Nearest neighbors reveal fast and slow components of motor learning

Changes in behavior, due to environmental influences, development, and learning(1–5), are commonly quantified based on a few hand-picked, domain-specific, features(2–4,6,7) (e.g. the average pitch of acoustic vocalizations(3)) and assuming discrete classes of behaviors (e.g. distinct vocal syllables)(2,3,8–10). Such methods generalize poorly across different behaviors and model systems and may miss important components of change. Here we present a more general account of behavioral change based on nearest-neighbor statistics(11–13) and apply it to song development in a songbird, the zebra finch(3). First, we introduce “repertoire dating”, whereby each rendition of a behavior (e.g. each vocalization) is assigned a repertoire time, reflecting when similar renditions were typical in the behavioral repertoire. Repertoire time (rT) isolates the components of vocal variability congruent with the long-term changes due to vocal learning and development and stratifies the behavioral repertoire into regressions (rT < true production time, t), anticipations (rT > t), and typical renditions (rT ≈ t). Second, we obtain a holistic, yet low-dimensional(14), description of vocal change in terms of a stratified “behavioral trajectory”, revealing multiple, previously unrecognized, components of behavioral change on fast and slow timescales, as well as distinct patterns of overnight consolidation(1,2,4,15,16). Diurnal changes in regressions undergo only weak consolidation, whereas anticipations and typical renditions consolidate fully. Because of its generality, our non-parametric description of how behavior evolves relative to itself, rather than relative to a potentially arbitrary, experimenter-defined, goal(2,3,15,17) appears well-suited to compare learning and change across behaviors and species(18,19), as well as biological and artificial systems(5).