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A Bayesian Model of Conditioned Perception.

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Human observers may prioritize self-consistency over optimal sensory evidence evaluation. This study models how committing to one hypothesis can lead to biased perceptions, explaining suboptimal decision-making and cognitive dissonance.

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Area of Science:

  • Cognitive psychology
  • Computational neuroscience
  • Decision science

Background:

  • Humans often process sensory information under multiple potential explanations (hypotheses).
  • Optimal inference typically involves weighting evidence by hypothesis probability.
  • However, observed behavior is often suboptimal, suggesting alternative processing strategies.

Purpose of the Study:

  • To model how human observers evaluate sensory evidence under multiple hypotheses.
  • To explain suboptimal perceptual behavior and cognitive dissonance through a self-consistency mechanism.
  • To test a conditional Bayesian observer model against psychophysical data.

Main Methods:

  • Formulated a conditional Bayesian observer model.
  • Simulated observer behavior under conditions of hypothesis commitment.
  • Compared model predictions to existing psychophysical data from perceptual experiments.

Main Results:

  • The model successfully explains suboptimal behavior where observers commit to a single hypothesis.
  • Demonstrated that self-consistency can lead to biased perceptual estimates.
  • Showed model's ability to predict subjective responses in experimental variants.

Conclusions:

  • Human observers may sacrifice optimal inference for self-consistency in perception.
  • The conditional Bayesian observer model provides a framework for understanding these biases.
  • This framework may also apply to understanding cognitive dissonance phenomena.