October 2020
Volume 20, Issue 11
Open Access
Vision Sciences Society Annual Meeting Abstract  |   October 2020
A Computational Account of Serial and Parallel Processing in Visual Search
Author Affiliations & Notes
  • Rachel Heaton
    University of Illinois
  • John Hummel
    University of Illinois
  • Alejandro Lleras
    University of Illinois
  • Simona Buetti
    University of Illinois
  • Footnotes
    Acknowledgements  This research was funded by NSF BCS Award #1921735.
Journal of Vision October 2020, Vol.20, 844. doi:https://doi.org/10.1167/jov.20.11.844
  • Views
  • Share
  • Tools
    • Alerts
      This feature is available to authenticated users only.
      Sign In or Create an Account ×
    • Get Citation

      Rachel Heaton, John Hummel, Alejandro Lleras, Simona Buetti; A Computational Account of Serial and Parallel Processing in Visual Search. Journal of Vision 2020;20(11):844. https://doi.org/10.1167/jov.20.11.844.

      Download citation file:

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

  • Supplements

We present a new computational model of visual search that follows on prior theoretical work by Buetti and Lleras emphasizing the contributions of parallel peripheral processing to visual search performance. The model uses concurrent parallel (distributed attention) and serial (focused attention) evaluative processes for inspecting items in a visual display. Search items are assigned random priorities for attentional selection. These priorities immediately begin to decay, and are refreshed based on feature similarity to the search template. Items are stochastically selected for focused attention based on Luce's choice axiom defined over their priorities. Selected items are matched to a search template and either accepted as the target or rejected as a distractor. During this serial process, the priorities of the remaining search items are updated in parallel, in proportion to their proximity to fixation. The resulting model successfully simulates the typical logarithmic slopes found in human data when the target-distractor similarity is medium to low (e.g., Buetti et al., 2016; Wang et al., 2017). It also produces linear search slopes when target-distractor similarity is elevated. We present simulations of these and other classic visual search phenomena, like the difference between feature and conjunction search, as well as search asymmetries.


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.