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# EXPRESS: How personality shapes gaze behavior without compromising subtle emotion recognition.

Alice Cartaud, Dorine Vergilino-Perez, Laurence Chaby

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Alice Cartaud, Dorine Vergilino-Perez, Laurence Chaby. EXPRESS: How personality shapes gaze behavior without compromising subtle emotion recognition. Quarterly Journal of Experimental Psychology, 2025, 10.1177/17470218251334118. hal-05025409

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How personality shapes gaze behavior without compromising subtle emotion recognition.

<table><tr><td>Journal:</td><td>Quarterly Journal of Experimental Psychology</td></tr><tr><td>Manuscript ID</td><td>QJE-STD-24-397.R1</td></tr><tr><td>Manuscript Type:</td><td>Standard Article</td></tr><tr><td>Date Submitted by the Author:</td><td>17-Feb-2025</td></tr><tr><td>Complete List of Authors:</td><td>Cartaud, Alice; Sorbonne Université Vergilino-Perez, Dorine; Université Paris Cité Institut de Psychologie Chaby, Laurence; Sorbonne Université, Faculté des Sciences et Ingenierie ; Université Paris Cité UFR Institut de Psychologie</td></tr><tr><td>Keywords:</td><td>Dynamic emotional facial expression;, Emotion identification, Fixation proportion, Signal Detection Theory, Subtle and high-intensity emotions</td></tr></table>
How personality shapes gaze behavior without compromising subtle emotion recognition.

Alice Cartaud<fnref n="1" />, Dorine Vergilino-Perez<fnref n="2" />, Laurence Chaby<fnref n="1" />,<fnref n="2" />

**Corresponding author:**

Alice Cartaud
Institut Systèmes Intelligents et de Robotique
Campus Pierre et Marie Curie
Pyramide - Tour 55-56
4, Place Jussieu
75005 Paris - France
alice.cartaud@isir.upmc.fr

<footnote n="1">¹Sorbonne Université, CNRS, Institut des Systèmes Intelligents et de Robotique, ISIR, F-75005 Paris</footnote>

<footnote n="2">²Université Paris Cité, Vision Action Cognition, F-92100 Boulogne-Billancourt, France</footnote>
**Abstract**

Can personality, known to shape emotional experiences and gaze behaviors, influence the recognition of subtle emotional facial expressions? Do these personality-related gaze patterns change with emotional intensity? To explore this, we conducted an eye-tracking experiment with 116 participants who identified dynamic emotional facial expressions of varying intensities (anger, fear, disgust, happiness). Using a multidimensional approach to personality, we clustered participants based on Big Five personality dimensions and anxiety scores, distinguishing those scoring high in dimensions associated with positive (extroversion, agreeableness) vs negative emotions (neuroticism, social anxiety).

Results showed that individuals with a positively-colored personality focused more on the mouth (likely seeking happiness cues), while those with a negatively-colored personality focused more on the eyes (likely seeking signs of negative emotions). However, at higher intensity, these gaze patterns persisted only when emotions matched participants' personality (individuals with a negatively-colored personality focused more on the eyes than others, but only when viewing fear or anger). Yet, personality did not affect the sensory discrimination or decision-making processes for subtle emotion identification.

Thus, personality, as a multidimensional construct, seems to influence gaze strategies, facilitating emotionally congruent experiences without hindering the recognition of others' emotions, even subtle ones, which is crucial for effective social interactions.

**Keywords:** Dynamic emotional facial expression; Emotion identification; Fixation proportion; Signal Detection Theory; Subtle and high-intensity emotions
# 1 Introduction.

Everyday social interactions are influenced by our ability to decode and interpret others' intention and emotional state, which is essential for the appropriate adjustment of our own behavior (Cartaud et al., 2022; Lebert et al., 2024; Xavier et al., 2016). Recognizing emotional facial expressions is a key aspect of the decoding process (Darwin, 1872; Ekman and Friesen, 1971; Ekman and O'Sullivan, 1988; Waller et al., 2017). Facial expressions are processed quickly and they automatically capture attentional resources (Esteves and Öhman, 1993; Palermo and Rhodes, 2007; Sawada et al., 2022; Vuilleumier, 2002). The visual exploration of others' face is crucial for this recognition process and is influenced by the emotion expressed. While multiple facial muscles contribute to the expression of emotions, certain are particularly indicative of specific emotions (Ekman and Friesen, 1978). For instance, in Western Caucasian cultures, happiness is primarily expressed through the mouth, while anger is primarily associated with the activation of the brows.

Similarly, the expression of fear is mainly associated with brow action and the expression of disgust typically relies on the activation of muscles around the nose area. Consequently, our attention is preferentially allocated to these “diagnostic areas” of the face, facilitating the recognition of the corresponding emotions (Calder et al., 2000; Calvo et al., 2018; Smith et al., 2005; Vaidya et al., 2014; Wells et al., 2016, but see also Jack et al., 2012). Thus, when observing emotional facial expressions (e.g., fear), individuals quickly and preferentially allocate attention to congruent facial diagnostic areas (e.g., the eyes) rather than other areas (Calvo et al., 2018; Eisenbarth and Alpers, 2011; Guo, 2012; Wells et al., 2016). However, to gain a more comprehensive understanding of how emotions are
perceived and processed, it is essential to consider individual differences such as personality
traits and the level of social anxiety that guide attention toward specific facial areas
(Günther et al., 2021; Libby and Yaklevich, 1973; Perlman et al., 2009), but also shape how
individuals decode facial expressions and interpret emotional cues (Rusting, 1998).

A relevant approach to studying how individuals perceive emotions in their social world,
and particularly how they direct their gaze to emotional cues in facial expressions, is to use
eye-tracking technology. This method is particularly useful for capturing fine-grained
differences in how personality characteristics influence attention to specific facial areas
(Sarsam et al., 2023; Wright and Ward, 2008). For instance, studies focusing on a specific
personality trait reported that extroverted individuals tend to prioritize their gaze toward
the mouth while avoiding the eye region (Pavic et al., 2021), in particular when looking at
happy faces (Ellingsen et al., 2019). Conversely, individuals scoring high on neuroticism
gaze more toward the eye area of fearful expression (Perlman et al., 2009). Similarly,
individuals with high levels of social anxiety initially focus on the eye area but subsequently
shift their gaze away, regardless of the emotion displayed (Calvo et al., 2018; Chen et al.,
2023; Gutiérrez-García et al., 2019, see also Günther et al., 2021 for a meta-analysis). The
effect of personality traits is also apparent when observing neutral expressions, an
emotionally ambiguous stimulus (Lee et al., 2008). Thus, individuals high in neuroticism
and agreeableness demonstrate a preference for the eyes area when looking at neutral faces
while those scoring high in conscientiousness tend to focus more on the central part of the
face (Sarsam et al., 2023).

This preference for specific facial areas driven by individual characteristics could be linked
to individuals' affective dispositions (Elliot and Thrash, 2002; McCrackin et al., 2022;
Perlman et al., 2009). According to trait-congruency theory, individuals are more inclined to process information in a way that is congruent with their own personality (Bargh et al., 1988; Rusting, 1998). More precisely, individuals may exhibit a selective attentional orientation toward stimuli whose valence aligns with, or is more likely to align with, their personality traits (affective dispositions). As a result, individuals with a predominantly negatively-colored personality (e.g., individuals scoring high in neuroticism) might focus more on the upper part of the face, thereby facilitating the recognition of anger and fear, two negative emotions. Conversely, those with a predominantly positively-colored personality (e.g., extroverted individuals) might focus more on the mouth area to be more likely to observe signals of happiness, a positive emotion.

In addition, personality-related affective dispositions further shape the way we interpret emotional cues (Rusting, 1998). For example, individuals scoring high in anxiety are better at recognizing negative emotions (Doty et al., 2013; Gutiérrez-García and Calvo, 2017; Yoon et al., 2014), while those scoring high in neuroticism exhibit a better ability in identifying fear (Doty et al., 2013) and lower ability in identifying happiness (Andric et al., 2015). In this context, Signal Detection Theory is a valuable tool as it determines whether emotion-specific recognition abilities results from a better discrimination of emotional cues, due to personality-driven attention to specific facial areas, or an interpretation bias, influenced by personality-driven decision making. This theory posits that identifying one stimulus from another relies on two distinct processes: sensory discrimination and decision-making (Green, Swets, et al., 1966; Hautus et al., 2021). The sensory discrimination process is reflected by one's sensitivity index (d'), and enables discriminating a specific emotion (signal) from other emotions (noise), potentially linked to
a specific focus on certain areas of the face. A higher sensitivity index (d') implies a better discrimination between signal and noise. Additionally, the decision-making process is reflected by the *response criterion* (c), which reveals an individual's response bias toward or away from a given signal (irrespective of the signal's presence). A negative value of the response criterion indicates a liberal strategy, biasing responses toward a specific emotion, whereas a positive value indicates a conservative strategy, requiring higher certainty to respond positively to the emotion concerned. Thus, individual differences can be observed in both the sensory and decision-making processes. Specifically, individuals scoring high in anxiety show liberal strategies toward words that represent a threat to the ego in a word recognition task (Dowens and Calvo, 2003) and show higher sensitivity to facial expressions of fear, anger and disgust (Doty et al., 2013; Gutiérrez-García and Calvo, 2017; Yoon et al., 2014). Similarly, individuals scoring high in neuroticism exhibit generally liberal decision-making strategies (Karmon-Presser and Meiran, 2019) and increased sensitivity to fearful faces (Doty et al., 2013). Conversely, individuals scoring high in extroversion favor liberal strategies toward positively valenced words (Kang, 2014).

Under challenging conditions, with emotions exhibited with a low intensity or for a brief duration, individual differences in emotion recognition become particularly salient.

However, it remains unclear whether these differences arise from personality-driven gaze behavior strategies (i.e., a specific focus toward a specific face area that favors the discrimination of an emotional cue), or from an interpretive bias (a preference for answering a specific emotion when uncertain). Indeed, when the emotion is subtle, the activation of relevant facial muscles becomes less pronounced, making them less salient and thereby reducing the attraction of visual attention to the diagnostic areas of the face. Consequently,
the ability to identify subtle emotional facial expressions should depend both on personality-induced preferred focus toward a specific facial area (affecting sensory discrimination) and interpretive bias specific to personality (affecting decision-making). In other words, when the emotion is subtle and the cues of the typical “diagnostic area” are poorly salient, people who naturally tend to focus on this area because of their personality are more likely to detect the emotional signal from this area (sensory discrimination).

However, when in doubt, their personality can also influence their interpretation of the emotional signal, making them more inclined to respond one emotion in particular (interpretive bias) even when observing neutral expressions.

So far, research has either focused on gaze behavior or decision strategy, often isolating a single personality trait or relying on static facial expressions at their apex. Here, we adopt a multidimensional approach to personality and use dynamic facial expressions of varying intensity to better capture the subtle dynamics of emotional experience in social interactions. We aimed to investigate whether differences in the recognition of emotional facial expressions, based on personality characteristics, depended on personality-driven gaze behavior or personality-driven interpretive bias. Our goal was threefold: 1) examine the influence of personality on gaze behavior during the observation of neutral faces, 2) identify whether this personality-specific gaze behavior extends to the observation of emotional facial expressions across varying intensities, and 3) investigate whether this gaze behavior, induced by personality, influences sensitivity index (reflecting the ability to discriminate between a specific emotion and others) and if it is associated with a specific response bias.

To do so, we assessed participants' personality profiles and recorded their gaze behavior during an emotional facial expression identification task. Stimuli were dynamic facial
expressions – anger, disgust, fear or happiness – displayed by virtual agents at varying
intensity levels. Some trials included neutral expressions to examine potential response bias
toward specific emotion (see Gutiérrez-García and Calvo, 2017 for similar procedure). We
based our analysis on personality valence (affective disposition), as suggested by
trait-congruence theories, and built our experimental groups using hierarchical clustering.

Our hypotheses were as follow:

**H1a:** When looking at neutral faces, individuals with a more positively-colored personality should spend more time on the mouth (area associated with the observation of happiness) than those with a negatively-colored personality who should spend more time on the eyes (area associated with the observation of anger or fear).

**H1b:** For low-intensity emotional faces, the same pattern of results as with neutral expressions is expected, regardless the emotion: individuals with a more positively-colored personality should spend more time on the mouth than those with a negatively-colored personality who should spend more time on the eyes.

**H1c:** At higher intensities, gaze behavior should be driven both by the stronger muscle activation and by personality-related affective dispositions: individuals with a negatively-colored personality should spend more time than others on the eye area only when the emotion is anger or fear. Conversely, those with a positively-colored personality should spend more time than others on the mouth, but only when the emotion is happiness. The aim being to be more exposed to the valence congruent with their affective disposition, in line with the trait-congruency theory.

**H2:** Assuming H1b is true, personality-induced gaze behaviors will impact sensitivity (d')
for low-intensity emotional facial expressions (e.g., larger *d'* for individuals with a negatively-colored personality – looking more at the upper part of the face – for expressions of anger or fear; and larger *d'* for individuals with a positively-colored personality – looking more at the lower part of the face – for expression of happiness).

**H3:** Response bias will depend on personality profile (e.g., lower *c* value –liberal strategies– for individuals with a positively-colored personality for expressions of happiness; and lower *c* value for individuals with a negatively-colored personality for facial expressions of fear, anger, or disgust).

# 2 Method.

## 2.1 Participants.

Out of 120 participants, four were excluded due to poor quality eye-tracking data, leaving 102 females ($M_{age} = 19.97, SD_{age} = 3.88$). A minimum sample size of 100 participants was determined for Bayesian inference with a Bayes Factor$_{10} >3$, a medium effect size ($d = 0.5$, Gutiérrez-García and Calvo, 2017) and mildly informative priors ($\mu = 0, \sigma = 1$).

Participants were psychology students and compensated with course credit. They had correct or corrected-to-correct vision and gave their written informed consent for participation. The research received ethical approval (IRB: 00012022-104).

## 2.2 Material.

The stimuli consisted of videos (1920 × 1080 px) composed of morphed images of the head and the neck of 12 virtual agents (six males, 500 px × 770 px) created on FaceGen
Modeller, and depicting facial expressions of anger, disgust, fear, or happiness, at varying intensity. The maximum intensity could be low (12.5%), medium (25%), high (50%) or null (0%, neutral trials). We opted for these four emotions because their “diagnostic areas” are primarily located either in the upper part of the face (anger, fear) or the lower part (happiness, disgust).

We generated the videos by morphing the image of the neutral face with an emotional face using Adobe After Effects. Subsequently, we compiled the resulting images into 30 frames per second videos of 2000 ms each, ensuring that the face remained neutral for the first 1000 ms. Videos with neutral facial expressions featured a single image of the agents. We controlled videos' luminance using Matlab (version R2013b, MathWorks) and displayed the faces on a gray background. A 300 ms backward visual mask, produced by pixel permutation of the last frame within a ±100 pixel range followed each video. Backward masking suppresses the neural response following the stimulus' offset (Macknik and Livingstone, 1998) by altering their visual characteristics of the stimuli while preserving the color and luminance. Material is available online (https://doi.org/10.5281/zenodo.13739938).

We recorded participants' gaze behavior monocularly from the right eye at 1000 Hz using an Eyelink-1000 eye-tracking system (SR Research Ltd.) positioned below a 25" monitor. Participants' head was stabilized on a headrest 57 cm away from the monitor.
## 2.3 Procedure.

### 2.3.1 Emotion identification task.

In this main task, participants identified the emotion displayed, while we recorded their gaze behavior. Each trial started with participants fixating a central target to perform a drift correction of the eye position (Figure 1A). Subsequently, a video featuring an agent displaying a neutral or an emotional facial expression of increasing intensity appeared on the screen. A backward visual mask appeared directly after the video for 300 ms, followed by response boxes labeled “anger”, “disgust”, “fear” and “happiness” (four-alternative forced choice task), with no option to choose “neutral”. This absence of a “neutral” label allowed us to detect any potential response bias favoring a specific emotion (Gutiérrez-García and Calvo, 2017). Participants had to click as fast as possible on the name of the emotion they had just observed, while minimizing errors. The position of the labels was counterbalanced across participants.

Participants completed a total of 156 trials (144 emotional trials (12 actors × 4 emotions × 3 intensities) + 12 neutral trials) preceded by a 4-trial training session. The experimental task consisted of three blocks, each starting with a 9-point calibration and separated by short breaks. The order of trials was pseudo-randomized to ensure that the same emotion did not appear consecutively, and that every agent appeared at least once per emotion per block.
<figure><img src="image_3.png" /><figcaption>Figure 1: (A) Sequence of events of an experimental trial in the emotion identification task. S1: first exposure phase, the face remains neutral (from 0 to 1000 ms), S2: second exposure phase, the emotion appears and increases in intensity (from 1000 to 2000 ms). (B) During neutral trials, the intensity remains null and the neutral face is displayed for the duration of the presentation. (C) Three areas of interest used for eye-tracking analysis: eye (width: 352 px; height: 129 px), nose (width: 352 px; height: 110 px) and mouth (width: 352 px; height: 110 px). A preview of the stimuli is available on (https://doi.org/10.5281/zenodo.13739938).</figcaption></figure>

2.3.2 Personality measures.

After the experiment, participants completed two questionnaires computerized with lab.js (Henninger et al., 2019): the French version of the Big Five (BFI-Fr, McCrae and Costa Jr, 1997; Plaisant et al., 2010) and of the Liebowitz social anxiety scale (LSAS, Liebowitz, 1987; Yao et al., 1999). The BFI-Fr assesses five personality dimensions: openness to
experience, conscientiousness, extroversion, agreeableness, and neuroticism, with scores for each trait derived from averaging nine associated items on a 5-point Likert scale. The LSAS evaluates the level of social anxiety through 24 items, each rated for anxiety (ranging from none to severe) and avoidance (ranging from never to habitual) on a 4-point scale.

We applied hierarchical clustering (using Ward's method) on centered scaled personality scores from the BFI-Fr and the LSAS to identify the optimal number of groups. This unsupervised method organizes data based on their (dis)similarities to minimize the variance within each cluster. Based on the dendrogram plot, we identified two clusters. The first cluster, labelled "positively-colored personality" (n = 50), included participants scoring high in personality dimensions associated with positive emotions (i.e., extroversion, agreeableness, John, Srivastava, et al., 1999). The second cluster, labelled "negatively-colored personality" (n = 66) grouped participants scoring high on neuroticism and social anxiety, dimensions associated with negative emotions (Table 1). We performed Bayesian regressions to confirm the differences between the two clusters. They differed for each personality characteristic except for openness to experience (see Appendix A for a detailed description of the clusters and the analysis performed).

<table><caption>Table 1: Mean (standard deviation) of personality characteristics for each cluster.</caption><thead><tr><th>Cluster</th><th>O</th><th>C</th><th>E</th><th>A</th><th>N</th><th>SA</th></tr></thead><tbody><tr><td>Positive</td><td>3.71 (0.62)</td><td>3.51 (0.57)</td><td>3.56 (0.72)</td><td>4.24 (0.36)</td><td>2.93 (0.80)</td><td>43.00 (15.97)</td></tr><tr><td>Negative</td><td>3.55 (0.62)</td><td>3.18 (0.81)</td><td>2.49 (0.77)</td><td>3.68 (0.58)</td><td>3.96 (0.62)</td><td>73.92 (19.52)</td></tr></tbody></table>

**Note:** Positive: positively-colored personality (n = 50), Negative: negatively-colored personality (n = 66). O: openness to experience, C: conscientiousness, E: extroversion, A: agreeableness, N: neuroticism, SA: social anxiety.
### 2.3.3 Processing of the eye-tracking data.

We created areas of interest (AOI, Figure 1C) prior to the experiment. Their size ensured that every AOI of the face (eye, nose, mouth) stayed in, whatever the agent, emotion or intensity. We processed eye-tracking data related to the emotional trials correctly identified and related to the neutral trials separately using EyetrackingR (Dink and Ferguson, 2015). We conducted the analysis in several steps. Initially, for each trial (neutral and emotional), we separated the data into two distinct exposure phases: the first phase (S1) covered the first second, during which the face remained neutral in emotional trials (from 0 ms to 1000 ms), and the second phase (S2) covered the next second when emotions were expressed in emotional trials. Then, we removed the trials with over 25% of trackloss (emotional trials: 1.34% for S1 and 0.71% for S2; neutral trials: 2.22% for S1 and 1.08% for S2). Finally, we computed the proportion of fixation time (expressed in percentage) within each AOI for each participant and trial for the first (S1) and the second phases (S2) of the neutral and the emotional trials.

### 2.3.4 Identifying gaze behavior over time.

To elucidate the temporal dynamics of gaze behavior related to personality when observing emotional facial expressions, we conducted an exploratory growth curve analysis (Mirman, 2014). This analysis focused on gaze behavior during low-intensity facial expressions trials. The muscular activation of the face being more subtle, the effect of personality on gaze behavior should be more pronounced. For each participant and each trial, we calculated proportion of fixation within each AOI in 50 ms time-bins during S2 of the emotional trials (from 1000 ms to 2000 ms). Then, for each AOI, we fitted fixation proportions over the
time-bins as a function of fourth-order orthogonal polynomials (Mirman, 2014), and the interaction between the polynomials and the emotion presented and personality (through the clusters). The linear term (first order) represents the slope (linear increase/decrease) of fixation proportion over time, the quadratic term (second order) reflects the sharpness of the central peak; the cubic and quartic terms (third and fourth order respectively) reflect the sharpness of the inflexions at the extremities of the curve (Kalénine et al., 2012).

### 2.3.5 Emotion identification.

We applied Signal Detection Theory to analyze the identification of low-intensity emotions. Initially, for each emotions and each participant, we identified the probability of hits (i.e., accurate recognition of the displayed emotion) and of false alarms (i.e., incorrect identification of an emotion that is not present) following the methodology outlined by Gutiérrez-García and Calvo (2017). We applied the log-linear correction by increasing each frequency by 0.5 before computing the z scores (to prevent infinite z scores, Hautus, 1995). Then, we derived the sensitivity index $d'$ and the response bias c using equations 1 and 2 respectively (Hautus et al., 2021). In addition, we analyzed the proportion of response for each emotion in trials where the facial expression remained neutral.

$$d' = z(\text{Hit}) - z(\text{False Alarm}) \qquad (1)$$

$$c = -\frac{1}{2}(z(\text{Hit}) + z(\text{False Alarm})) \qquad (2)$$
### 2.3.6 Statistical analysis.

Data treatment and statistical analysis were conducted on R (version 4.1.3, R Core Team, 2018) and RStudio (version 2022.12.0, RStudio Team, 2020). We performed Bayesian regressions using the brms package (version 2.13.0; Bürkner, 2017). We fitted each mixed-model using four independent Markov chains (2,000 iterations each, with 1000 warm-up) and applied mildly informative normally-distributed priors. We report the median contrasts of posterior distributions and 95% credible interval (95% CI: the range containing the most credible values given the data and the model). If the 95% CI of a contrast between two conditions exclude 0, the difference between the two conditions can be supported. However, if the 95% CI includes 0, the null hypothesis cannot be rejected.

Detailed descriptions of each statistical model are available in Appendix C and data and statistical analysis are available online (https://zenodo.org/records/13739938).

## 3 Results

Our analysis of the results is structured into three distinct sections. First, we examined the influence of personality on proportion of fixation time allocated to both neutral and emotional facial expressions of varying intensities, addressing H1. Then, we conducted an exploratory analysis to investigate the dynamic changes in gaze behavior over time depending on both personality and the emotion presented. Finally, we focused on the impact of personality on emotion identification. This section includes an assessment of how personality affects the categorization of neutral faces, as well as its effect on the parameters of Signal Detection Theory (d' and c), relevant to hypotheses H2 and H3. For the sake of
conciseness and relevance, we report only the statistical results that test our hypotheses.

Main effects of AOI on percentage of fixation time and main effects of emotion on Signal Detection Theory parameters are available on Appendix D and B respectively.

## 3.1 Effect of personality on fixation proportion.

### 3.1.1 Neutral trials.

Our analysis of the neutral trials partially supported H1a, whether relative to the first (S1, averaged R-Squared of the fit: $R^2 = 0.40$, 95% CI [0.03, 0.49]) or second exposure phases (S2, averaged R-Squared of the fit: $R^2 = 0.01$, 95% CI [0.01, 0.01]) of the trials. The negatively-colored personality cluster spent more time than the other cluster on the eye AOI, in both S1 and S2. However, it spent more time on the nose only in S1. Finally, no difference emerged for the mouth, either for S1 or S2 (Table 2 and Figure 2, first and second lines).

<table><caption>Table 2: Median estimates of the contrasts between the negatively and the positively colored clusters and 95% Confidence Intervals of the percentage of fixation time as a function of AOI.</caption><thead><tr><th>Phase</th><th>AOI</th><th>Estimate</th><th>95% CI</th></tr></thead><tbody><tr><td rowspan="3">Neutral S1</td><td>Eye</td><td>+2.93</td><td>[+1.30, +4.65]</td></tr><tr><td>Nose</td><td>-2.17</td><td>[-3.73, -0.63]</td></tr><tr><td>Mouth</td><td>-2.81</td><td>[-5.91, +0.46]</td></tr><tr><td rowspan="3">Neutral S2</td><td>Eye</td><td>+2.34</td><td>[+0.54, +3.97]</td></tr><tr><td>Nose</td><td>+0.43</td><td>[-2.01, +1.28]</td></tr><tr><td>Mouth</td><td>-1.74</td><td>[-4.01, +0.27]</td></tr><tr><td rowspan="3">Emotion S1</td><td>Eye</td><td>+2.19</td><td>[+1.53, +2.99]</td></tr><tr><td>Nose</td><td>-1.94</td><td>[-2.64, -1.28]</td></tr><tr><td>Mouth</td><td>+1.03</td><td>[-0.16, +2.25]</td></tr></tbody></table>

**Note:** Contrasts are credible if the 95% CI exclude 0 (in bold). Negative estimates indicate that the negatively-colored personality cluster spend less time than the other cluster on a specific AOI.
<figure><img src="image_4.png" /><figcaption>Figure 2: Median estimates of the contrasts (dots) between the negatively and positively-colored personality clusters and the associated 95% CI (horizontal lines) in percentage of fixation time as a function of AOI, intensity and emotion. There is evidence of differences between clusters when the 95% CI does not contain 0 (represented by the vertical dashed lines). In this case, the 95% CI are in solid line, otherwise, they are striped. Negative estimates indicate that the negatively-colored personality cluster spend less time on the AOI than the positively-colored personality cluster for a specific condition. The color code (black and gray) changes between intensities (low, medium, high) to enhance the figure readability. The two first lines regard the first (S1) and the second exposure phase (S2) of the neutral trials respectively, and the third line represents the first phase (S1) of the emotional stimuli. The other lines, below the horizontal dotted line, represent S2 of the emotional stimuli for the different intensities.</figcaption></figure>

### 3.1.2 Emotional trials.

In the first exposure phase of emotional trials (S1), where the face remained neutral, we observed a pattern similar to the neutral trials (averaged R-Squared of the fit: $R^2 = 0.05$, 95% CI [0.05, 0.06], Table 2). Partially supporting H1a, the negatively-colored personality cluster spent more time on the eye AOI and less on the nose AOI than the positively-colored personality cluster. No difference emerged between them for the mouth AOI (Figure 2, third line).
This personality effect persisted in S2 (averaged R-Squared of the fit: *R*² = 0.01, 95% CI [0.01, 0.01]) for low-intensity emotional stimuli, also partially supporting H1b (Table 3.

More precisely, the negatively-colored personality cluster spent more time on the eye AOI than the other cluster for anger, disgust, and happiness, but not for fear. The difference between the two clusters disappeared for the nose AOI, regardless of the emotion, and emerged for the mouth AOI only in the case of happiness and disgust: the negatively-colored personality cluster spent less time on the mouth compared to the other cluster. (Figure 2).

At medium intensity, the negatively-colored personality cluster continued to spend more time than the other cluster on the eye AOI, but for disgust and happiness only. Regarding the nose AOI, the positively-colored personality cluster spent more time than the other cluster for disgust and fear. Finally the positively-colored personality cluster spent more time on the mouth AOI than the other cluster for anger only.

At high intensity, consistent with H1c: i) the negatively-colored personality cluster spent more time than the other cluster on the eye AOI, but only when the emotion was anger or fear; ii) it also spent more time on the nose AOI for disgust, but not for anger nor fear; iii) and less time than the other cluster on the mouth AOI when the emotion was happiness, but not for anger, nor disgust. However, and not supporting H1c: i) the negatively-colored personality cluster also spent more time than the other cluster on the nose AOI when the emotion was happiness ; and ii) less time on the mouth AOI when the emotion was fear.
<table><caption>Table 3: Median estimates and 95% Confidence Intervals (in percentage point of fixation time) of the contrasts between the negatively and the positively colored clusters as a function of Intensity, AOI, and Emotion.</caption><thead><tr><th>Intensity</th><th>AOI</th><th>Emotion</th><th>Estimate</th><th>95% CI</th></tr></thead><tbody><tr><td rowspan="4">Low</td><td rowspan="4">Eye</td><td>Anger</td><td>+1.66</td><td>[+0.01, +3.46]</td></tr><tr><td>Disgust</td><td>+3.36</td><td>[+1.03, +5.55]</td></tr><tr><td>Fear</td><td>+1.35</td><td>[-0.98, +3.60]</td></tr><tr><td>Happiness</td><td>+2.26</td><td>[+0.45, +3.98]</td></tr><tr><td rowspan="4">Low</td><td rowspan="4">Nose</td><td>Anger</td><td>-1.72</td><td>[-3.63, +0.00]</td></tr><tr><td>Disgust</td><td>-1.25</td><td>[-3.63, +1.18]</td></tr><tr><td>Fear</td><td>-1.87</td><td>[-4.33, +0.38]</td></tr><tr><td>Happiness</td><td>+0.01</td><td>[-1.74, +1.79]</td></tr><tr><td rowspan="4">Low</td><td rowspan="4">Mouth</td><td>Anger</td><td>-1.94</td><td>[-4.25, +0.17]</td></tr><tr><td>Disgust</td><td>-4.54</td><td>[-7.34, -1.80]</td></tr><tr><td>Fear</td><td>-1.17</td><td>[-3.99, +1.75]</td></tr><tr><td>Happiness</td><td>-2.12</td><td>[-4.14, -0.06]</td></tr><tr><td rowspan="4">Medium</td><td rowspan="4">Eye</td><td>Anger</td><td>+1.27</td><td>[-0.37, +2.96]</td></tr><tr><td>Disgust</td><td>+2.33</td><td>[+0.37, +4.52]</td></tr><tr><td>Fear</td><td>+1.54</td><td>[-0.62, +3.32]</td></tr><tr><td>Happiness</td><td>+2.20</td><td>[+0.53, +3.96]</td></tr><tr><td rowspan="4">Medium</td><td rowspan="4">Nose</td><td>Anger</td><td>-0.67</td><td>[-2.39, +1.08]</td></tr><tr><td>Disgust</td><td>-2.27</td><td>[-4.27, -0.09]</td></tr><tr><td>Fear</td><td>-1.99</td><td>[-4.04, -0.16]</td></tr><tr><td>Happiness</td><td>-0.79</td><td>[-2.47, +0.85]</td></tr><tr><td rowspan="4">Medium</td><td rowspan="4">Mouth</td><td>Anger</td><td>-3.34</td><td>[-5.58, -1.24]</td></tr><tr><td>Disgust</td><td>-1.86</td><td>[-4.44, +0.60]</td></tr><tr><td>Fear</td><td>+0.22</td><td>[-2.27, +2.39]</td></tr><tr><td>Happiness</td><td>-1.47</td><td>[-3.43, +0.54]</td></tr><tr><td rowspan="4">High</td><td rowspan="4">Eye</td><td>Anger</td><td>+1.54</td><td>[+0.07, +3.22]</td></tr><tr><td>Disgust</td><td>+0.13</td><td>[-1.74, +2.20]</td></tr><tr><td>Fear</td><td>+2.52</td><td>[+0.78, +4.28]</td></tr><tr><td>Happiness</td><td>+0.25</td><td>[-1.38, +1.88]</td></tr><tr><td rowspan="4">High</td><td rowspan="4">Nose</td><td>Anger</td><td>-0.98</td><td>[-2.54, +0.74]</td></tr><tr><td>Disgust</td><td>+2.07</td><td>[+0.27, +4.17]</td></tr><tr><td>Fear</td><td>-0.71</td><td>[-2.46, +1.02]</td></tr><tr><td>Happiness</td><td>+1.74</td><td>[+0.07, +3.33]</td></tr><tr><td rowspan="4">High</td><td rowspan="4">Mouth</td><td>Anger</td><td>-1.57</td><td>[-3.69, +0.47]</td></tr><tr><td>Disgust</td><td>-1.53</td><td>[-4.17, +0.83]</td></tr><tr><td>Fear</td><td>-2.13</td><td>[-4.23, -0.06]</td></tr><tr><td>Happiness</td><td>-3.59</td><td>[-5.76, -1.71]</td></tr></tbody></table>

Note: Contrasts are credible if the 95% CI excludes 0 (in bold). Negative estimates indicate that the negatively-colored personality cluster spends less time on the AOI than the other cluster.
## 3.2 Gaze behavior over time.

The exploratory analysis, centered on the presentation of low-intensity emotional stimuli, revealed no difference between clusters regarding the curve shape of gaze behavior over time on the eye AOI and the mouth AOI, whatever the emotion. However, a distinct pattern emerged for the nose AOI (averaged R-Squared of the fit: $R^2 = 3.11E-5$, 95% CI [1.99E-5, 4.72E-5]): the cubic time term was lower for the positively-colored personality cluster compared to the negatively-colored personality cluster (-38.83E-4 percentage point, 95% CI [-74.34E-4, -4.87E-4]), indicating a faster increase of fixation proportion over the nose AOI for the negatively-colored personality cluster (Figure 3B). Specifically, the fixation proportion was lower for the negatively-colored personality cluster at the onset of this period then increased rapidly (without exceeding the positively-colored personality cluster).

## 3.3 Effect of personality on emotion identification.

In our analysis of bias in identifying emotions from neutral faces, we observed a higher categorization as happiness (31.67%, 95% CI [28.63, 34.67]), followed by disgust (25.69%, 95% CI [28.63, 34.67]) and fear (24.13%, 95% CI [21.32, 26.87]), with anger being the least frequent (20.24%, 95% CI [17.46, 22.96]). No differences between the clusters emerged (i.e., the 95% CI for the differences between the two clusters for each emotion included 0). In addition, and as shown in Figure 4, overall accuracy in correctly identifying emotions across all intensities was relatively high. Low-intensities were the least accurately identified (65.52%, 95% CI [63.64, 67.33]), followed by medium-intensity (80.14%, 95% CI [78.80, 81.59], +14.61 percentage points, 95% CI [+13.04, +16.15]), while high-intensity emotions
<figure><img src="image_5.png" /></figure>

Figure 3: A) Mean fixation proportion over time (raw data) over the eye AOI (left panel), the nose AOI (central panel) and the mouth AOI (right panel) as a function of cluster and emotion. The vertical dotted lines represent the onset of emotion (1000 ms after the onset of the trial). The ribbons represent the standard error. B) Mean fitted fixation proportion over time from the onset of the emotion on the nose AOI as a function of cluster. Dots and error bars represent the mean and standard error for each 50 ms time bin respectively.

were identified with the highest accuracy (87.21%, 95% CI [86.11, 88.28], +7.06 percentage points, 95 % CI [+5.79, +8.40]).

Then, we focused on Signal Detection Theory parameters for low-intensity emotions, testing H2 and H3. Since the identification performance was the lowest at this intensity, differences between clusters should be the most pronounced. Despite the influence of personality on
<figure><img src="image_6.png" /><figcaption>Figure 4: Confusion matrix between emotions for each intensity. A: anger, D: disgust, F: fear, H: happiness.</figcaption></figure>

gaze behavior, this did not impact the sensitivity index *d*', thereby not supporting H2. The *d*' values, indicating the ability to correctly identify emotions, did not differ between the clusters, regardless of the emotion (Table 4, averaged R-Squared of the fit: *R*² = 0.70, 95% CI [0.66, 0.73]).

Furthermore, our findings did not support H3, as the response bias *c* indicative of a tendency to favor certain emotions, was unaffected by personality since no difference emerged between the two clusters (Table 4, averaged R-Squared of the fit: *R*² = 0.36, 95% CI [0.31, 0.41]).

# 4 Discussion

This study investigated how the exploration and recognition of emotional facial expressions are influenced by personality-specific gaze behavior. We aimed to understand whether these gaze patterns affect the identification of subtle emotional cues in facial expressions. To do
<table><caption>Table 4: Posterior estimates of *d'* and *c* for the negatively- and positively-colored clusters, along with contrast estimates and 95% confidence intervals for the contrasts between the two clusters in response to low-intensity stimuli.</caption><thead><tr><th>Parameter</th><th>Emotion</th><th>Negative</th><th>Positive</th><th>Contrast estimate</th><th>95% CI</th></tr></thead><tbody><tr><td><i>d'</i></td><td>.</td><td>1.45</td><td>1.40</td><td>+0.05</td><td>[-0.07, +0.16]</td></tr><tr><td><i>d'</i></td><td>Anger</td><td>1.76</td><td>1.73</td><td>+0.03</td><td>[-0.14, +0.19]</td></tr><tr><td><i>d'</i></td><td>Disgust</td><td>0.95</td><td>0.90</td><td>+0.05</td><td>[-0.11, +0.21]</td></tr><tr><td><i>d'</i></td><td>Fear</td><td>1.01</td><td>1.01</td><td>0</td><td>[-0.17, +0.15]</td></tr><tr><td><i>d'</i></td><td>Happiness</td><td>2.08</td><td>1.96</td><td>+0.11</td><td>[-0.05, +0.28]</td></tr><tr><td><i>c</i></td><td>.</td><td>0.44</td><td>0.44</td><td>-0.01</td><td>[-0.06, +0.05]</td></tr><tr><td><i>c</i></td><td>Anger</td><td>0.40</td><td>0.39</td><td>+0.01</td><td>[-0.09, +0.11]</td></tr><tr><td><i>c</i></td><td>Disgust</td><td>0.54</td><td>0.63</td><td>-0.09</td><td>[-0.19, +0.02]</td></tr><tr><td><i>c</i></td><td>Fear</td><td>0.63</td><td>0.68</td><td>-0.04</td><td>[-0.14, +0.07]</td></tr><tr><td><i>c</i></td><td>Happiness</td><td>0.17</td><td>0.09</td><td>+0.09</td><td>[-0.02, +0.18]</td></tr></tbody></table>

**Note:** Negative: negatively-colored personality, Positive: positively-colored personality.

so, we conducted an experiment during which we recorded participants' gaze behavior as they identified dynamic emotional facial expressions of anger, disgust, fear, and happiness. The intensity of the facial expressions could be low, medium, high, or remain neutral. We categorized participants into personality-based clusters: one comprising positively-colored, socially-inclined individuals and another comprising negatively-colored, more introverted individuals. Furthermore, we employed Signal Detection Theory to analyze whether their responses to the identification of subtle emotional facial expressions differed between clusters be in terms of sensory discrimination or interpretive bias.

## 4.1 Effect of personality on gaze behavior.

To investigate the presence of personality-specific gaze patterns, we examined whether the gaze behavior of the two clusters differed across the areas of interest (eyes, nose, mouth), starting with neutral expressions, followed by subtle, and then more intense emotional expressions. Consistent with the trait-congruency theory, when viewing neutral faces,
individuals with a more negatively-colored personality spent more time on the eye area, a region associated with negative emotions (such as anger or fear) than those with a more positively-colored personality (Bargh et al., 1988; Perlman et al., 2009; Rusting, 1998).

Interestingly, contrary to our expectation, individuals with a positively-colored personality did not spend more time on the mouth area than other individuals (Ellingsen et al., 2019; Pavic et al., 2021), but on the nose area. This specific gaze pattern was most evident during the first second of fixation, indicating a dominance of automatic, personality-driven processes in this initial period. The emergence of this nose-focused pattern may be attributed to the multidimensional nature of personality within our clusters. Hence, participants in the positively-colored cluster also exhibited higher levels of conscientiousness, a trait known to foster focus on the central part of the face, thereby offering a more holistic view of the face (McCrackin et al., 2022; Sarsam et al., 2023).

This difference between the two groups partly persisted with subtle emotions. Specifically, the negatively-colored personality cluster continued to spend more time on the eye area than the positively-colored personality cluster except for fear. This absence of difference between the two groups for fear may be attributed to the eyes' critical role in identifying threats, a fundamental aspect regardless of personality. The differences between groups for the nose area dissipated as soon as emotion emerged, possibly due to a rapid shift in gaze toward this area in the negatively-colored personality group, as revealed by the growth curve analysis. Indeed, when confronted with dynamic stimuli, individuals tend to fixate more on the central part of the face, taking advantage of kinematic information from the whole face to recognize emotions (Blais et al., 2017; Roy et al., 2010). In addition, the positively-colored personality cluster spent more time on the mouth area than the
negatively-colored personality cluster for emotions of happiness and unexpectedly for disgust. This specific gaze for disgust faces may stem from the proximity between the mouth and nose AOI boundaries. A more data-driven analysis could potentially overcome this constraint, providing a finer-grained understanding of gaze behavior and its relation to emotional recognition. While this approach was not adopted in the current study, future research could uncover more nuanced findings (Caldara and Miellet, 2011). Indeed, previous data-driven analyses have highlighted gaze differences between groups during face perception that might not be captured by traditional AOI methods (Masulli et al., 2022). Adopting a data-driven approach in future studies could yield valuable insights, furthering our understanding of how personality influences gaze behavior and even how gaze behavior predicts personality. Taken together, the results suggest that even at low intensity, muscle activation induced by dynamic emotional facial expressions affects differently gaze behavior, depending on personality profile, in line with the trait-congruency theory.

Although we expected the effect of emotions to increase with intensity, the salience – linked to muscle activation – was stronger than our initial predictions for medium intensity emotions. More precisely, both clusters spent the same amount of time on AOIs when they were diagnostic for the emotion presented. Personality effect only emerged for non-diagnostic areas, with the negatively-colored personality cluster spending more time than the other over the eyes area for expressions of happiness and disgust, and the positively-colored personality cluster spending more time than the other cluster over the mouth area, but only when viewing angry faces.

Regarding high-intensity emotions, individuals with a positively-colored personality spent more time on the diagnostic areas of happiness (e.g., the mouth), compared to other
individuals. Conversely, those with a negatively-colored personality focused more than others on the diagnostic areas of negative emotions (e.g., the eyes in fear and anger expressions and the nose in disgust expression). Our results therefore suggest that personality potentiates gaze behaviors that favor the exposure to emotions congruent with its positive/negative coloration, in accordance with the trait-congruency theory. One interesting point concerns the change of gaze strategies between medium and high intensity. While at high intensity personality influences gaze patterns toward diagnostic areas of the displayed emotions; at medium intensity, salience takes over. This suggests a complex interplay between the salience of emotions and the influence of personality on gaze behavior, indicating distinct dynamics across different intensities of emotional expressions that requires further investigation.

## 4.2 Identification of low-intensity emotional facial expressions.

We then investigated whether these personality-related gaze behaviors could affect the identification of subtle expressions applying Signal Detection Theory. Our study reveals a homogeneous sensory discrimination across different personality clusters when identifying low-intensity emotions. This finding could be attributed to the dynamic nature of the stimuli used, which, even at a low intensity of 12%, were reasonably identified by participants. Consistent with prior research, dynamic facial expressions, offering more detailed kinematic cues, are generally recognized more easily than static representations (Barrett et al., 2019; Blais et al., 2017; Bould and Morris, 2008; Jack et al., 2016; Roy et al., 2010). In addition, this effective identification was particularly pronounced for happiness and anger, emotions we hypothesized to be most distinctively recognized between
the two groups. This may result from the early attentional orientation toward negative
emotions, and to the facial features associated with expressions of happiness, particularly
salient, both favoring better recognition of these emotions (Calvo and Nummenmaa, 2008;
Lagattuta and Kramer, 2017; Wells et al., 2016). The lack of differences in response bias
across the groups, coupled with their comparable performance in categorizing neutral
expressions, suggests that personality-driven gaze behaviors do not adversely affect the
recognition of subtle emotional expressions, especially at lower intensities. Another point to
consider is that, unlike previous studies that typically focus on isolated traits by comparing
individuals high versus low on a given dimension (Doty et al., 2013; Dowens and Calvo,
2003; Gutiérrez-García and Calvo, 2017; Karmon-Presser and Meiran, 2019; Yoon et al.,
2014), our unsupervised clustering approach captures personality as a multidimensional
construct. While this multidimensional perspective may fade specific personality trait
effects on emotion recognition, it offers a more ecologically valid framework for
understanding how personality shapes emotion recognition. Future research could further
investigate how this multidimensional approach to personality influences emotional
experiences in more naturalistic social contexts, such as real-world interactions or situations
involving more complex emotions.

To conclude, the results of this study provide a deeper insight into the impact of personality,
and more precisely affective disposition, on gaze behavior in emotional social situations. We
found that individuals possess a robust capacity to accurately perceive and interpret subtle
emotional expressions, an ability that remains consistent despite the variations in gaze
patterns influenced by personality. More precisely, personality guides attention toward
facial features that align with one's affective disposition, regardless of the presence or
subtlety of emotions, thereby facilitating exposure to more intense emotional expressions.

## Acknowledgments

The Authors would like to thank *** for programming the experiment.

## Contribution

A: Conceptualization, Methodology, Software, Validation, Formal analysis, Data curation, Investigation, Visualization, Writing - original draft, review & editing

B: Conceptualization, Methodology, Validation, Resources, Visualization, Writing - original draft, review & editing

C: Conceptualization, Methodology, Validation, Resources, Visualization, Writing - original draft, review & editing

## Statements and declarations

### Ethical considerations

The research received ethical approval (IRB: 00012022-104).

### Consent to participate

Participants gave their written informed consent for participation. Consent form received the ethical approval.
**Consent for publication**

Not applicable.

**Declaration of Conflicting Interests**

The authors declared that they had no conflicts of interest with respect to their authorship or the publication of this article.

**Funding**

This work has been supported by the French National Agency (ANR) (In-PACT, project ANR-19-CE28-0011). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

**Data availability**

Data, analysis code, and research materials are available at

https://zenodo.org/records/13739938.
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# Appendices

## A Details of the hierarchical clustering analysis

<figure><img src="image_7.png" /><figcaption>Figure A.1: Dendrogram plot using Ward's method. The negatively-colored personality cluster is in gray, the positively-colored personality cluster is in black. The x-axis groups the participants based on their distance from their cluster.</figcaption></figure>
<figure><img src="image_8.png" /><figcaption>Figure A.2: Correlations between personality traits centered scores and density plots for each personality trait depending on the cluster. O: openness to experience, C: conscientiousness, E: extroversion, A: agreeableness, N: neuroticism, SA: social anxiety. Corr: correlation on the entire sample, CI: 95% confidence interval. Correlations with strong evidence of being non-zero are in bold. Individuals from the negatively-colored personality cluster are presented in gray, those from the positively-colored personality cluster are in black.</figcaption></figure>
<table><caption>Table A.1: Contrast estimates and 95% Confidence Intervals of the personality characteristics between the negatively-colored and the positively clusters.</caption><thead><tr><th>Personality Characteristics</th><th>Estimate</th><th>95% CI</th></tr></thead><tbody><tr><td>Openness</td><td>-0.16</td><td>[-0.38, +0.07]</td></tr><tr><td>Conscientiousness</td><td>-0.32</td><td>[-0.57, -0.06]</td></tr><tr><td>Extroversion</td><td>-1.05</td><td>[-1.32, -0.77]</td></tr><tr><td>Agreeableness</td><td>-0.53</td><td>[-0.73, -0.33]</td></tr><tr><td>Neuroticism</td><td>+0.98</td><td>[+0.72, +1.24]</td></tr><tr><td>Social Anxiety</td><td>+27.66</td><td>[+21.24, +33.99]</td></tr></tbody></table>

Note: Positive estimates indicate that the median of the negatively-colored personality cluster is greater than that of the positively-colored personality cluster. Contrasts are credible if the 95% CI does not include 0 (in bold).
B Main effects of emotion on SDT parameters

<table><caption>Table B.2: Median Estimates and 95% Confidence Intervals for Signal Detection Theory Parameters (d' and c) Contrasts Across Emotions Median estimates and 95% Confidence Intervals of the Signal Detection Theory parameters (d' and c) contrasts between emotions.</caption><thead><tr><th>Parameter</th><th>Contrast</th><th>Estimate</th><th>95% CI</th></tr></thead><tbody><tr><td>d'</td><td>Anger - Disgust</td><td>+0.82</td><td>[+0.73, +0.91]</td></tr><tr><td>d'</td><td>Anger - Fear</td><td>+0.73</td><td>[+0.64, +0.83]</td></tr><tr><td>d'</td><td>Anger - Happiness</td><td>-0.27</td><td>[-0.37, -0.18]</td></tr><tr><td>d'</td><td>Disgust - Fear</td><td>-0.09</td><td>[-0.18, +0.00]</td></tr><tr><td>d'</td><td>Disgust - Happiness</td><td>-1.09</td><td>[-1.18, -0.99]</td></tr><tr><td>d'</td><td>Fear - Happiness</td><td>-1.01</td><td>[-1.1, -0.91]</td></tr><tr><td>c</td><td>Anger - Disgust</td><td>-0.19</td><td>[-0.27, -0.12]</td></tr><tr><td>c</td><td>Anger - Fear</td><td>-0.26</td><td>[-0.33, -0.18]</td></tr><tr><td>c</td><td>Anger - Happiness</td><td>+0.26</td><td>[+0.19, +0.34]</td></tr><tr><td>c</td><td>Disgust - Fear</td><td>-0.07</td><td>[-0.14, +0.00]</td></tr><tr><td>c</td><td>Disgust - Happiness</td><td>+0.46</td><td>[+0.38, +0.53]</td></tr><tr><td>c</td><td>Fear - Happiness</td><td>+0.52</td><td>[+0.46, +0.60]</td></tr></tbody></table>

Note: Contrasts are credible if the 95% CI exclude 0 (in bold).
C Statistical models

For each model, the interaction and additive terms are illustrated by the symbols ‘:’ and ‘+’ respectively. Random effects, taking into account interindividual variability and intertrial variability (when applicable) are represented by (1|Subject) and (1|Trial) respectively. When applicable, we controlled for the gender (male, female) of the stimulus, but we did not test it as we did not formulate hypothesis on this variable. Through the different models, the categorical variables emotion (anger, disgust, fear, happiness), intensity (low, medium, high), AOI (eye, nose, mouth) and gender are within-subject whereas the variable cluster (other-oriented, self-oriented) is a between-subject variable.

Models for cluster comparison between the different personality
characteristics

Equation C.1 represents the model applied to each personality characteristic assessed in the experiment. We specified a normal (gaussian) distribution for the dimensions openness to experience, conscientiousness, extraversion and social anxiety and a skew normal distribution (due to the asymmetry of the distribution of the data) for the agreeableness and neuroticism dimensions.

$$
\textit{Personality characteristic} \sim \textit{Cluster} \qquad (C.1)
$$
## Models for fixation proportions.

Equation C.2 represents the model used for the analysis of fixation proportions of the neutral trials (S1 and S2) and S1 of the emotional trials, taking into account the effect of area of interest (AOI) and cluster and their interaction while controlling for the gender of the stimulus. Equation C.3 corresponds to the model used for the analysis of S2 of the emotional trials, taking into account the AOI, the emotion and intensity of the stimulus and the cluster; their interaction and controlling for the effect of the stimulus' gender.

$$ 
\begin{aligned}
\text{Fixation proportion} \sim & \text{AOI} + \text{Cluster} + \text{Gender} + \text{AOI} : \text{Cluster} \\
& + (1|\text{Trial}) + (1|\text{Subject})
\end{aligned}
\qquad (C.2)
$$

$$ 
\begin{aligned}
\text{Fixation proportion} \sim & \text{AOI} + \text{Emotion} + \text{Intensity} + \text{Cluster} + \text{Gender} \\
& + \text{AOI} : \text{Emotion} : \text{Intensity} : \text{Cluster} \\
& + (1|\text{Trial}) + (1|\text{Subject})
\end{aligned}
\qquad (C.3)
$$

## Models for the growth curve analysis.

As described in the main text, for the growth curve analysis, we applied one model per AOI (eye, nose, mouth) and limited our analysis to S2 of the emotional trials displayed at low intensity. Orthogonal polynomials (continuous variables) are labelled *ot1*,..., *ot4* in the models and correspond to polynomials of the first to the fourth order. The random structure includes random slopes for each trial for each participant on each time term and their interaction. For each model, we specified a beta distribution (zero one inflated beta
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Fixation proportion ~ Emotion + Cluster + Gender

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$$+ (ot1 + ot2 + ot3 + ot4) \qquad (C.4)$$

+ (ot1 + ot2 + ot3 + ot4|Subject)

+ (ot1 + ot2 + ot3 + ot4|Subject : Trial)

## Model for the categorization of the neutral stimuli.

Regarding the categorization of the neutral stimuli, we applied a beta distribution ("zero one inflated beta distribution", allowing the integration of values equal to 0 and 1 to the fit).

Proportion of response ~ Emotion + Cluster

$$+ Emotion : Cluster + (1|Subject) \qquad (C.5)$$

## Models for signal detection theory parameters

We fitted the same models to test the effect of personality and emotion on *d'* and *c* parameters and applied a normal distribution to the fits:

$$d' \sim Emotion + Cluster + Emotion : Cluster + (1|Subject) \qquad (C.6)$$

$$c \sim Emotion + Cluster + Emotion : Cluster + (1|Subject) \qquad (C.7)$$
D Main effects of AOI on percentage of fixation time

<table><caption>Table D.3: Median estimates of the contrast and 95% Confidence Intervals of the percentage of fixation time as a function of trial (Neutral and Emotional) and exposure phase (S1 and S2).</caption><thead><tr><th>Trial</th><th>Phase</th><th>Contrast</th><th>Estimate</th><th>95% CI</th></tr></thead><tbody><tr><td>Neutral</td><td>S1</td><td>Eye - Nose</td><td>+13.95</td><td>[+12.66, +15.19]</td></tr><tr><td>Neutral</td><td>S1</td><td>Eye - Mouth</td><td>+13.60</td><td>[+11.79, +15.60]</td></tr><tr><td>Neutral</td><td>S1</td><td>Nose - Mouth</td><td>-0.34</td><td>[-2.15, +1.50]</td></tr><tr><td>Neutral</td><td>S2</td><td>Eye - Nose</td><td>+4.72</td><td>[+3.67, +6.04]</td></tr><tr><td>Neutral</td><td>S2</td><td>Eye - Mouth</td><td>+5.16</td><td>[+3.83, +6.60]</td></tr><tr><td>Neutral</td><td>S2</td><td>Nose - Mouth</td><td>+0.44</td><td>[-0.87, +1.80]</td></tr><tr><td>Emotion</td><td>S1</td><td>Eye - Nose</td><td>+15.45</td><td>[+15.06, +15.85]</td></tr><tr><td>Emotion</td><td>S1</td><td>Eye - Mouth</td><td>+16.25</td><td>[+15.62, +16.87]</td></tr><tr><td>Emotion</td><td>S1</td><td>Nose - Mouth</td><td>+0.80</td><td>[+0.18, +1.38]</td></tr></tbody></table>

Note: Positive: positively-colored personality, Negative: negatively-colored personality. Contrasts are credible if the 95% CI does not include 0 (in bold).