Point Estimate Observers: A New Class of Models for Perceptual Decision Making

Heiko H. Schütt, Aspen H. Yoo, Joshua Calder-Travis, Wei Ji Ma

Research output: Contribution to journalArticlepeer-review


Bayesian optimal inference is often heralded as a principled, general framework for human perception. However, optimal inference requires integration over all possible world states, which quickly becomes intractable in complex real-world settings. Additionally, deviations from optimal inference have been observed in human decisions. A number of approximation methods have previously been suggested, such as sampling methods. In this study, we additionally propose point estimate observers, which evaluate only a single best estimate of the world state per response category. We compare the predicted behavior of these model observers to human decisions in five perceptual categorization tasks. Compared to the Bayesian observer, the point estimate observer loses decisively in one task, ties in two and wins in two tasks. Two sampling observers also improve upon the Bayesian observer, but in a different set of tasks. Thus, none of the existing general observer models appears to fit human perceptual decisions in all situations, but the point estimate observer is competitive with other observer models and may provide another stepping stone for future model development.

Original languageEnglish (US)
Pages (from-to)334-367
Number of pages34
JournalPsychological Review
Issue number2
StatePublished - Feb 20 2023


  • Bayesian observer
  • observer model
  • perceptual decision making
  • point estimate observer

ASJC Scopus subject areas

  • General Psychology


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