September 2019
Volume 19, Issue 10
Open Access
Vision Sciences Society Annual Meeting Abstract  |   September 2019
Distinct pupil features correlate with between-participant and across-session performance variability in a 16-week, longitudinal data set
Author Affiliations & Notes
  • Russell A Cohen Hoffing
    Human and Research Engineering Directorate, US Army Research Laboratory
  • Steven M Thurman
    Human and Research Engineering Directorate, US Army Research Laboratory
  • Nina Lauharatanahirun
    Human and Research Engineering Directorate, US Army Research Laboratory
    Annenberg School of Communication, University of Pennsylvania
  • Daniel E Forster
    Human and Research Engineering Directorate, US Army Research Laboratory
  • Javier O Garcia
    Human and Research Engineering Directorate, US Army Research Laboratory
    Department of Biomedical Engineering, University of Pennsylvania
  • Nick Wasylyshyn
    Human and Research Engineering Directorate, US Army Research Laboratory
  • Barry Gies-brecht
    Department of Psychological and Brain Sciences, University of California, Santa Barbara
  • Scott T Grafton
    Department of Psychological and Brain Sciences, University of California, Santa Barbara
  • Jean M Vettel
    Human and Research Engineering Directorate, US Army Research Laboratory
    Department of Psychological and Brain Sciences, University of California, Santa Barbara
    Department of Biomedical Engineering, University of Pennsylvania
Journal of Vision September 2019, Vol.19, 126c. doi:https://doi.org/10.1167/19.10.126c
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      Russell A Cohen Hoffing, Steven M Thurman, Nina Lauharatanahirun, Daniel E Forster, Javier O Garcia, Nick Wasylyshyn, Barry Gies-brecht, Scott T Grafton, Jean M Vettel; Distinct pupil features correlate with between-participant and across-session performance variability in a 16-week, longitudinal data set. Journal of Vision 2019;19(10):126c. doi: https://doi.org/10.1167/19.10.126c.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Pupillary response provides a rich and accessible source of insight into cognitive processes due to its link with neural circuits in the brain. However, the relationships between pupil diameter and specific cognitive constructs are not yet dissociable because of the complex temporal dynamics of cognitive processes and individual variability. While extant research has identified a number of features that correlate with cognition, group-level analyses are typically used, making it unclear whether these relationships would hold between participants and upon repeated measurements. To gain insight into the robustness of pupil features and cognition, we analyzed a longitudinal pupillometry data set, characterizing relationships between standard pupil features and cognitive performance between participants (subject-level), across sessions (session-level) and within sessions (trial-level). Participants (N=26) completed 8 bi-weekly sessions of a mental arithmetic task in which participants indicated whether modular arithmetic statements were true or false under easy and difficult conditions. In the present analysis, we focused on the relationship between response speed and three pupil features typically studied in the literature: pre-stimulus baseline, peak amplitude, and peak latency. Mixed-effects model results indicated that the pre-stimulus baseline was associated with response speed at the subject-level, whereas peak amplitude was associated with the trial-level type (i.e. easy vs. difficult trials), but not trial-level performance. Peak latency was robustly associated with response speed at the group-, subject-, and trial-levels. We show that distinct pupil features correlate with performance at dissociable levels of analysis and may also reflect distinct influences of underlying cognitive processes.

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