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Chapter 20 - An Introduction to Psychologically Plausible Sampling Schemes for Approximating Bayesian Inference

from Part VI - Computational Approaches

Published online by Cambridge University Press:  01 June 2023

By
Jian-Qiao Zhu ,
Nick Chater ,
Pablo León-Villagrá ,
Jake Spicer ,
Joakim Sundh  and
Adam Sanborn
Edited by
Klaus Fiedler ,
Peter Juslin  and
Jerker Denrell
Klaus Fiedler
Affiliation:
Universität Heidelberg
Peter Juslin
Affiliation:
Uppsala Universitet, Sweden
Jerker Denrell
Affiliation:
University of Warwick
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Summary

The brain must make inferences about, and decisions concerning, a highly complex and unpredictable world, based on sparse evidence. An “ideal” normative approach to such challenges is often modeled in terms of Bayesian probabilistic inference. But for real-world problems of perception, motor control, categorization, language understanding, or commonsense reasoning, exact probabilistic calculations are computationally intractable. Instead, we suggest that the brain solves these hard probability problems approximately, by considering one, or a few, samples from the relevant distributions. Here we provide a gentle introduction to the various sampling algorithms that have been considered as the approximation used by the brain. We broadly summarize these algorithms according to their level of knowledge and their assumptions regarding the target distribution, noting their strengths and weaknesses, their previous applications to behavioural phenomena, as well as their psychological plausibility.

Keywords

sampling algorithmsBayesian models of cognitionapproximationresource rationalityautocorrelation
Type
Chapter
Information
Sampling in Judgment and Decision Making , pp. 467 - 489
Publisher: Cambridge University Press
Print publication year: 2023

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