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Yep, R.

Publications and source records attributed to Yep, R..

2 recordsLinked to original sources

Learning to blink strategically is crucial to performance in a predictable saccade task and varies across the lifespan

Humans blink their eyes 16-20 times each minute to spread tear film on the cornea, representing a substantial amount of waking time when ones eyes are closed. These spontaneous blinks are strategically timed to prioritize the processing of important visual input, balancing both stimulus characteristics and personal goals. Until now, the learning process underlying optimal blink timing has not been investigated in detail. Here, we present video-based eye-tracking data from 703 healthy participants (aged 5-91 years, 470 female) performing a structured interleaved pro-/anti-saccade task, in which we previously found that blink suppression occurs in anticipation of visual stimulus appearance (Pitigoi et al., 2024). Our goals are to understand (1) how participants modify their blink timing according to the temporal contingencies of the task; (2) whether the capacity to optimize blink timing impacts performance; and (3) whether this pattern varies with age. We found that participants quickly and strategically modified their blink distribution to optimize task performance. Blink probability decreased in periods that would compromise anti-saccade execution and increased when visual input was less critical. We also found significant differences in blink patterns and adaptive ability based on cognitive control capacity (indicated by participants anti-saccade error rates). Furthermore, we demonstrated that blink optimization improves gradually from childhood to early adulthood, before declining with advanced age. This supports a possible link between regulation of blink behavior and age-related changes in learning capacity and inhibitory control across the lifespan.

neuroscience↗

Automated Analysis Pipeline for Extracting Saccade, Pupil, and Blink Parameters Using Video-Based Eye Tracking

The tremendous increase of video-based eye tracking has made it possible to collect data from thousands of participants. Traditional manual detection and classification procedures for saccades and trial categorization is not viable for the large data sets being collected. Additionally, high-speed video-based eye trackers now allow for the novel analysis of pupil responses and blink behavior. Here we present a detailed description of our pipeline for collecting data, storing data, organizing participant codes, and cleaning data, which are fairly lab-specific but nonetheless important precursory steps in establishing standardized pipelines. More importantly, we also include descriptions of the automated detection and classification of saccades, blinks, blincades (blinks occurring during saccades), and boomerang saccades (two saccades in opposite directions that occur almost simultaneously so that speed-based algorithms fail to split them), which are almost entirely task-agnostic and can be used on a wide variety of data. We additionally describe novel findings about post-saccadic oscillations, and provide a method to get more accurate estimates for end-points of saccades. Lastly, we describe the automated behavior classification for the Interleaved Pro- and Anti-Saccade Task (IPAST), a well-known task that probes voluntary and inhibitory control. This pipeline was evaluated using data collected from 592 human participants between 5 and 93 years of age, making it robust enough to handle large clinical patient data sets as well. In sum, this pipeline has been optimized to consistently handle large data sets obtained from diverse study cohorts (i.e., development, aging, clinical), and collected across multiple laboratory sites.

neuroscience↗