Fig. 4: Action kernel prior.
From: Brain-wide representations of prior information in mouse decision-making
a, The model frequency and exceedance probabilities for three subjective prior models, using session-wise cross-validation in 107 mice (≥2 sessions; Methods; the error bars show the s.d.). The best model involves filtering recent actions with an exponential kernel. Act. kernel, action kernel; Bayes opt., Bayes optimal; Stim. kernel, stimulus kernel. b, The decay constant for the action kernel across sessions and animals (light purple; median = 5.45 trials, dashed line). The proportion correct for the action kernel as a function of the decay constant is shown (dark purple). The median trial constant aligns with the optimal performance achievable with the action kernel, only 1.9% below the Bayes-optimal performance. c, Performance on zero-contrast trials conditioned on whether the previous action was correct or incorrect, across behavioural models and animal behaviour. Right, the same analysis for a simulated agent using the Bayes-optimal prior decoded from neural data (neural prior) to generate decisions. The performance drop between correct and incorrect previous trials for the neural prior suggests that the action kernel model better accounts for neural activity, consistent with behaviour. Top, WFI data; n = 51 sessions. Bottom, Ephys data; n = 139 mice. Statistical analysis was performed using Wilcoxon signed-rank tests. The error bars show the s.e.m. d, The uncorrected R2 is higher when decoding the action kernel prior compared with when decoding the Bayes-optimal prior during the ITI, for the Ephys and WFI modalities. Statistical analysis was performed using Wilcoxon signed-rank tests. e, The weight of the previous actions (purple) and previous stimuli (yellow) on the decoded Bayes-optimal prior, estimated from neural activity (left, Ephys; right, WFI). The dashed lines show the 95th percentile of the null distribution (Methods). f, The correlation between neural inverse decay constants (estimating the temporal dependency of the neural signals on previous actions) and behavioural inverse decay constants (from fitting the action kernel to behaviour). Both Ephys and WFI data show correlations (two-sided Pearson test; Methods; the shading shows the 95% confidence intervals). NS, not significant; *P < 0.05, ***P < 0.001.
