December 2022
Volume 22, Issue 14
Open Access
Vision Sciences Society Annual Meeting Abstract  |   December 2022
Stability of individual differences in distractor suppression driven by statistical learning
Author Affiliations
  • Yavor Ivanov
    Vrije Universiteit Amsterdam
  • Jan Theeuwes
    Vrije Universiteit Amsterdam
  • Louisa Bogaerts
    Vrije Universiteit Amsterdam
    Ghent University
Journal of Vision December 2022, Vol.22, 3917. doi:
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      Yavor Ivanov, Jan Theeuwes, Louisa Bogaerts; Stability of individual differences in distractor suppression driven by statistical learning. Journal of Vision 2022;22(14):3917.

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      © ARVO (1962-2015); The Authors (2016-present)

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Statistical learning has been implicated in various domains of cognition such as language, motor learning, scene perception, etc. More recently, its role in attentional selection has become a popular topic. A growing number of group-level studies have shown that attentional capture by distracting stimuli is reduced if they are more frequently presented at a particular location during a visual search task (i.e. learned distractor suppression). Given recent reports of large variance in statistical learning abilities, we were interested in studying the modulation of attention by distractor-location regularities at the individual level. Are there meaningful differences in the extent to which different peoples’ attention is (1) captured by distracting stimuli and (2) modulated by statistical regularities? In a large-scale online study employing the additional singleton paradigm, we investigated the reliability of attentional capture and learned distractor suppression. We employed the split-half and test-retest methods (with sessions 2-months apart) to assess reliability. We found that the individual-level measures of capture and suppression were moderately reliable and stable between sessions. At the same time the psychometric properties of the existing measures may not yet be sufficient for the study of individual differences. We discuss the implications for future research into distractor suppression and attentional capture targeting individual performance.


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