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Nelson Roque, Walter Boot; Pupillometry as a window into the content and strength of attention sets. Journal of Vision 2018;18(10):1119. doi: 10.1167/18.10.1119.
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© ARVO (1962-2015); The Authors (2016-present)
Research has highlighted the potential of pupil size as a measure of the allocation of attention (Mathôt, van der Linden, Grainger, & Vitu, 2013), with pupil size also observed to reflexively respond to words that convey a sense of lightness or darkness (Mathôt, van der Linden, & Strijkers, 2017). If pupillary response is sensitive to mental representations associated with lightness/darkness, can pupil size serve as an observable measure of an individual's attention set during search? Further, if maintaining an attention set for a white target has a different effect on pupillary response compared to maintaining an attention set for a black target, can the size of this difference index the strength of an individual's attention set? If so, pupil response preceding target presentation should be predictive of task accuracy. Our experiment asked participants to search an RSVP stream of gray letters, and report the identity of a black or white letter. At the beginning of each trial, participants were cued to their target of search (report white or black letter) by either a visual or auditory cue (cue type was blocked and counterbalanced). Overall accuracies across cue presentation method were not significantly different (t(42) = 1.20; p = 0.24), so pupillometry measures were collapsed across cue type. Preceding the target presentation pupillary response curves for black and white target trials were created for each participant, with metrics including a difference score derived from these curves, and the maximal difference between the two functions. The maximal difference between both curves (representing attentional set modulation) was correlated with task accuracy (r = 0.23; p = 0.053), whereas the point at which the maximal difference occurred was not significantly correlated with overall accuracy (r = -0.02; p = .897). These data highlight the predictive potential of pupillometry measures to determine both the content and strength of an observer's attention set.
Meeting abstract presented at VSS 2018
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