Predictive reward-prediction errors of climbing fiber inputs integrate modular reinforcement learning with supervised learning
Fig 4
Sparse canonical-correlation analysis (sCCA) of TC scores with reinforcement-learning and sensorimotor-control variables.
A: Bars show coefficients of reinforcement-learning and sensorimotor-control variables corresponding to TC1-4 scores. B-F: the scatter plots of trials showing correlations of TC1 with Q (B), TC1 with δQ (C), TC2 with δQ (D), TC3 with R (E) and TC4 with No-go ✕ ELick (F). Black and gray lines indicate regression between variables when using all trials and trials of the cue-response condition with which each TC is primarily associated, i.e., TC1-HIT, TC2-FA and TC4-CR, respectively. Panel E shows the boxplot with gray lines indicating the median and the bottom and top edges of the box the 25th and 75th percentiles, respectively. All correlations in B-F are significant (p < 0.0001). Color convention of trials is the same as Fig 1. The inset of A shows color codes of the selected 7 reward and sensorimotor variables among 10 according to sCCA.