Extended Data Fig. 5: Querying different possible sources of variability in dLight waveforms.

a) Average per-syllable dLight peaks associated with six behavioural categories (n = 7 dive syllables, 7 grooms, 9 pauses, 13 rears, 5 scrunches, 16 walks). Each category is associated with multiple syllables and were identified through human annotation. b) Within-syllable changes in kinematic parameters do not covary with peak dLight. Box plots of kinematic parameters binned by syllable-associated peak dLight – shown are the first and fourth quartiles. Kinematic variables were averaged from syllable onset to syllable offset, and box plots show the distribution of per-instance averages. Box plots for two examples syllables are shown, an investigatory pause (top; N = 15245 syllable instances) and a scrunch (bottom; N = 11838 syllable instances). c) Left top: average dLight fluorescence waveforms for two syllables that contain a left- (contralateral) and right-ward (ipsilateral) turning component. Consistent with prior studies indicating that elevated dopamine and striatal activity is associated with contralateral turning, we find higher average dLight levels are associated with contralateral turning^34,88. Fluorescence traces were z-scored to a circular shuffle. Left bottom: dLight waveforms broken out into quartiles based on syllable-associated fluorescence, as in Fig. 1k. Right: performance of a linear SVM classifier predicting individual syllable instances as left or right turns. Average observed accuracy was 51%, indicating substantial instance-by-instance variability (p < .001, one-sided shuffle test). d) Schematic illustrating the hypothesis that dopamine fluctuations may reflect performance prediction errors; here “performance error” is defined as the degree to which a given syllable instance differs from its mean implementation (see Methods). e) Top: schematic describing the linear model used to characterize whether syllable rendition quality compared to a template provides additional information about dLight fluorescence on top of the kinematic parameters described in Fig. 1e. Bottom: model coefficient for each kinematic parameter. Significant parameters are shaded black (p < .001 two-sided shuffle test, n = 1000 bootstraps; error bars indicate 95% CI). f) Left half: Distribution of dLight waveforms across different velocity change bins. Syllable transitions were binned by the change in velocity from one syllable to the next. The peak magnitudes of dLight waveforms within each “velocity change” bin were then binned from lowest to highest; these binned dLight waveforms reveal the diversity of dLight transients associated with each behavioural transition type. Left: averaged velocity traces for each velocity change/dLight peak bin pair. Right: averaged dLight traces for each velocity change/dLight peak bin pair. Right half: Same as left half, but transitions were binned by their associated jerk, and waveform distributions are plotted as described above. Here inter-syllable jerk is used as a surrogate for the biomechanical difficulty mice are likely to experience as they transition across syllables. g) Syllable-associated dopamine peaks do not contain information about position in the open field. For each syllable, peak dLight and velocity were binned into ten equally spaced bins, and the animal’s 2D centroid position in the arena was binned into four equally spaced bins. Then, mutual information was computed between the dopamine and the position bin. Shown are 2D histograms of mouse position for the highest and lowest bin for dLight peaks (left) and velocity (right) for an example syllable. h) Per-syllable mutual information between dLight per-syllable average peaks and position in the open field (p = .107, n = 57 syllables, one-sided test). The p-value was computed by comparing the average mutual information across all syllables against the mutual information computed on shuffled data. i) Specific syllable transitions do not contain information about the likelihood of a dopamine transient (p = .165, n = 14 mice, one-sided test). Here, we estimated the average likelihood of syllable-associated dLight peak crossing the 95^th percentile for all syllable transitions. These likelihoods were used to build a 2D matrix, where cell i, j was the likelihood of a transient for the transition from syllable i to syllable j. Finally, we computed the mutual information of this matrix per-mouse, and estimated p-values by comparing with the mutual information computed on shuffled data.