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A Biased Bayesian Inference for Decision-Making and Cognitive Control.

ABSTRACT: Although classical decision-making studies have assumed that subjects behave in a Bayes-optimal way, the sub-optimality that causes biases in decision-making is currently under debate. Here, we propose a synthesis based on exponentially-biased Bayesian inference, including various decision-making and probability judgments with different bias levels. We arrange three major parameter estimation methods in a two-dimensional bias parameter space (prior and likelihood), of the biased Bayesian inference. Then, we discuss a neural implementation of the biased Bayesian inference on the basis of changes in weights in neural connections, which we regarded as a combination of leaky/unstable neural integrator and probabilistic population coding. Finally, we discuss mechanisms of cognitive control which may regulate the bias levels.

SUBMITTER: Matsumori K

PROVIDER: S-EPMC6195105 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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A Biased Bayesian Inference for Decision-Making and Cognitive Control.

Matsumori Kaosu K Koike Yasuharu Y Matsumoto Kenji K

Frontiers in neuroscience 20181012

Although classical decision-making studies have assumed that subjects behave in a Bayes-optimal way, the sub-optimality that causes biases in decision-making is currently under debate. Here, we propose a synthesis based on exponentially-biased Bayesian inference, including various decision-making and probability judgments with different bias levels. We arrange three major parameter estimation methods in a two-dimensional bias parameter space (prior and likelihood), of the biased Bayesian inferen ...[more]

PMID: 30369867

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