happiness macro-motion, sadness macro-motion), and motion coefficient as fixed effects. M4_all = pupil dilation ∼ social gradient * motion gradient + motion coefficient + proportion of fixation + local average pixel value + (1 | participant). Backward elimination of non-significant effects determined the optimal model as follows: M4_optimal = pupil dilation ∼ social gradient + motion gradient + local average pixel value + (1 | participant).

Planned pairwise comparisons (Tukey method) with adjusted P-values were performed to specify the main effects (three comparisons for the social and motion gradients for M1, M2 and M3; two comparisons for the social gradient and four comparisons for the motion gradient for M4). Normality of distribution and homogeneity of variance were also verified. An a posteriori sensitivity analysis was performed using the SIMR package (Green et al. 2016) for model M1_optimal and estimated a statistical power above 0.8 (exact number; alpha = 0.05) for a minimum of 16 participants, well below our sample size.

The following packages were used for the analysis and graphics: lmerTest (Kuznetsova et al. 2017), lme4 (Bates et al. 2015), car (Fox et al. 2023), emmeans (Lenth et al. 2023), ggplot2 (Wickham 2009), ggpubr (Kassambara 2023), STAT (Bolar 2019).

The figures were created with R and R Studio (v4.1.1; R Core Team 2021) and Inkscape (v.0.92.3).

Results

Social gradient and motion gradient influence separately pupil dilation

To explore the influence of the social gradient and the motion gradient of our stimuli on pupil dilation, we analysed whether the pupil dilation varied according to the social gradient (objects, avatars, and faces) and the motion gradient (static, micro, and macro). The selected model [M1_optimal = pupil dilation ∼ social gradient + motion gradient + local average pixel value + (1 | participant)] revealed significant main effects of social gradient [F(21, 301) = 47.67, P < .001] and motion gradient [F(21, 290) = 8.39, P < .001], indicating that both components influence independently pupil dilation. The selected model also revealed a significant main effect of local average pixel value [F(11, 128) = 8.90, P < .01] (See the Supplementary Information for more details).

For the social gradient, adults were more sensitive to social stimuli than to non-social stimuli, with a larger pupil dilation for faces and avatars compared to objects (P < .001 for both comparisons) (Fig. 3). Surprisingly, they also revealed a larger pupil dilation for avatars than for faces (P = .0018). As avatars are uncommon stimuli that are close to faces, we checked whether this difference was due to a potential surprise effect between the first and second trials using a paired t-test. No significant difference was found between the two trials [t(29)= −0.095, P = .924] (See Supplementary Fig. S4). Concerning the motion gradient, adults were more sensitive to dynamic stimuli compared to static stimuli, with a larger pupil dilation for macro-motion compared to static stimuli (P < .001) (Fig. 4).

Figure 3. Boxplot illustrating pupil dilation according to the social gradient of stimuli (objects, avatars, and faces). Each point represents the median value of the plateau of variation in the diameter of a participant’s pupil (mm) defined for each stimulus [4 actors*3 social saliency levels (objects, avatars, faces)] for the two trials (***P < .001; ** P < .01).

Figure 4. Boxplot representing pupil dilation according to the motion gradient of stimuli (static, micro, and macro-motion). Each point represents the median value of the plateau of variation in the diameter of a participant’s pupil (mm) defined for each stimulus [4 actors*3 levels of motion (static, micro, and macro-motion)] for the two trials (***P < .001).

Given that the large-amplitude facial expressions (i.e. macro-motion) for both avatars and faces included the emotions of happiness and sadness, we controlled whether the observed pupil sensitivity to macro-motion was not specific to a particular emotion. Adults showed no significant differences in pupillary dilation between happiness and sadness (P = .795), meaning that adults were sensitive to macro-motion independently of the emotional content (See Supplementary S3).

Social gradient and the real quantity of motion both influence pupil dilation

In addition to the motion gradient, we also explored whether pupil dilation as a function of the real quantity of motion varied according to the social gradient. The selected model [M2_optimal = pupil dilation ∼ motion coefficient*social gradient + local average pixel value + (1 | participant)] revealed significant main effects of the motion coefficient [F(11, 285) = 4.70, P = .030], and the social gradient [F(21, 96) = 19.01, P < .001] (Fig. 5b). The selected model also revealed a significant main effect of local average pixel value [F(11, 134) = 6.50, P < .05] (See the Supplementary Information for more details).

Figure 5. Point graphs illustrating pupil dilation according to (a) the different motion coefficients of each stimulus and (b) the motion coefficient and the social gradient of each stimulus. Each black or coloured dot corresponds to a participant’s pupillary dilation (in mm) for a given motion coefficient.

For the motion coefficient, the result indicates that pupil dilation varies with motion: the more motion there is, the larger the dilation (Fig. 5a). Concerning the social gradient, we found the same results as previously, with a sensitivity to social compared to non-social stimuli. Moreover, we did find a significant interaction between the two factors [F(21, 285) = 4.15, P < .05], meaning that the relationship between the motion coefficient and pupil dilation varies according to the social gradient (Fig. 5b). To test this significant interaction, we compared the slopes of the different categories of the social gradient. We found a significant difference between faces and avatars (P < .011), with a smaller slope for faces. Note that one specific motion coefficient, corresponding to one macromotion real face video, was particularly high compared to the other videos from the same category (See Supplementary Fig. S5). In a complementary analysis omitting this specific video (See Supplementary S4), we found no significant interaction between motion coefficient and social gradient, with similar slopes for all categories.

The real quantity of motion primes over the social nature of motion on pupil dilation

To explore the social/non-social contribution within the motion quantity, we analysed whether the pupil dilation varied according both to the motion gradient and the motion coefficient. The selected model [M3_optimal = pupil dilation ∼ social gradient + motion coefficient + local average pixel value + (1 | participant)] revealed significant main effects of the social gradient [F(21, 302) = 36.56, P < .001] and motion coefficient [F(1, 1291) = 16.06, P < .001], indicating that both components influence independently pupil dilation. The selected model also revealed a significant main effect of local average pixel value [F(11, 136) = 7.91, P < .01] (See the Supplementary Information for more details).

This result indicates that the pupil continuously varies according to the social gradient, as observed with previous models, with a more significant pupil dilation for social stimuli than for non-social stimuli. In addition, the pupil still varies as a function of motion coefficient, this time independently of the social category, and without taking the motion gradient into account: the real quantity of motion seems to be more important than the motion gradient (relative to naturalness for objects, and social for avatars and faces).

Emotional content influences pupil dilation

As the emotional valence of macromotion videos had not yet been taken into account in the analyses, we explored the influence of the emotional content of the social movement on pupil dilation only in avatars and faces. The selected model [M4_optimal = pupil dilation ∼ social gradient + motion gradient + local average pixel value + (1 | participant)] revealed significant main effects of the social gradient [F(1, 929) = 12.14, P < .001] and motion gradient [F(3, 932) = 5.89, P < .001], indicating that both components influence independently pupil dilation. The selected model also revealed a significant main effect of local average pixel value [F(1, 618) = 10.02, P < .01] (See the Supplementary Information for more details).

For the social gradient, we found the same results as previously with a larger pupil dilation for avatars compared to faces (P < .001). Concerning the motion gradient, participants were more sensitive to the motion dynamic relevant to emotional content compared to static stimuli. They exhibited a larger pupil dilation for the sadness macromotion compared to the neutral static stimuli (P < .001), as well as for the happiness macromotion compared to the neutral static stimuli (P < .05). However, adults showed no significant differences in pupillary dilation between happiness and sadness (P = .795) (Fig. 6).

Figure 6. Boxplot representing pupil dilation according to the motion gradient (static neutral, micro neutral, happiness macro-motion, and sadness macro-motion). Each point represents the median value of the plateau of variation in the diameter of a participant’s pupil (mm) defined for each stimulus [4 actors*4 levels of motion (static, micro, happiness macro-motion and sadness macro-motion)] for the two trials (***P < .001, **P < .01, *P < .05).

Discussion

In this study, we describe the relative influence of social content of human faces, as well as the real quantity of motion on pupil reactivity using ecological stimuli combining a gradient of human aspect (going from objects, to avatars and real faces) and a gradient of dynamism (going from static to dynamic stimuli, with micro- and macro-motion).

Consistent with previous studies (Martineau et al. 2011, Aguillon-Hernandez et al. 2020), our results demonstrate that physiological mobilization is larger for social (faces and avatars) than for non-social stimuli (objects). This finding illustrates the attractivity for human components, reflecting engagement for social content (Chevallier et al. 2012). However, we surprisingly find a larger pupil dilation for avatars compared to faces, contrary to previous studies in neurotypical children (Martineau et al. 2011, Aguillon-Hernandez et al. 2020). Based on these past studies, and on the social gradient we constructed, we expected the avatars to be less prominent than faces, resulting in lower physiological mobilization. This result could be explained by the fact that our avatars have a more apparent amount of sclera than our real faces. Indeed, the study by Whalen et al. (2004) reported that faces exhibiting eyes with a larger amount of white induced a significantly larger response in the amygdala. Given the relationship between LC-Norepinephrine (LC-NE) and amygdala (Chen and Sara 2007, Schwarz and Luo 2015), the amount of sclera contained in avatars may have influenced the pupillary response. In addition, the avatars we used are virtual representations of real faces (of the two actresses and two actors). In this way, our study features avatars that are accurate in terms of luminosity and colorimetry but also designed to mirror real faces’ aesthetic and biological properties. Some studies have explored the aesthetic effects of stimuli and have shown that the exaggeration of certain facial features can enhance the attractiveness of the face (Cunningham et al. 2002, Costa and Corazza 2006). Indeed, these stimuli are considered supernormal because they correspond to exaggerated or artificial versions of stimuli that go beyond what is generally found in natural stimuli. We can, therefore, hypothesise that our avatars are a form of supernormal stimuli which exaggerate the features of real faces and thus reinforce the physiological response. Our avatars would facilitate the identification of social characteristics typical of human interactions, mobilizing our attention and arousal, and thus eliciting pupil dilation. This finding underscores the sensitivity of the pupil to social content, mainly when it resembles human beings. It aligns with the media equation hypothesis, which posits that humans naturally and socially respond to computers, new media (Reeves and Nass 1996, Nass and Moon 2000) and virtual agents (Krämer 2008, Hoffmann et al. 2009, Von der Pütten et al. 2010). Our finding holds significant benefits for human–computer interaction and virtual reality. Using pupil dilation as feedback can reshape how we design and interact with avatars in virtual reality environments and help enhance user engagement and optimize the user experience. On the contrary, the larger pupil dilation for avatars compared to faces could also be explained by the potential eeriness of the avatars. Although the unanimous feedback from the internal subjective evaluations was that they looked like real faces without being strange or eerie, we cannot be sure that the adult participants felt the same way. To disentangle the relative effect of attractiveness and eeriness, future studies should include a questionnaire to assess participants’ perception of avatars. Another factor that could have impacted pupil response to avatars is the novelty or surprise effect of these stimuli, possibly provoking more excitation and engagement, and thus evoking a supplementary phasic activation of the LC-NE system. However, the avatars’ novelty could not explain the whole pupil response, otherwise we would have found the same result for the objects. Indeed, the object stimuli we created were kites designed from scrambled images of the faces of the different actors and that did not look like classical kites. Within the object itself, the design based on scrambled images could have created a feeling of novelty and surprise, eliciting a more significant pupillary response. To our knowledge, one study (Reuten et al. 2018) explored pupil response to different images of static robots, from cartoonish to human-like appearance. Reuten et al. (2018) showed that pupil dilated less for the emotional facial expressions of robots judged uncanny by the participants compared to more human-like robots. Moreover, the authors highlighted a similar pupil response between human-like robot and real human emotional facial expressions. To complete our results and extend previous research (Reuten et al. 2018), it would be relevant to add another level of avatar that would be less naturalistic, real, and familiar to check that the larger pupil response observed for avatars in the present study is indeed due to the closeness to reality and the social content, and not to the strangeness of the avatars.

Dynamic facial motion, including both subtle postural adjustments and emotion expression, are ecological stimuli already known to enhance physiological mobilization compared to static faces (Aguillon-Hernandez et al. 2020). Agreeing with the study of Aguillon-Hernandez et al. (2020), we found that adults were more sensitive to dynamic compared to static stimuli, with a larger pupil dilation for macro-motion than for static, whatever the social saliency levels. As the animate monitoring hypothesis states, the human visual system is very sensitive to stimuli that may exhibit biological movement (New et al. 2007). Our study is one of the few exploring the effect of motion on pupil dilation by dissociating the motion dynamics relating to the social aspect and the motion dynamics relating to the real quantity aspect. One interesting result we found about the real quantity of motion is that the larger the quantity of motion the larger the pupil dilates. Our findings shed light on the intricate interplay between the real quantity of motion, social gradient, and pupil dilation. Notably, we observed no potentiation of the effect of the real quantity of motion by the social content on pupil dilation (i.e. no larger response to social motion compared to non-social motion for an equal real quantity of motion), suggesting an additive effect of the two factors. Our results thus suggest that the amount of movement overrides the social nature of the movement. In the context of the social motivation hypothesis (Chevallier et al. 2012) and animate monitoring theory (New et al. 2007), we expected our social stimuli displaying biological motion to be more effective than non-social stimuli displaying natural (non-biological) motion. Indeed, Cheng et al. (2021; 2024) observed a larger pupil dilation in response to biological motion compared to inverted biological motion, with an identical quantity of motion. While the quantity of motion was perfectly matched in their studies, Cheng et al. used refined stimuli aiming at eliciting only biological motion processes, and for which the social content emerged only from the motion of points. The discrepancy with our present study could result from our use of ecological stimuli respecting biophysical laws, containing many social and motion cues, and for which we could only quantify the amount of motion. Our stimuli conveyed social information even in the absence of motion, as illustrated by the difference between social and non-social static stimuli, while Cheng et al. (2021; 2024) did not observe any difference in pupil dilation in response to static upright and inverted point-light displays. Pupil dilation is thus a very sensitive index of overall physiological mobilization dependant on the stimuli saliency and the experimental context. Regarding the emotional saliency, the emotional content of our macromotion social stimuli seems to predominate over the real quantity of motion. As expected from previous studies (Aguillon-Hernandez et al. 2020), we observed larger pupil dilation for sadness compared to neutral static stimuli. To go further, dissociating the amount of motion from the presence of emotion would require testing faces exhibiting other kinds of non-emotional macromotion. However, instances of non-emotional macromotion would either recruit supplementary cognitive processes (e.g. language), or produce physiological contagion (e.g. yawning), or induce joint attention orientation (e.g. large head rotation). Disentangling the real quantity of motion from the emotional content thus remains a challenge and our design cannot dissociate the emotional content from the social motion content.

The influence of motion (related to social or the real quantity aspect) and social content on pupillary dilation may represent parallel information processing streams involving the coordination of interconnected brain regions. Indeed, motion-related visual processing occurs in areas such as MT/MST, which involve motion detection, speed assessment, and tracking of moving objects (Newsome et al. 1990, Giese and Poggio 2003). Moreover, the STS has been shown to be responsive to visual motion, particularly biological motion (Bonda et al. 1996, Puce et al. 2003). While most studies on biological motion have used PLD of body motion, STS, and particularly STSp, has also been shown to be sensitive to dynamic facial information (Puce et al. 1998, 2003, Fox et al. 2008). Interestingly, STS is part of the cerebral network involved in face processing, without considering the movement aspect. This network involved in social-related visual processing, particularly for faces and facial expressions, involve the FFA and OFA for face recognition and the STS for processing social cues and biological movement (Grossman et al. 2000, 2005, Haxby et al. 2000, Grossman and Blake 2002, Thompson et al. 2005, Herrington et al. 2011, Bernstein and Yovel 2015, Bernstein et al. 2018). This suggests that the integration of information about motion and social content occurs at different levels (from early visual processing areas to higher-order cognitive centres), with possibly the STSp region as a convergence hub between the two kinds of information, interpreting these stimuli as socially and dynamically salient.

The STS is one of the cerebral regions in which multisensory integration phenomena have been described (Calvert 2001, Wright et al. 2003, Beauchamp et al. 2004, Beauchamp 2010). While we are not in a multisensory integration context per se, we could consider a possible multisensory-like integration (i.e. non-additive) of the social and movement integration. This is not what we observed. However, even if a non-integrative phenomenon existed, it is also possible that it would not be visible in the pupil response. Indeed, some studies, that have explored multisensory integration at the pupil level, have shown an additive response to multisensory stimulations (Van der Stoep et al. 2021, Liu et al. 2024). Pupil dilation is considered a peripheral index of the activity of the LC and the SC, both modulating arousal and attention orienting (see Strauch et al. 2022 for a complete review), which could have an impact on the processing of visual information. The global pupillary response involves a sophisticated dynamic orchestration of neural networks encompassing visual processing, emotional regulation, cognitive control, and autonomic regulation, finely tuned to the complexities of the dynamic and social environment.

Overall, this study characterized the relative influence of social content of human faces and the effects of motion on pupil reactivity by distinguishing between the motion dynamic related to social aspect and the motion dynamic associated with the apparent quantity aspect. Our results showed that all these aspects contributed to physiological mobilization in an additive fashion. The pupil is a robust physiological index that enables the interpretation of human social stimuli, in particular their social and real quantity aspects. Investigating how pupils respond to social and motion content can provide valuable information on several cognitive aspects, such as attention and arousal, as well as social perception and communication. These implications can help improve the user experience of consumer electronics (e.g. virtual reality) as well as aid clinical diagnosis with specific biomarkers for neurodevelopmental disorders, such as Autism Spectrum Disorder.

Supplementary Material

nsae055_Supp

Acknowledgements

The authors would like to thank the actors for their contribution to the protocol, the FacLab for their contribution to the stimuli creation, and all the participants. The authors would also like to thank Frederic Briend for his advice on statistical analysis.

Author contributions

Nadia Aguillon-Hernandez and Claire Wardak (Conceptualization and Methodology), Claire Wardak, Yassine Mofid, Camille Ricou, Vivien Rabadan (Investigation), Camille Ricou (Formal analysis), Claire Wardak and Camille Ricou (Writing—original draft), Camille Ricou, Vivien Rabadan, Yassine Mofid, Nadia Aguillon-Hernandez, Claire Wardak (Writing—review and editing).

Supplementary data

Supplementary data is available at SCAN online.

Conflict of interest

The authors declared they have no competing or potential conflicts of interest.

Funding

This research was funded in whole by l’Agence Nationale de la Recherche (ANR; ANR-21-CE17-0045). For the purpose of open access, the author has applied a CC-BY public copyright licence to any Author Accepted Manuscript (AAM) version arising from this submission.

Data availability

Requests can be addressed to the corresponding author.
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