Neighborhood-statistics reveal complex dynamics of song acquisition in the zebra finch

Motor behaviors are continually shaped by a variety of processes such as environmental influences, development, and learning1,2. The resulting behavioral changes are commonly quantified based on hand-picked features3-10 (e.g. syllable pitch11) and assuming discrete classes of behaviors (e.g. distinct syllables)3-5,9,10,12-17. Such methods may generalize poorly across behaviors and species and are necessarily biased. Here we present an account of behavioral change based on nearest-neighbor statistics18-23 that avoids such biases and apply it to song development in the juvenile zebra finch3. First, we introduce the concept of repertoire dating, whereby each syllable rendition is dated with a \"pseudo\" production-day corresponding to the day when similar renditions were typical in the behavioral repertoire. Differences in pseudo production-day across renditions isolate the components of vocal variability congruent with the long-term changes due to vocal learning and development. This variability is large, as about 10% of renditions have pseudo production-days falling more than 10 days into the future (anticipations) or into the past (regressions) relative to their actual production time. Second, we obtain a holistic, yet low-dimensional, description of vocal change in terms of a behavioral trajectory, which reproduces the pairwise similarities between renditions grouped by production time and pseudo production-day24. The behavioral trajectory reveals multiple, previously unrecognized components of behavioral change operating at distinct time-scales. These components interact differently across the behavioral repertoire--diurnal change in regressions undergoes only weak overnight consolidation4,5, whereas anticipations and typical renditions consolidate fully2,6,25. Our nearest-neighbor methods yield model-free descriptions of how behavior evolves relative to itself, rather than relative to a potentially arbitrary, experimenter-defined, goal3-5,11. Because of their generality, our methods appear well-suited to comparing learning across behaviors and species1,26-32, and between biological and artificial systems.