Bayesian comparison of explicit and implicit causal inference strategies in multisensory heading perception

doi:10.1371/journal.pcbi.1006110

Bayesian comparison of explicit and implicit causal inference strategies in multisensory heading perception

Fig 6

Joint fits.

Results of the joint fits across tasks. A: Protected exceedance probability and estimated posterior frequency (mean ± SD) of distinct model components for each model factor. Each factor also displays the Bayesian omnibus risk (BOR). B: Joint model fits of the explicit causal inference (unity judgment) task, for different models of interest. Each panel shows the proportion of ‘unity’ responses, as a function of stimulus disparity and for different levels of visual reliability. Bars are ±1 SEM of data across subjects. Shaded areas are ±1 SEM of model predictions across subjects. Numbers on top right of each panel report the absolute goodness of fit across all tasks. C: Joint model fits of the implicit causal inference task, for the same models of panel B. Panels show vestibular bias as a function of co-presented visual heading direction s_vis, and for different levels of visual reliability. Bars are ±1 SEM of data across subjects. Shaded areas are ±1 SEM of model predictions across subjects.

doi: https://doi.org/10.1371/journal.pcbi.1006110.g006