Humans incorporate attention-dependent uncertainty into perceptual decisions and confidence

Rachel N. Denison, William T. Adler, Marisa Carrasco, Wei Ji Ma

Research output: Contribution to journalArticlepeer-review


Perceptual decisions are better when they take uncertainty into account. Uncertainty arises not only from the properties of sensory input but also from cognitive sources, such as different levels of attention. However, it is unknown whether humans appropriately adjust for such cognitive sources of uncertainty during perceptual decision-making. Here we show that, in a task in which uncertainty is relevant for performance, human categorization and confidence decisions take into account uncertainty related to attention. We manipulated uncertainty in an orientation categorization task from trial to trial using only an attentional cue. The categorization task was designed to disambiguate decision rules that did or did not depend on attention. Using formal model comparison to evaluate decision behavior, we found that category and confidence decision boundaries shifted as a function of attention in an approximately Bayesian fashion. This means that the observer's attentional state on each trial contributed probabilistically to the decision computation. This responsiveness of an observer's decisions to attention-dependent uncertainty should improve perceptual decisions in natural vision, in which attention is unevenly distributed across a scene.

Original languageEnglish (US)
Pages (from-to)11090-11095
Number of pages6
JournalProceedings of the National Academy of Sciences of the United States of America
Issue number43
StatePublished - Oct 23 2018


  • Attention
  • Bayesian
  • Confidence
  • Criterion
  • Perceptual decision

ASJC Scopus subject areas

  • General


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