Article,
Bayesian Decision Models: A Primer
Neuron, 104 (1): 164 - 175 (2019)
DOI: https://doi.org/10.1016/j.neuron.2019.09.037
Abstract
To understand decision-making behavior in simple, controlled environments, Bayesian models are often useful. First, optimal behavior is always Bayesian. Second, even when behavior deviates from optimality, the Bayesian approach offers candidate models to account for suboptimalities. Third, a realist interpretation of Bayesian models opens the door to studying the neural representation of uncertainty. In this tutorial, we review the principles of Bayesian models of decision making and then focus on five case studies with exercises. We conclude with reflections and future directions.
