September 2017
Volume 17, Issue 10
Open Access
Vision Sciences Society Annual Meeting Abstract  |   August 2017
Statistical learning of distractor suppression
Author Affiliations
  • Oscar Ferrante
    Department of Neuroscience, Biomedicine and Movement Sciences, University of Verona, Verona, Italy
  • Alessia Patacca
    Department of Neuroscience, Biomedicine and Movement Sciences, University of Verona, Verona, Italy
  • Valeria Di Caro
    Department of Neuroscience, Biomedicine and Movement Sciences, University of Verona, Verona, Italy
  • Elisa Santandrea
    Department of Neuroscience, Biomedicine and Movement Sciences, University of Verona, Verona, Italy
  • Chiara Della Libera
    Department of Neuroscience, Biomedicine and Movement Sciences, University of Verona, Verona, Italy
    Italian Institute of Neuroscience (INN), Verona, Italy
  • Leonardo Chelazzi
    Department of Neuroscience, Biomedicine and Movement Sciences, University of Verona, Verona, Italy
    Italian Institute of Neuroscience (INN), Verona, Italy
Journal of Vision August 2017, Vol.17, 674. doi:10.1167/17.10.674
  • Views
  • Share
  • Tools
    • Alerts
      ×
      This feature is available to Subscribers Only
      Sign In or Create an Account ×
    • Get Citation

      Oscar Ferrante, Alessia Patacca, Valeria Di Caro, Elisa Santandrea, Chiara Della Libera, Leonardo Chelazzi; Statistical learning of distractor suppression. Journal of Vision 2017;17(10):674. doi: 10.1167/17.10.674.

      Download citation file:


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

      ×
  • Supplements
Abstract

The cognitive system has a remarkable capacity to extract and make good use of statistical information (statistical learning, SL), including for attentional guidance. In the attention domain, most SL studies so far explored the influence on performance of manipulating probability of target occurrence at various spatial locations. However, an emerging literature is starting to address changes in the efficiency of distractor suppression triggered by the unequal spatial distribution of distracting stimuli. Here we systematically assessed the latter form of learning. Our main focus here was to look for any interaction (cross-talk) between distractor suppression and target selection mechanisms; specifically, we wished to ask whether any changes in performance reflecting SL of distractor suppression would transfer to the efficiency of target selection across locations. In a series of experiments, participants had to report the pointing direction of an arrow target while ignoring a task-irrelevant salient distractor, when present (50%). While target probability was equal across locations, the distractor was more likely to appear at one particular display location and less at another location (and intermediate at two further locations). The results showed greater interference (capture) when the distractor was presented at the low probability location compared to any other location. Moreover, we demonstrated that this effect could not be explained in terms of inter-trial effects (e.g., repeated distractor location across consecutive trials). Importantly, although the target occurred equally often at all locations, the efficiency of target selection differed across locations, with faster responses for targets at the location with rare distractors. These results confirm that SL can modulate distractor suppression mechanisms. Furthermore, our findings indicate some degree of interdependence between distractor suppression and target selection processes, suggesting at least partly shared underlying mechanisms. The results will be discussed in relation to the notion of spatial priority maps.

Meeting abstract presented at VSS 2017

×
×

This PDF is available to Subscribers Only

Sign in or purchase a subscription to access this content. ×

You must be signed into an individual account to use this feature.

×