Literature DB >> 33078381

Spatial suppression due to statistical learning tracks the estimated spatial probability.

Rongqi Lin1, Xinyu Li1, Benchi Wang2,3,4,5, Jan Theeuwes6,7.   

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.

Entities:  

Keywords:  Attentional capture; Statistical learning; Suppression

Mesh:

Year:  2020        PMID: 33078381     DOI: 10.3758/s13414-020-02156-2

Source DB:  PubMed          Journal:  Atten Percept Psychophys        ISSN: 1943-3921            Impact factor:   2.199


  22 in total

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Authors:  Jillian H Fecteau; Douglas P Munoz
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Authors:  Jillian H Fecteau; Ilia Korjoukov; Pieter R Roelfsema
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3.  Reward modulates oculomotor competition between differently valued stimuli.

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Review 4.  How motivation and reward learning modulate selective attention.

Authors:  A Bourgeois; L Chelazzi; P Vuilleumier
Journal:  Prog Brain Res       Date:  2016-07-12       Impact factor: 2.453

Review 5.  Rewards teach visual selective attention.

Authors:  Leonardo Chelazzi; Andrea Perlato; Elisa Santandrea; Chiara Della Libera
Journal:  Vision Res       Date:  2012-12-19       Impact factor: 1.886

6.  Altering spatial priority maps via statistical learning of target selection and distractor filtering.

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Journal:  Cortex       Date:  2017-10-09       Impact factor: 4.027

Review 7.  Attention, intention, and priority in the parietal lobe.

Authors:  James W Bisley; Michael E Goldberg
Journal:  Annu Rev Neurosci       Date:  2010       Impact factor: 12.449

Review 8.  Top-down versus bottom-up attentional control: a failed theoretical dichotomy.

Authors:  Edward Awh; Artem V Belopolsky; Jan Theeuwes
Journal:  Trends Cogn Sci       Date:  2012-07-12       Impact factor: 20.229

9.  More capture, more suppression: Distractor suppression due to statistical regularities is determined by the magnitude of attentional capture.

Authors:  Michel Failing; Jan Theeuwes
Journal:  Psychon Bull Rev       Date:  2020-02

Review 10.  Selection history: How reward modulates selectivity of visual attention.

Authors:  Michel Failing; Jan Theeuwes
Journal:  Psychon Bull Rev       Date:  2018-04
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  2 in total

1.  Statistical distractor learning modulates perceptual sensitivity.

Authors:  Dirk van Moorselaar; Jan Theeuwes
Journal:  J Vis       Date:  2021-11-01       Impact factor: 2.240

2.  Spatial enhancement due to statistical learning tracks the estimated spatial probability.

Authors:  Yuanyuan Zhang; Yihan Yang; Benchi Wang; Jan Theeuwes
Journal:  Atten Percept Psychophys       Date:  2022-04-14       Impact factor: 2.157

  2 in total

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