Spatial suppression due to statistical learning tracks the estimated spatial probability
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Spatial suppression due to statistical learning tracks the estimated spatial probability Rongqi Lin 1
&
Xinyu Li 1 & Benchi Wang 2,3,4,5 & Jan Theeuwes 6,7
Accepted: 15 September 2020 # The Psychonomic Society, Inc. 2020
Abstract People are sensitive to regularities in the environment. Recent studies employing the additional singleton paradigm showed that a singleton distractor that appeared more often in one specific location than in all other locations may lead to attentional suppression of high-probability distractor locations. This in turn effectively reduced the attentional capture effect by the salient distractor singleton. However, in basically all of these previous studies, the probability that the salient distractor was presented at this specific location was relatively high (i.e., 65%; or a ratio of 13:1 between high- and low-probability locations). The question we addressed here was whether participants still can learn the regularities in the display even when these regularities are quite subtle. We systematically manipulated the ratio of the distractor appearing at the high- and low-probability location from 2:1 to 8:1. We asked the question whether the suppression effect would depend on the probabilities of the distractor appearing in the high-probability location. The results showed that the suppression of the high-probability location was linearly related to the high-low-probability ratio. In other words, the more evidence that a distractor appears more often at a particular location, the stronger the suppression. This indicates that the distribution of attention is optimally adapted to the statistical regularities present in the display. Keywords Attentional capture . Suppression . Statistical learning
Introduction Spatial attention is an important mechanism for visual selection. Previous research that focused on the target’s probability learning showed that in visual search, spatial attention would drift to those locations in the search array where the target was displayed with higher probability relative to other locations (Jiang, Swallow, Rosenbaum, & Herzig, 2013; Jiang, Swallow, Won, Cistera, & Rosenbaum, 2014). In addition, locations that are likely to contain distractors are suppressed
relative to other locations such that they compete less for attentional resources (Ferrante et al., 2018; Wang & Theeuwes, 2018a, b, c). It is generally assumed through statistical learning (SL) that people learn the statistical regularities regarding target and distractor probabilities biasing attentional selection. SL is defined as the ability to extract events that co-occur in our environment and to utilize this learned co-variance to deploy our attentional resources implicitly in an efficient manner (Schapiro & Turk-Browne, 2015). It is assumed that through SL the weights within the spatial priority map are adjusted
Electronic supplementary material The online version of this article (https://doi.org/10.3758/s13414-020-02156-2) contains supplementary material, which is available to authorized users.
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