AUTHOR=Matsumori Kaosu , Koike Yasuharu , Matsumoto Kenji TITLE=A Biased Bayesian Inference for Decision-Making and Cognitive Control JOURNAL=Frontiers in Neuroscience VOLUME=12 YEAR=2018 URL=https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2018.00734 DOI=10.3389/fnins.2018.00734 ISSN=1662-453X 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.