Skip to main content
Dryad

Data from: Learning the sound inventory of a complex vocal skill via an intrinsic reward

Toutounji, Hazem1; Zai, Anja2; Tchernichovski, Ofer3; Hahnloser, Richard2; Lipkind, Dina4

Published Feb 19, 2024 on Dryad. https://doi.org/10.5061/dryad.3r2280gpp

Data files

Feb 19, 2024 version files 42.89 GB

Abstract

Reinforcement learning (RL) is thought to underlie the acquisition of vocal skills like birdsong and speech, where sounding like one’s “tutor” is rewarding. But what RL strategy generates the rich sound inventories for song or speech? We find that the standard actor-critic model of birdsong learning fails to explain juvenile zebra finches’ efficient learning of multiple syllables. But when we replace a single actor with multiple independent actors that jointly maximize a common intrinsic reward, then birds’ empirical learning trajectories are accurately reproduced. Importantly, the influence of each actor (syllable) on the magnitude of global reward is competitively determined by its acoustic similarity to target syllables. This leads to each actor matching the target it is closest to, and occasionally, to the competitive exclusion of an actor from the learning process (i.e., the learned song). We propose that a competitive-cooperative multi-actor (MARL) algorithm is key for the efficient learning of the action inventory of a complex skill.