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Humans selectively update beliefs based on sensory information reliability, discarding unreliable data. This strategy reduces cognitive costs and improves decision accuracy in uncertain environments.

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Area of Science:

  • Cognitive Science
  • Decision Making
  • Computational Neuroscience

Background:

  • Human statistical inference is crucial for navigating uncertain environments.
  • Inference is prone to biases and imprecision, leading to variable beliefs and behavior.
  • Understanding how humans manage inference costs is key to explaining decision-making.

Purpose of the Study:

  • To investigate human strategies for belief updating under uncertainty.
  • To determine if humans selectively process sensory information.
  • To evaluate the impact of selective updating on decision accuracy and reliability.

Main Methods:

  • Studied human decisions in a sequential categorization task.
  • Utilized noisy visual stimuli to create uncertainty.
  • Analyzed participants' belief updating patterns and stimulus discard rates.

Main Results:

  • Humans conditionally update beliefs, integrating information only when sensory reliability is high.
  • Participants discarded up to one-third of incoming stimuli.
  • This conditional updating strategy demonstrated high test-retest reliability and correlated with perceptual confidence.

Conclusions:

  • Humans employ a selective belief updating strategy to manage inference costs.
  • This strategy, by filtering unreliable information, counterintuitively enhances decision accuracy.
  • The findings offer a novel explanation for human behavior in uncertain environments.