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捕获后过程有助于视觉搜索中分心物位置的统计学习。

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

机构信息

Institut für Psychologie, Universität der Bundeswehr München, Munich, Germany; Department Psychologie, Ludwig-Maximilians-Universität München, Munich, Germany.

Department Psychologie, Ludwig-Maximilians-Universität München, Munich, Germany.

出版信息

Cortex. 2021 Feb;135:108-126. doi: 10.1016/j.cortex.2020.11.016. Epub 2020 Dec 5.

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.

摘要

人们可以学习忽略经常出现在特定位置的突出干扰物,从而减少它们对任务表现的干扰。这种效应归因于在注意力优先计算的预选择阶段学习抑制可能的干扰物位置。然而,频繁(而非罕见)位置的干扰物并非仅仅不太可能吸引注意力,注意力可能也会更快地从这些干扰物中脱离——这是对它们干扰减少的后选择贡献。眼动研究证实,学习抑制是一个主要因素,表现为在频繁位置的眼动捕获中,干扰物的捕获率降低,而对于快速脱离的作用,证据则存在分歧。然而,这些研究中的方法选择限制了对后捕获效应贡献的结论。使用调整后的设计,我们在这里积极确立了快速脱离效应,同时证实了眼动捕获效应。此外,我们不仅研究了与搜索目标在不同视觉维度上定义的干扰物的位置学习效应,还研究了在同一维度上定义的干扰物的位置学习效应,后者已知会引起特别强烈的干扰和概率提示效应。在这里,我们表明,眼动捕获和脱离动力学都对这种模式有贡献。此外,在没有干扰物的试验中,我们仅在相同维度条件下观察到,频繁干扰物位置的目标反应较慢——这是在第一次眼跳的非常长的潜伏期中预先出现的,这支持了这样的观点,即学习抑制是在不同的优先级计算水平上实现的,而对于相同和不同维度的干扰物。

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