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Poster A28

Temporal predictions dynamically modulate attentional capture by expected target features during visual search

Poster Session A - Saturday, April 13, 2024, 2:30 – 4:30 pm EDT, Sheraton Hall ABC

Gwenllian C. Williams1 (, Sage E. P. Boettcher1, Anna C. Nobre1,2; 1University of Oxford, 2Yale University

We use temporal regularities in everyday environments to predict when relevant events will occur. Such temporal predictions may help us to find targets within busy, dynamic scenes. Consider searching for multiple visually distinct targets in a continuously changing environment, like finding friends in a crowded street. If each target appears with predictable timing, then selectively guiding attention in a time-dependent manner towards features of the anticipated target is more efficient than continuously attending to features of all targets equally. However, it remains unclear if we can exploit temporal regularities to dynamically shift feature-based attention. We investigated this across two experiments using a novel dynamic visual-search task. Participants searched continuously evolving displays for two transiently appearing targets amongst distractors. Stimuli were colour-shape combinations. Targets shared neither shape nor colour; distractors shared zero or one feature with each target. Critically, targets appeared at one of two times during trials. Although exact timings varied across experiments, one target always most likely appeared earlier (early target) and the other later (late target). Targets were unpredictable in spatial location. Participants’ performances were consistent with having utilised the temporal regularities. At each appearance time, participants were significantly more likely to identify the temporally-expected target than the temporally-unexpected target. Further, eye-tracking measures indicated that participants’ attention was captured more by distractors sharing their colour with the early target than those sharing their colour with the late target, only for distractors appearing early during trials. This work provides initial evidence for temporal predictions guiding feature-based attention during search.

Topic Area: ATTENTION: Nonspatial


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April 13–16  |  2024