Extended Data Fig. 3 ∣. Relationship between DA signal and behavioral parameters.

a, Schematics of building a generalized linear model that relates user controlled stimuli and behavioral parameters to fluorescence signals. Briefly, there are 3 types of explanatory (independent) variables in the model. Continuous variables (speed, acceleration, rotation, position) continuously change their values as time passes. Event variables (movement initiation, cue, reward delivery, receptacle entry) are 0 except at a time point of an event when they temporarily change their value to 1. Whole trial variables (accuracy=0 for current trial failure, 1 for current trial success; previous trial=0 for previous trial failure, 1 for previous trial success) change their values in the beginning of a trial and stay constant until the next trial.

b, Comparison of average variable contributions for VTA jRCaMP (red), NAc jRCaMP (orange), and dLight (green) for beginner (left), intermediate (middle), and expert (right) sessions. Contribution of each category was calculated by a method described in a. Kinematic variables include speed, acceleration, and rotation variables. Other categories are assigned to an individual variable (a set of time shifted variables). Mean contributions to 3 signals were compared by one-way RM ANOVA for each variable category. Plotted as mean ± SEM across mice (n=10 mice).

c, Comparison of model fits for VTA jRCaMP (red), NAc jRCaMP (orange), and dLight (green) for beginner (left), intermediate (middle), and expert (right) sessions plotted as mean ± SEM across mice (n=10 mice). Model fit was estimated by the correlation between actual and predicted signals from the model. Left set of bars represent correlations during a full duration (−40~+80s respect to the trigger zone entry). Right set of bars represent correlations during a trial duration (−5+15s respect to the trigger zone entry). Model fits for 3 signals were compared by one-way RM ANOVA. (*)p<0.10 for one-way RM ANOVA (Bonferroni corrected).