BCI Toolbox: An open-source python package for the Bayesian causal inference model
Fig 4
Examples of BCI toolbox outputs.
(A) The model fitting results with continuous data from a spatial localization task. Each plot corresponds to one of the stimulus conditions, with the first row plots representing unisensory auditory conditions (stimulus position varying from left-most to right-most positions along azimuth from left to right), and first column representing unisensory visual conditions, and all other plots corresponding to bisensory conditions. Positions of the auditory and visual stimuli are denoted using broken red and blue vertical lines, respectively. The red and blue histograms represent the auditory and visual response distributions of a specific subject, respectively. The red and blue solid lines represent the model fits produced by the toolbox. (B) The simulation results for one visual stimulus accompanied by two auditory stimuli. We used the fixed parameters (Weak tendency: pcommon = 0.2; Strong tendency: pcommon = 0.8; σ1 = 1; σ2 = 0.5; σP = 1.5; μP = 1.5) to simulate how prior integration tendency influences multisensory numerosity perception. We also used the fixed parameters (pcommon = 0.5; Low visual precision: σ1 = 1; high visual precision: σ1 = 0.5; σ2 = 0.5; σP = 1.5; μP = 1.5) to simulate how unisensory precision influences multisensory numerosity perception.