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A Switching Observer for Human Perceptual Estimation.

Steeve Laquitaine1, Justin L Gardner1

  • 1Department of Psychology, Stanford University, Stanford, CA 94305, USA; Laboratory for Human Systems Neuroscience, RIKEN Brain Science Institute, Wako-shi, Saitama 351-0198, Japan.

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Summary
This summary is machine-generated.

Humans may not multiply prior beliefs with sensory evidence for perception. Instead, a switching heuristic between prior and sensory information better explains human estimation tasks, approximating Bayesian optimality.

Keywords:
Bayesian inferencebounded rationalityheuristicsoptimalityprior knowledgestatistical inferencestrategies

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Area of Science:

  • Cognitive Science
  • Neuroscience
  • Computational Vision

Background:

  • Human perception is often modeled as a normative Bayesian process, combining sensory evidence with prior knowledge.
  • This Bayesian framework assumes optimal multiplicative combination of priors and likelihoods for perceptual inference.

Purpose of the Study:

  • To test if a basic Bayesian observer model explains human behavior in sensory estimation tasks.
  • To investigate how humans integrate sensory evidence and priors when estimating motion direction or orientation.

Main Methods:

  • Subjects performed two estimation tasks involving varying sensory evidence (motion coherence/contrast) and priors (directions/orientations).
  • Behavioral data (estimates) were compared against predictions from a basic Bayesian observer model and a novel switching model.

Main Results:

  • While mean and variability of estimates matched the basic Bayesian model, distributions showed unpredicted bimodality.
  • A model incorporating a switching heuristic between prior and sensory evidence provided a better fit to the observed estimate distributions.

Conclusions:

  • Human perceptual inference may not rely solely on multiplicative combination of priors and sensory evidence.
  • A switching heuristic offers a more accurate account of how humans approximate Bayesian optimality in estimation tasks.