Extended Data Fig. 6: Dopamine predicts future syllable choices, and behaviour predicts prior dopamine dynamics.

a) Correlation matrix between dLight associated with a given syllable, entropy (which summarizes the variability of the subsequent syllable choice), syllable counts (for the syllable associated with dLight), and the dLight associated with the next syllable. Here, each feature was averaged per syllable/mouse pair, and the Pearson correlation was computed between feature averages (n = 760 syllable/mouse pairs). Syllable-associated dLight, entropy and syllable counts are all substantially correlated with each other, as described in the manuscript. Note that entropy (here defined as outbound entropy, the degree to which the subsequent syllable choice is predictable or variable) does not correlate with the amount of dLight on the subsequent syllable. This observation means that the amount of dopamine associated with a given syllable does not reflect whether that specific syllable was a more or less variable choice, given the preceding syllable; this contrasts with the correlation between syllable-associated dLight and outbound entropy, which demonstrates that the amount of dopamine associated with a given syllable predicts whether the next syllable choice will be deterministic or variable. b) Left: schematic for an encoding model which uses future behaviour to predict average syllable-associated dLight in the past (n = 760 syllable/mouse pairs, see Methods). Middle: plot of model predictions against actual dLight peak values on held-out data (5-fold cross-validation repeated 50 times). This model combines each feature at its best lag, lag = 10 syllables for velocity, 100 syllables for counts, and 10 syllables for entropy. Each point is a syllable/mouse pair, and the color of each point represents a kernel density estimate. Regression line is shown in blue. Right: the correlation between predicted syllable-associated dLight values and actual dLight values compared to n = 1000 shuffles (average Pearson correlation of held-out mouse/syllable pairs r = 0.46, p < .001; p-values for correlations throughout this figure were estimated by comparing observed correlation to Pearson correlation from shuffled data via a one-sided test). Performance using kinematic parameters only, r = 0.39, counts and entropy only r = 0.22, both models p < .001 one-sided shuffle test. To evaluate model performance using feature subsets, we refit the model from scratch for each group of features using cross validation. c) Median beta coefficients of the encoding model shown in Extended Data Fig. 6b at increasing bin sizes. Shaded region indicates 95% confidence intervals for each behavioural variable across Markov-chain Monte Carlo samples. d) Schematic of a linear encoding model predicting instantaneous dLight fluorescence from future behaviour. In this model each behavioural variable is convolved with a learned kernel, with the result of each convolution summed to produce a predicted dLight trace (see Methods). e) Top: correlation between model predictions and true dLight fluorescence values (median correlation over all held-out experiments using all features r = 0.28, in black is model performance with experiment-permuted dLight traces, p < .001 shuffle test, n = 211 experiments). Bottom: model performance quantified as held-out correlation (2-fold cross-validation, Pearson r) shown using all behavioural variables (“all”), variables related to behavioural structure (syllable counts or transition entropy, “syllable only”), or kinematic parameters (velocity, angular velocity, height velocity, or acceleration, “kinematic only”). Held-out correlation was evaluated for each experiment (n = 211). To evaluate model performance using feature subsets, we refit the model for each group of features (median r over held-out experiments for kinematic parameters 0.23; syllable-related measures 0.16; all correlations p < .001, one-sided shuffle test). f) Representative kernels learned by the fitting procedure (with cross-validation, see Methods) for each behavioural variable. Left: kernels for all behavioural variables with the same scaling. Right: kernels y-axes are re-scaled according to the scalebar shown on the left to visualize temporal dynamics of each kernel. Error bars indicate 99% bootstrap confidence interval. g) Model prediction of instantaneous dLight fluorescence for two example held-out experiments. Green indicates observed dLight fluctuations over time, orange indicates model-predicted fluctuations.