Face exploration dynamics differentiate men and women

Abstract

The human face is central to our everyday social interactions. Recent studies have shown that while gazing at faces, each one of us has a particular eye-scanning pattern, highly stable across time. Although variables such as culture or personality have been shown to modulate gaze behavior, we still don't know what shapes these idiosyncrasies. Moreover, most previous observations rely on static analyses of small-sized eye-position data sets averaged across time. Here, we probe the temporal dynamics of gaze to explore what information can be extracted about the observers and what is being observed. Controlling for any stimuli effect, we demonstrate that among many individual characteristics, the gender of both the participant (gazer) and the person being observed (actor) are the factors that most influence gaze patterns during face exploration. We record and exploit the largest set of eye-tracking data (405 participants, 58 nationalities) from participants watching videos of another person. Using novel data-mining techniques, we show that female gazers follow a much more exploratory scanning strategy than males. Moreover, female gazers watching female actresses look more at the eye on the left side. These results have strong implications in every field using gaze-based models from computer vision to clinical psychology.

Predicting artificial visual field losses: A gaze-based inference study

“…Indeed, cognitive processes can be observed through eye movements and offer a wealth of information related to internal processes (Itti, 2015;Coutrot, Hsiao, & Chan, 2018). Inference from gaze data consists in deducing subjective characteristics solely from ocular data, such as age (Le Meur et al, 2017b), gender (Coutrot, Binetti, Harrison, Mareschal, & Johnston, 2016;Sammaknejad, Pouretemad, Eslahchi, Salahirad, & Alinejad, 2017), mental states and traits (Liao, Zhang, Zhu, & Ji, 2005;Hoppe, Loetscher, Morey, & Bulling, 2015;Yamada & Kobayashi, 2017;Hoppe, Loetscher, Morey, & Bulling, 2018), expertise and skill proficiency (Eivazi & Bednarik, 2011;Boccignone, Ferraro, Crespi, Robino, & de'Sperati, 2014;Tien et al, 2014;Kolodziej, Majkowski, Francuz, Rak, & Augustynowicz, 2018), and neurological disorders (Kupas, Harangi, Czifra, & Andrassy, 2017;Terao, Fukuda, & Hikosaka, 2017).It has proven useful in identifying autism spectrum disorder (Pierce et al, 2016), fetal alcohol spectrum disorder (Tseng, Paolozza, Munoz, Reynolds, & Itti, 2013), dementia (Zhang et al, 2016;Beltrán, García-Vázquez, Benois-Pineau, Gutierrez-Robledo, & Dartigues, 2018), dyslexia (Benfatto et al, 2016), anxiety (Abbott, Shirali, Haws, & Lack, 2017), mental fatigue (Yamada & Kobayashi, 2017), and other disorders. It has also been applied to task detection (Borji & Itti, 2014;Haji-Abolhassani & Clark, 2014;Kanan, Ray, Bseiso, Hsiao, & Cottrell, 2014;Boisvert & Bruce, 2016).…”

“…We selected these two types of models for their dissimilarities as classifiers, which may lead us to learn separate information about our experimental data. Markov models have shown their effectiveness when applied to gaze data (e.g., Simola, Salojärvi, & Kojo, 2008;Kanan, Bseiso, Ray, Hsiao, & Cottrell, 2015;Coutrot et al, 2016;Rai et al, 2016;Sammaknejad et al, 2017). They have been extensively used for modeling time series in general (Camastra & Vinciarelli, 2008).…”

Recognizing images: The role of motivational significance, complexity, social content, age, and gender

“…This mechanism seems unlikely to explain the present findings because we statistically accounted for the influence of verbal fluency. An alternative explanation for the observed gender effect is that women exhibit more explorative eye-gaze behavior than men (Coutrot, Binetti, Harrison, Mareschal & Johnston, 2016;Mercer Moss, Baddeley & Canagarajah, 2012;Rennels & Cummings, 2013;Shen & Itti, 2012). Relatedly, according to the selectivity hypothesis of information processing females are comprehensive processors engaging in more detailed elaboration of the available information in apprehending environmental stimuli as basis for judgement, whereas males are selective processors considering only a subset of the available information (Darley & Smith, 1995;Meyers-Levy, 1989).…”

Fear Generalization as Threat Prediction: Adaptive Changes in Facial Exploration Strategies revealed by Fixation-Pattern Similarity Analysis

“…For example, classifier-based methods can successfully decode the identity of different observers [31,33], or task-dependent changes in eye-movements strategies within the same observers [27,28,30,31,52]. Furthermore, they might contribute to improve mental health screening when used as behavioral biomarkers [25,53].…”