Spontaneous animal behaviour is built from action modules that are concatenated by the brain into sequences1,2. However, the neural mechanisms that guide the composition of naturalistic, self-motivated behaviour remain unknown. Here we show that dopamine systematically fluctuates in the dorsolateral striatum (DLS) as mice spontaneously express sub-second behavioural modules, despite the absence of task structure, sensory cues or exogenous reward. Photometric recordings and calibrated closed-loop optogenetic manipulations during open field behaviour demonstrate that DLS dopamine fluctuations increase sequence variation over seconds, reinforce the use of associated behavioural modules over minutes, and modulate the vigour with which modules are expressed, without directly influencing movement initiation or moment-to-moment kinematics. Although the reinforcing effects of optogenetic DLS dopamine manipulations vary across behavioural modules and individual mice, these differences are well predicted by observed variation in the relationships between endogenous dopamine and module use. Consistent with the possibility that DLS dopamine fluctuations act as a teaching signal, mice build sequences during exploration as if to maximize dopamine. Together, these findings suggest a model in which the same circuits and computations that govern action choices in structured tasks have a key role in sculpting the content of unconstrained, high-dimensional, spontaneous behaviour.

Pure tone audiograms are used to assess the degree and underlying source of hearing loss. Audiograms are typically categorized into a few canonical types, each thought to reflect distinct pathologies of the ear. Here, we analyzed 116,400 patient records from our clinic collected over a 24-year period and found that standard categorization left 46% of patient records unclassified. To better account for the full spectrum of hearing loss profiles, we used a Gaussian Mixture Model (GMM) to segment audiograms without any assumptions about frequency relationships, interaural symmetry or etiology. The GMM converged on ten types, featuring varying degrees of high-frequency hearing loss, flat loss, mixed loss, and notched profiles, with predictable relationships to patient age and sex. A separate GMM clustering of 15,380 audiograms from the National Health and Nutrition Examination Survey (NHANES) identified six similar types, that only lacked the more extreme hearing loss configurations observed in our patient cohort. Whereas traditional approaches distill hearing loss configurations down to a few canonical types by disregarding much of the underlying variability, an objective probabilistic model that accounted for all of the data identified an organized, but more heterogenous set of audiogram types that was consistent across two large clinical databases.The pure tone audiogram is the current gold standard clinical hearing assessment. Clinical management of hearing loss, from diagnosis to intervention, largely depends upon quantifying pure tone thresholds at octave intervals between 250 and 8000 Hz 1 . Multiple factors including age, genetic background and noise exposure history can influence the level and configuration of pure-tone thresholds within the standard audiometric frequency range 2-5 .A long-standing goal has been to infer the underlying cause of hearing loss using the pure-tone audiogram, medical diagnoses and knowledge from post-mortem human temporal bone and animal studies 6-10 . These approaches have identified four essential types -(i) a normal audiogram, where thresholds across test frequencies are equal or better than 20 dB HL, (ii) a flat hearing loss, where audiogram thresholds are elevated outside of the normal range but are approximately flat across test frequencies, presumably due to possible metabolic losses in the stria vascularis; (iii) a sloping, high frequency hearing loss (HFHL) due to presumed sensorineural damage; and (iv) a mixed phenotype reflecting modest threshold shift at low frequencies and a steeply sloping loss at high frequencies, which has been interpreted as a mixture of metabolic and sensorineural contributors 7,11-15 .These categories are useful as a first approximation, but studies with larger data samples either note the presence of mixed phenotypes or phenotypes with indeterminate causes 6 . Studies using algorithmic clustering methods note the presence of a gradual continuum of shapes rather than distinct categories 16 . Additionally, a recent study that reimaged historical...