October 2020
Volume 20, Issue 11
Open Access
Vision Sciences Society Annual Meeting Abstract  |   October 2020
Background complexity decreases evidence accumulation rates during parallel processing in efficient search
Author Affiliations & Notes
  • Gavin Ng
    University of Illinois
  • Kirk Ballew
    Graduate School of Informatics, Kyoto University
  • Alejandro Lleras
    Johns Hopkins University
  • Simona Buetti
    Universidad Autonoma de Madrid
  • Footnotes
    Acknowledgements  This material is based upon work supported by the National Science Foundation under Grant No BCS1921735.
Journal of Vision October 2020, Vol.20, 460. doi:https://doi.org/10.1167/jov.20.11.460
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      Gavin Ng, Kirk Ballew, Alejandro Lleras, Simona Buetti; Background complexity decreases evidence accumulation rates during parallel processing in efficient search. Journal of Vision 2020;20(11):460. https://doi.org/10.1167/jov.20.11.460.

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

  • Supplements

Parallel processing in efficient search tasks involves a rejection of non-target items via a process of evidence accumulation. This evidence accumulation process results in a logarithmic increase in response times as a function of set size. The slope of this logarithmic function indexes the rate of accumulation; the greater the target-item similarity, the slower the rate, the steeper the slope. Although almost all of visual search in the real world involves items against backgrounds, evidence accumulation thus far has only been examined without backgrounds. Here, we examined the effect of background information in efficient search tasks. In Experiment 1, search stimuli were displayed against a background that was either a scene, phase-scrambled, or solid-colored. When target-distractor similarity was low, there was no effect of background type on both the slope and intercept of the logarithmic function. When target- distractor similarity was high, the slope for the scene background was steeper than that for the scrambled background, which was in turn steeper than that for the single-colored background. Thus, the greater the complexity of the background, the slower the rate of evidence accumulation of individual items. In Experiment 2, we examined the effect of meaningful but unstructured backgrounds by replacing the solid-colored background with an upside-down scene. Regardless of target-distractor similarity, the upside-down background produced the shallowest slope (fastest accumulation rate) and the highest intercept. Consistent with previous findings, our results suggest that processing of scene gist is automatic. When the scene is meaningful but unstructured, a constant processing cost (increased intercept) is incurred. This could arise either from the discounting of the inconsistent background, or a longer time to obtain scene gist. However, object segmentation is easier since objects do not blend in with the upside-down scene structure, resulting in a faster accumulation rate.


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