Moher Jeff, Leber Andrew B
Psychology Department, Connecticut College, 270 Mohegan Avenue, New London, CT, 06320, USA.
Department of Psychology, The Ohio State University, 1835 Neil Ave, Columbus, OH, 43210, USA.
Atten Percept Psychophys. 2025 Feb;87(2):303-315. doi: 10.3758/s13414-024-03004-3. Epub 2025 Jan 10.
Our attention can sometimes be disrupted by salient but irrelevant objects in the environment. This distractor interference can be reduced when distractors appear frequently, allowing us to anticipate their presence. However, it remains unknown whether distractor frequency can be learned implicitly across distinct contexts. In other words, can we implicitly learn that in certain situations a distractor is more likely to appear, and use that knowledge to minimize the impact that the distractor has on our behavior? In two experiments, we explored this question by asking participants to find a unique shape target in displays that could contain a color singleton distractor. Forest or city backgrounds were presented on each trial, and unbeknownst to the participants, each image category was associated with a different distractor probability. We found that distractor interference was reduced when the image predicted a high rather than low probability of distractor presence on the upcoming trial, even though the location and (in Experiment 2) the color of the distractor was completely unpredictable. These effects appear to be driven by implicit rather explicit learning. We conclude that implicit learning of context-specific distractor probabilities can drive flexible strategies for the reduction of distractor interference.
我们的注意力有时会被环境中突出但不相关的物体所干扰。当干扰物频繁出现时，这种干扰物干扰可以减少，从而使我们能够预期它们的出现。然而，尚不清楚干扰物频率是否可以在不同情境中被隐性学习。换句话说，我们能否隐性地了解到在某些情况下干扰物更有可能出现，并利用这一知识将干扰物对我们行为的影响降至最低？在两项实验中，我们通过要求参与者在可能包含颜色单一干扰物的展示中找到独特形状的目标来探究这个问题。每次试验都会呈现森林或城市背景，而参与者并不知道，每个图像类别都与不同的干扰物出现概率相关联。我们发现，当图像预测即将到来的试验中干扰物出现的概率高而不是低时，干扰物干扰会减少，即使干扰物的位置和（在实验2中）颜色是完全不可预测的。这些效应似乎是由隐性而非显性学习驱动的。我们得出结论，对特定情境下干扰物概率的隐性学习可以推动灵活的策略来减少干扰物干扰。
