The Algorithm(s) of Birdsong Learning

Song learning requires birds to interpret sensory evidence, assign credit to changes in behaviour, and reproduce remembered vocal targets. This lecture compares auditory-feedback adaptation, reinforcement learning and imitation as parts of a possible common learning architecture.

Incomplete compensation for pitch-shifted feedback is examined through Bayesian causal inference: internal pitch predictions are combined with auditory evidence while feedback unlikely to be self-generated is discounted. Nonlinear pitch trajectories during conditioning motivate an extension of classical policy-gradient learning. The model separates an exploratory component updated by reward from other components of motor output that need not be updated on each trial.

For imitation, vocal syllables can be learned separately from their order. An intrinsic reward based on a sum–max rule combines each target syllable’s best pitch match, with a light-tailed similarity function limiting the contribution of large mismatches. The lecture then considers how these complementary mechanisms might form a unified, testable account of birdsong learning, including initial attempts to use AI to develop the synthesis.