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On the Origins of Suboptimality in Human Probabilistic Inference

Figure 5

Group mean optimality index.

Each bar represents the group-averaged optimality index for a specific session, for each prior (indexed from 1 to 8, see also Figure 2) and cue type, low-noise cues (red bars) or high-noise cues (blue bars). The optimality index in each trial is computed as the probability of locating the correct target based on the subjects' responses divided by the probability of locating the target for an optimal responder. The maximal optimality index is 1, for a Bayesian observer with correct internal model of the task and no sensorimotor noise. Error bars are SE across subjects. Priors are arranged in the order of differential entropy (i.e. increasing variance for Gaussian priors), except for ‘unimodal test’ priors which are listed in order of increasing width of the main peak in the prior (see text). The dotted line and dash-dotted line represent the optimality index of a suboptimal observer that takes into account respectively either only the cue or only the prior. The shaded area is the zone of synergistic integration, in which an observer performs better than using information from either the prior or the cue alone.

Figure 5

doi: https://doi.org/10.1371/journal.pcbi.1003661.g005