Thermodynamics and kinetics of sulfide oxidation by oxygen: a look at inorganically controlled reactions and

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 eyescanning 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 eyeposition 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 eyetracking 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 gazebased models from computer vision to clinical psychology. Introduction Our eyes constantly move around to place our highresolution fovea on the most relevant visual information. Arguably, one of the most important objects of regard is another person's face. Until recently, a majority of face perception studies have been pointing to a ''universal'' face exploration pattern: Humans systematically follow a triangular scanpath (sequence of fixations) over the eyes and the mouth of the presented face Methods and results Experiment This data set has been described and used in a pupillometry study Participants We recorded the gaze of 459 visitors to the Science Museum of London, UK. We removed from the analysis the data of participants under age 18 (n ¼ 8) as well as 46 other participants whose eye data exhibited some irregularities (loss of signal, obviously shifted positions). The analyses are performed on a final group of 405 participants (203 males, 202 females). Mean age of participants 30.8 years (SD ¼ 11.5; males: M ¼ 32.3, SD ¼ 12.3; females: M ¼ 29.3, SD ¼ 10.5). The experiment was approved by the UCL research ethics committee and by the London Science Museum ethics board, and the methods were carried out in accordance with the approved guidelines. Signed informed consent was obtained from all participants. Stimuli Stimuli consisted of video clips of eight different actors (four females, four males, see Apparatus The experimental setup consisted of four computers: two for administering the personality questionnaire and two dedicated to the eye-tracking experiment and actor face-rating questionnaire (see Procedure). Each setup consisted of a stimulus presentation PC (Dell precision T3500 and Dell precision T3610) hooked up to a 19-in. LCD monitor (both 1280 3 1024 pixels, 49.98 3 39.98 of visual angle) at 60 Hz and an EyeLink 1000 kit (http:// www.sr-research.com/). Eye-tracking data was collected at 250 Hz. Participants sat 57 cm from the monitor, their head stabilized with a chin rest, forehead rest, and headband. A protective opaque white screen encased the monitor and part of the participant's head in order to shield the participant from environmental distractions. Procedure The study took place at the Live Science Pod in the Who Am I? exhibition of the London Science Museum. Journal of Vision (2016) 16(14):16, 1-19 Coutrot et al. 2 Downloaded from jov.arvojournals.org on 07/01/2019 The room had no windows, and the ambient luminance was very stable across the experiment. It consisted of three phases for a total duration of approximately 15 min. Phase 1 was a 10-item personality questionnaire based on the Big Five personality inventory The dispersion is the mean Euclidian distance between the eye positions of the same observers for a given clip. Small dispersion values reflect clustered eye positions. Scanpath modeling Hidden Markov models To grasp the highly dynamic and individualistic components of gaze behavior, we model participant's scanpaths using hidden Markov models (HMMs;