Dataset Information

PyTrack: An end-to-end analysis toolkit for eye tracking.

ABSTRACT: Eye tracking is a widely used tool for behavioral research in the field of psychology. With technological advancement, we now have specialized eye-tracking devices that offer high sampling rates, up to 2000 Hz, and allow for measuring eye movements with high accuracy. They also offer high spatial resolution, which enables the recording of very small movements, like drifts and microsaccades. Features and parameters of interest that characterize eye movements need to be algorithmically extracted from raw data as most eye trackers identify only basic parameters, such as blinks, fixations, and saccades. Eye-tracking experiments may investigate eye movement behavior in different groups of participants and in varying stimuli conditions. Hence, the analysis stage of such experiments typically involves two phases, (i) extraction of parameters of interest and (ii) statistical analysis between different participants or stimuli conditions using these parameters. Furthermore, the datasets collected in these experiments are usually very large in size, owing to the high temporal resolution of the eye trackers, and hence would benefit from an automated analysis toolkit. In this work, we present PyTrack, an end-to-end open-source solution for the analysis and visualization of eye-tracking data. It can be used to extract parameters of interest, generate and visualize a variety of gaze plots from raw eye-tracking data, and conduct statistical analysis between stimuli conditions and subject groups.

SUBMITTER: Ghose U

PROVIDER: S-EPMC7725757 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature

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PyTrack: An end-to-end analysis toolkit for eye tracking.

Ghose Upamanyu U Srinivasan Arvind A AA Boyce W Paul WP Xu Hong H Chng Eng Siong ES

Behavior research methods 20201201 6

Eye tracking is a widely used tool for behavioral research in the field of psychology. With technological advancement, we now have specialized eye-tracking devices that offer high sampling rates, up to 2000 Hz, and allow for measuring eye movements with high accuracy. They also offer high spatial resolution, which enables the recording of very small movements, like drifts and microsaccades. Features and parameters of interest that characterize eye movements need to be algorithmically extracted f ...[more]

PMID: 32500364

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Project description:Background:Visual atypicalities in autism spectrum disorder (ASD) are a well documented phenomenon, beginning as early as 2-6 months of age and manifesting in a significantly decreased attention to the eyes, direct gaze and socially salient information. Early emerging neurobiological deficits in perceiving social stimuli as rewarding or its active avoidance due to the anxiety it entails have been widely purported as potential reasons for this atypicality. Parallel research evidence also points to the significant benefits of animal presence for reducing social anxiety and enhancing social interaction in children with autism. While atypicality in social attention in ASD has been widely substantiated, whether this atypicality persists equally across species types or is confined to humans has not been a key focus of research insofar. Methods:We attempted a comprehensive examination of the differences in visual attention to static images of human and animal faces (40 images; 20 human faces and 20 animal faces) among children with ASD using an eye tracking paradigm. 44 children (ASD n = 21; TD n = 23) participated in the study (10,362 valid observations) across five regions of interest (left eye, right eye, eye region, face and screen). Results:Results obtained revealed significantly greater social attention across human and animal stimuli in typical controls when compared to children with ASD. However in children with ASD, a significantly greater attention allocation was seen to animal faces and eye region and lesser attention to the animal mouth when compared to human faces, indicative of a clear attentional preference to socially salient regions of animal stimuli. The positive attentional bias toward animals was also seen in terms of a significantly greater visual attention to direct gaze in animal images. Conclusion:Our results suggest the possibility that atypicalities in social attention in ASD may not be uniform across species. It adds to the current neural and biomarker evidence base of the potentially greater social reward processing and lesser social anxiety underlying animal stimuli as compared to human stimuli in children with ASD.

Dataset Information

PyTrack: An end-to-end analysis toolkit for eye tracking.

Publications

PyTrack: An end-to-end analysis toolkit for eye tracking.

Similar Datasets

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