Integrative processing of perception and reward in an auditory localization paradigm

Under natural conditions, human beings routinely have to choose among multiple alternatives which are associated with specific outcomes of varying desirability. Typically, decisions are based upon the processing of perceptual input, which introduces additional noise to the system. Bayesian decision theory (BDT) allows us to formalize decision under risk and to predict statistically optimal choice behavior. In the present study, human observers performed a classification task characterized by an extensive amount of perceptual uncertainty (auditory localization). In addition, a spatial reward function was imposed on the task. We set up a BDT model with no free parameters to serve as a benchmark for statistically optimal choices, and tested it against a purely perceptual model and a hybrid, heuristic model. In addition, we tested these three models with free rather than fixed parameters for the perceptual uncertainty and the peak of the reward function. The log likelihoods of the models given the empirical data were determined by means of Monte Carlo simulations. Bayesian model comparison (BMC) revealed that the BDT model with two free parameters was the most plausible among the tested models. The fitted parameter values for the peak of the reward function were consistently smaller than the actual peak reward communicated to the participants. The results are discussed in the context of an internal underweighting of the reward function.