Model parameters (across-subjects mean ± standard error of the mean) and fit indices of the three computational models.

pC, prior common-source probability; σP, standard deviation of the spatial prior (in °); σA, standard deviation of the auditory likelihood (in °); σV, standard deviation of the visual likelihood at two levels of visual reliability (1, high; 2, low) (in °); R2, coefficient of determination; relBICGroup, Bayesian information criterion at the group level, i.e., subject-specific BICs summed over all subjects (BIC = LL − 0.5 M ln(N), LL = log likelihood, M = number of parameters, N = number of data points) of a model relative to the Bayesian Causal Inference (“model averaging”) model (n.b. a smaller relBICGroup indicates that a model provides a better explanation of our data); EP, exceedance probability, i.e., probability that a model is more likely than any other model.Model parameters (across-subjects mean ± standard error of the mean) and fit indices of the three computational models.