Statistical learning of frequent distractor locations in visual search involves regional signal suppression in early visual cortex

bioRxiv (Cold Spring Harbor Laboratory)

Abstract

Abstract Observers can learn the locations where salient distractors appear frequently to reduce potential interference – an effect attributed to better suppression of distractors at frequent locations. But how distractor suppression is implemented in the visual cortex and frontoparietal attention networks remains unclear. We used fMRI and a regional distractor-location learning paradigm (Sauter et al. 2018, 2020) with two types of distractors defined in either the same (orientation) or a different (colour) dimension to the target to investigate this issue. fMRI results showed that BOLD signals in early visual cortex were significantly reduced for distractors (as well as targets) occurring at the frequent versus rare locations, mirroring behavioural patterns. This reduction was more robust with same-dimension distractors. Crucially, behavioural interference was correlated with distractor-evoked visual activity only for same- (but not different-) dimension distractors. Moreover, with different- (but not same-) dimension distractors, a colour-processing area within the fusiform gyrus was activated more when a colour distractor was present versus absent and with a distractor occurring at a rare versus frequent location. These results support statistical learning of frequent distractor locations involving regional suppression in the early visual cortex and point to differential neural mechanisms of distractor handling with different-versus same-dimension distractors.

Authors 7

1. Bei Zhang corresponding

   Ludwig-Maximilians-Universität München

   Affiliation as printed

   General and Experimental Psychology, Ludwig-Maximilians-Universität München, 80802, München, Germany

2. Ralph Weidner

   Forschungszentrum Jülich

   Affiliation as printed

   Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Research Centre Jülich, 52428, Jülich, Germany

   Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Research Centre Jülich, 548, Jülich, Germany

3. Fredrik Allenmark

   Ludwig-Maximilians-Universität München

   Affiliation as printed

   General and Experimental Psychology, Ludwig-Maximilians-Universität München, 80802, München, Germany

4. Sabine Bertleff Aachen Traffic Psychology and Acceptance Institute for Automotive Engineering

   RWTH Aachen University

   Affiliation as printed

   Traffic Psychology and Acceptance, Institute for Automotive Engineering (ika), RWTH Aachen University, 52074, Aachen, Germany

5. Gereon R. Fink

   Forschungszentrum Jülich · University of Cologne · University Hospital Cologne

   Affiliation as printed

   Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Research Centre Jülich, 52428, Jülich, Germany

   Department of Neurology, University Hospital Cologne, Cologne University, 50937, Cologne, Germany

   Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Research Centre Jülich, 548, Jülich, Germany

6. Zhuanghua Shi

   Ludwig-Maximilians-Universität München

   Affiliation as printed

   General and Experimental Psychology, Ludwig-Maximilians-Universität München, 80802, München, Germany

7. Hermann Joseph Muller

   Ludwig-Maximilians-Universität München

   Affiliation as printed

   General and Experimental Psychology, Ludwig-Maximilians-Universität München, 80802, München, Germany