Decision-making strategies of a coral reef fish given changing visual signal reliability

Abstract

ABSTRACTMaking informed decisions is fundamental to intelligent life and is crucial to survival. The problem many organisms face, is how to make optimal decisions in the face of incomplete or conflicting information. There is mounting evidence that humans employ a Bayesian framework which optimally combines the statistical properties of current evidence with those of past experience. This, it is assumed, requires the operation of a sophisticated cortical system in which the statistics of past experience are stored. Here we demonstrate Bayesian updating behaviour in a fish performing a non-adaptive, visual discrimination task. We find that fish can learn probability distributions and apply this knowledge as uncertainty in the correct choice increases, ultimately increasing the overall frequency of correct choices. Our study underlines the ubiquity of Bayes-like behaviour in animals and, since fish have no cortex, implies that Bayesian integration can be performed by relatively simple neural circuitry. Furthermore, our results reveal that probabilistic decision rules can be used by fish when visual information is unreliable, indicating a possible mechanism for decision making given the inherent noise in incoming sensory information.