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Post-capture processes contribute to statistical learning of distractor locations in visual search.

Research paper by Marian M Sauter, Nina M NM Hanning, Heinrich R HR Liesefeld, Hermann J HJ Müller

Indexed on: 30 Dec '20Published on: 29 Dec '20Published in: Cortex



Abstract

People can learn to ignore salient distractors that occur frequently at particular locations, making them interfere less with task performance. This effect has been attributed to learnt suppression of the likely distractor locations at a pre-selective stage of attentional-priority computation. However, rather than distractors at frequent (vs rare) locations being just less likely to capture attention, attention may possibly also be disengaged faster from such distractors - a post-selective contribution to their reduced interference. Eye-movement studies confirm that learnt suppression, evidenced by a reduced rate of oculomotor capture by distractors at frequent locations, is a major factor, whereas the evidence is mixed with regard to a role of rapid disengagement However, methodological choices in these studies limited conclusions as to the contribution of a post-capture effect. Using an adjusted design, here we positively establish the rapid-disengagement effect, while corroborating the oculomotor-capture effect. Moreover, we examine distractor-location learning effects not only for distractors defined in a different visual dimension to the search target, but also for distractors defined within the same dimension, which are known to cause particularly strong interference and probability-cueing effects. Here, we show that both oculomotor-capture and disengagement dynamics contribute to this pattern. Additionally, on distractor-absent trials, the slowed responses to targets at frequent distractor locations-that we observe only in same-, but not different-, dimension conditions-arise pre-selectively, in prolonged latencies of the very first saccade. This supports the idea that learnt suppression is implemented at a different level of priority computation with same-versus different-dimension distractors. Copyright © 2020 Elsevier Ltd. All rights reserved.