Context-dependent sensory modulation underlies Bayesian vocal sequence perception

Abstract

Vocal communication in both songbirds and humans relies on categorical perception of smoothly varying acoustic spaces. Vocal perception can be biased by expectation and context, but the mechanisms of this bias are not well understood. We developed a behavioral task in which songbirds, European starlings, are trained to to classify smoothly varying song syllables in the context of predictive syllable sequences. We find that syllable-sequence predictability biases perceptual categorization following a Bayesian model of probabilistic information integration. We then recorded from populations of neurons in the auditory forebrain while birds actively categorized song syllables, observing large proportions of neurons that track the smoothly varying natural feature space of syllable categories. We observe that predictive information in the syllable sequences dynamically modulates sensory neural representations. These results support a Bayesian model of perception where predictive information acts to dynamically reallocate sensory neural resources, sharpening acuity (i.e. the likelihood) in high-probability regions of stimulus space.

Bidirectional Generative Adversarial Representation Learning for Natural Stimulus Synthesis

“…Additionally, it is possible to smoothly interpolate between different vocalisations (Fig. 2) which can be used to study decision making in a more natural context [ Sainburg et al, 2022 ]. Similar methods exist for the generation of vocalisations [ Sainburg et al, 2020, Arneodo et al, 2021 ], however, these models generate spectrograms whose phase must be reconstructed to produce waveforms, while our model produces waveforms directly.…”