September 2024
Volume 24, Issue 10
Open Access
Vision Sciences Society Annual Meeting Abstract  |   September 2024
Learning to Suppress Color Singletons via Feature-Based Regularities
Author Affiliations
  • Brad T. Stilwell
    Texas A&M University
  • Brian A. Anderson
    Texas A&M University
Journal of Vision September 2024, Vol.24, 738. doi:https://doi.org/10.1167/jov.24.10.738
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      Brad T. Stilwell, Brian A. Anderson; Learning to Suppress Color Singletons via Feature-Based Regularities. Journal of Vision 2024;24(10):738. https://doi.org/10.1167/jov.24.10.738.

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

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Abstract

Do salient items, such as uniquely colored stimuli, automatically capture our attention? This question has been heatedly debated for decades, traditionally spurred on by two competing accounts: stimulus-driven and goal-driven. According to stimulus-driven accounts, salient items, such as color singleton distractors, have the power to automatically capture attention. In contrast, goal-driven accounts posit that only items matching the observers top-down attentional set will capture attention. This failed dichotomy misses another important source of attentional control: an observer’s previous experience or selection history. One such example of selection history is the finding that color singletons appearing in a frequent color result in less capture than singletons presented in less frequent colors. But what is the nature of this selection history? The current study varied exposure to feature-based statistical regularities to assess how they influence the control of attention. High- and low-frequency singleton distractors were presented either within blocks, to test “short-lived” regularities, such as intertrial priming, or between blocks, to test “longer-lived” regularities, such as attentional control settings. We found that capture by the high probable color was suppressed relative to capture by the low probable colors, and this learned suppression was evident for both the within- and between-block manipulations. These findings suggest that learning to suppress a distractor feature based on statistical regularities is robust and longer-lived.

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