September 2018
Volume 18, Issue 10
Open Access
Vision Sciences Society Annual Meeting Abstract  |   September 2018
Using frequency tagging to study the effect of category learning on visual attention to object parts
Author Affiliations
  • Yue Meng
    Psychology department, Florida State University
  • Jonathan Folstein
    Psychology department, Florida State University
Journal of Vision September 2018, Vol.18, 399. doi:
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      Yue Meng, Jonathan Folstein; Using frequency tagging to study the effect of category learning on visual attention to object parts. Journal of Vision 2018;18(10):399.

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

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Subordinate-level category learning is known to cause perceptual improvements for learned stimuli outside of the category learning task. These improvements could be caused by improvements in visual attention or improvements in the feed-forward visual signal. Category learning tasks can cause changes in allocation of visual attention, specifically the ability to select diagnostic features in learned stimuli. On the other hand, object-based attention predicts that attention automatically spreads onto the whole object. Here we used frequency-tagging to examine how visual attention changes after training on categorization. Participants were trained over six sessions to categorize cartoon space plant stimuli based on 3 out of 6 features. In the following Steady-state EEG session, diagnostic features and nondiagnostic features were frequency-tagged. In each trial, participants were cued to monitor either diagnostic features or nondiagnostic features of an exemplar to detect the onset of a small red dot, they performed the same task on another set of untrained stimuli as well. Finally, participants completed a behavioral task in which they reported the number of perceived different features between 2 sequentially presented stimuli on both the trained and untrained stimulus set, stimuli were presented upright in half of all trials and inverted for the rest. We found an attention effect for both the diagnostic and nondiagnostic features in EEG data. In the feature counting task, participants had better differentiation performance for the trained set than the untrained set, an inversion effect was observed for the trained set.

Meeting abstract presented at VSS 2018


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