Active reinforcement learning versus action bias and hysteresis: control with a mixture of experts and nonexperts
Fig 8
(a) Based on individual fits of the 2CE1 model, Good and Poor learners were combined and then reclassified according to whether the constant lateral bias was a leftward bias (βR < 0) (magenta bars) or a rightward bias (βR > 0) (cyan bars). The model comparison extended this posterior predictive check and others to another six intermediate models—four models nested within the 2CE1 model featuring exponential hysteresis (2N1, 2E1, 2C, 2CN1) and two models substituting 2-back hysteresis (2N2, 2CN2) but matched for degrees of freedom. For the probabilities of left or right actions, some of these right-handed people actually exhibited a contrary leftward bias; those who did exhibited a smaller absolute magnitude of bias than that of the rightward-bias group (p < 0.05). The models with a parameter for constant bias (2C through 2CE1) could replicate these effects (p < 0.05), falsifying the models that could not at all for lack of this parameter (p > 0.05). (b) Results were replicated in the 7-T Color/Motion version of the experiment.