Comparison between alternative models of decision making.

<p>We tested a class of alternative models of decision making which differ with respect to predictions for non-Gaussian trials only. <b>a</b>: Each column represents a subject, divided by group (either unimodal or bimodal test session), each row an observer model identified by a model string (see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003661#pcbi-1003661-t002" target="_blank">Table 2</a>). Cell color indicates model's evidence, here displayed as the Bayes factor against the best model for that subject (a higher value means a worse performance of a given model with respect to the best model). Models are sorted by their posterior likelihood for a randomly selected subject (see panel b). Numbers above cells specify ranking for most supported models with comparable evidence (difference less than 10 in 2 log Bayes factor <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003661#pcbi.1003661-Kass1" target="_blank">[32]</a>). <b>b</b>: Probability that a given model generated the data of a randomly chosen subject. Here and in panel c, brown bars represent the most supported models (or model levels within a factor). Asterisks indicate a significant exceedance probability, that is the posterior probability that a given model (or model component) is more likely than any other model (or model component): , . <b>c</b>: Probability that a given model level within a factor generated the data of a randomly chosen subject. Label ‘GA’ stands for no Gaussian approximation (full posterior).