Nature, Vol.577, No.7791, 526-+, 2020
Nearest neighbours reveal fast and slow components of motor learning
A new method for analysing change in high-dimensional data is based on nearest-neighbour statistics and is applied here to song dynamics during vocal learning in zebra finches, but could potentially be applied to other biological and artificial behaviours. Changes in behaviour resulting from environmental influences, development and learning(1-5) are commonly quantified on the basis of a few hand-picked features(2-4,6,7) (for example, the average pitch of acoustic vocalizations(3)), assuming discrete classes of behaviours (such as distinct vocal syllables)(2,3,8-10). However, such methods generalize poorly across different behaviours and model systems and may miss important components of change. Here we present a more-general account of behavioural change that is based on nearest-neighbour statistics(11-13), and apply it to song development in a songbird, the zebra finch(3). First, we introduce the concept of 'repertoire dating', whereby each rendition of a behaviour (for example, each vocalization) is assigned a repertoire time, reflecting when similar renditions were typical in the behavioural repertoire. Repertoire time isolates the components of vocal variability that are congruent with long-term changes due to vocal learning and development, and stratifies the behavioural repertoire into 'regressions', 'anticipations' and 'typical renditions'. Second, we obtain a holistic, yet low-dimensional, description of vocal change in terms of a stratified 'behavioural trajectory', revealing numerous previously unrecognized components of behavioural change on fast and slow timescales, as well as distinct patterns of overnight consolidation(1,2,4,14,15) across the behavioral repertoire. We find that diurnal changes in regressions undergo only weak consolidation, whereas anticipations and typical renditions consolidate fully. Because of its generality, our nonparametric description of how behaviour evolves relative to itself-rather than to a potentially arbitrary, experimenter-defined goal(2,3,14,16)-appears well suited for comparing learning and change across behaviours and species(17,18), as well as biological and artificial systems(5).