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Efficient stabilization of imprecise statistical inference through conditional belief updating
Julie Drevet1,2, Jan Drugowitsch3, Valentin Wyart4,5
1Laboratoire de Neurosciences Cognitives et Computationnelles, Institut National de la Santé et de la Recherche Médicale (Inserm), Paris, France. julie.drevet@ens.fr.
Nature Human Behaviour
|September 22, 2022
Summary
Humans selectively update beliefs based on sensory information reliability, discarding unreliable data. This strategy reduces cognitive costs and improves decision accuracy in uncertain environments.
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.
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