Probabilistic Biases Meet the Bayesian Brain.

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Bibliographic Details
Title: Probabilistic Biases Meet the Bayesian Brain.
Authors: Chater, Nick (AUTHOR), Zhu, Jian-Qiao (AUTHOR), Spicer, Jake (AUTHOR), Sundh, Joakim (AUTHOR), León-Villagrá, Pablo (AUTHOR), Sanborn, Adam (AUTHOR)
Source: Current Directions in Psychological Science. Oct2020, Vol. 29 Issue 5, p506-512. 7p.
Subjects: Cognitive science, Mental arithmetic, Heuristic, Probability theory, Reconciliation
Abstract: In Bayesian cognitive science, the mind is seen as a spectacular probabilistic-inference machine. But judgment and decision-making (JDM) researchers have spent half a century uncovering how dramatically and systematically people depart from rational norms. In this article, we outline recent research that opens up the possibility of an unexpected reconciliation. The key hypothesis is that the brain neither represents nor calculates with probabilities but approximates probabilistic calculations by drawing samples from memory or mental simulation. Sampling models diverge from perfect probabilistic calculations in ways that capture many classic JDM findings, which offers the hope of an integrated explanation of classic heuristics and biases, including availability, representativeness, and anchoring and adjustment. [ABSTRACT FROM AUTHOR]
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Database: Psychology and Behavioral Sciences Collection
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