Visual cues to fertility are in the eye (movements) of the beholder

Eye-Tracking Reveals a Role of Oxytocin in Attention Allocation Towards Familiar Faces

“…Furthermore, it should be acknowledged that there was no assessment of the menstrual cycle phase or the use of hormonal contraceptives in the female participants. Thus, we cannot rule out potential interactions of exogenous OXT with fluctuations of steroid hormones over the menstrual cycle (68)(69)(70). Future studies should therefore consider assessing the influence of OXT on the attention towards familiar faces by including participants of different age groups, with a different relationship status, and a more nuanced assessment of the female cycle stages.…”

A review of machine learning in scanpath analysis for passive gaze-based interaction

“…However, some publications focused on specific tasks such as driving cars (Lethaus et al, 2013 ), piloting plans (Peysakhovich et al, 2022 ), or reading text (Biedert et al, 2012 ; Kelton et al, 2019 ). For stimuli prediction, publications used different videos, images, or text types to study the visual behavior unique to each stimulus (Greene et al, 2012 ; Lanatà et al, 2013 ; Kanan et al, 2014 ; Coutrot et al, 2016 , 2018 ; Fuhl et al, 2019 ; Necka et al, 2019 ; Lan et al, 2020 ; Wang et al, 2020 ; Li et al, 2021 ).…”

“…We can see that 60 Hz is the most frequently used eye tracker frequency, followed by 1,000 Hz. However, most studies that reported a downsampling frequency used a 1,000 Hz eye tracker, which was then downsampled to either 500 Hz (Benson et al, 2012 ; Mishra et al, 2016 ; Necka et al, 2019 ; Kucharský et al, 2020 ) or 250 Hz (Coutrot et al, 2016 , 2018 ; George and Routray, 2016 ). Upon further investigation, we found that 60 Hz was the most common frequency across all five domains except for Psychology , which had 1,000 Hz as the most popular choice.…”

“…The second feature group contains different algorithms for quantifying the difference between pairs of scanpaths. They were used by 11 publications (Glady et al, 2013 ; Jiang et al, 2016 ; French et al, 2017 ; Shi et al, 2017 ; Castner et al, 2018 ; Król and Król, 2019 ; Li et al, 2019 ; Necka et al, 2019 ; Sáiz Manzanares et al, 2020 ; Liaqat et al, 2021 ; Appel et al, 2022 ). Across the publications, we found the following algorithms: Levenshtein Distance (Levenshtein, 1966 ), Mannan Distance (Mannan et al, 1997 ), Uniform Distance model, City Block Distance, Euclidian Distance, Hausdorff Distance, Frechett Distance, Vector-Based Comparison (Jarodzka et al, 2010 ), Dynamic Time Warping (DTW) (Müller, 2007 ), FastDTW (Salvador and Chan, 2007 ), Contrast mining (Dong and Bailey, 2012 ), Needleman-Wunsch Algorithm (Needleman and Wunsch, 1970 ), in addition to SubsMatch (Kübler et al, 2017 ) and ScanMatch (Cristino et al, 2010 ) which both use the Needleman-Wunsch Algorithm.…”

“…Emotion, or Face Perception/Recognition (n = publications) Emotion, or Face Perception/Recognition holds publications that focus on the visual behavior associated with viewing different faces or emotion-inducing stimuli to predict the recognized face or the perceived emotion. These publications explicitly used stimuli depicting different emotions, such as anger and happiness (Lanatà et al, 2013;Kanan et al, 2015;Chaby et al, 2017;Shi et al, 2017), or different people's faces (Coutrot et al, 2016;Chaby et al, 2017;Chuk et al, 2017a;Król and Król, 2019;Necka et al, 2019). This task group focused explicitly on those types of stimuli, which meant that if a publication focused, for example, on different activities to induce certain emotions, it was not included in this task group.…”