Explaining the effects of distractor statistics in visual search

Joshua Calder-Travis, Wei Ji Ma

Research output: Contribution to journalArticlepeer-review

Abstract

Visual search, the task of detecting or locating target items among distractor items in a visual scene, is an important function for animals and humans. Different theoretical accounts make differing predictions for the effects of distractor statistics. Here we use a task in which we parametrically vary distractor items, allowing for a simultaneously fine-grained and comprehensive study of distractor statistics. We found effects of target-distractor similarity, distractor variability, and an interaction between the two, although the effect of the interaction on performance differed from the one expected. To explain these findings, we constructed computational process models that make trial-by-trial predictions for behavior based on the stimulus presented. These models, including a Bayesian observer model, provided excellent accounts of both the qualitative and quantitative effects of distractor statistics, as well as of the effect of changing the statistics of the environment (in the form of distractors being drawn from a different distribution). We conclude with a broader discussion of the role of computational process models in the understanding of visual search.

Original languageEnglish (US)
Pages (from-to)1-26
Number of pages26
JournalJournal of vision
Volume20
Issue number13
DOIs
StatePublished - 2020

ASJC Scopus subject areas

  • Ophthalmology
  • Sensory Systems

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