Abstract
Our ability to parse our acoustic environment relies on the brain’s capacity to extract statistical regularities from surrounding sounds. Previous work in regularity extraction has predominantly focused on the brain’s sensitivity to predictable patterns in sound sequences. However, natural sound environments are rarely completely predictable, often containing some level of randomness, yet the brain is able to effectively interpret its surroundings by extracting useful information from stochastic sounds. It has been previously shown that the brain is sensitive to the marginal lower-order statistics of sound sequences (i.e., mean and variance). In this work, we investigate the brain’s sensitivity to higher-order statistics describing temporal dependencies between sound events through a series of change detection experiments, where listeners are asked to detect changes in randomness in the pitch of tone sequences. Behavioral data indicate listeners collect statistical estimates to process incoming sounds, and a perceptual model based on Bayesian inference shows a capacity in the brain to track higher-order statistics. Further analysis of individual subjects’ behavior indicates an important role of perceptual constraints in listeners’ ability to track these sensory statistics with high fidelity. In addition, the inference model facilitates analysis of neural electroencephalography (EEG) responses, anchoring the analysis relative to the statistics of each stochastic stimulus. This reveals both a deviance response and a change-related disruption in phase of the stimulus-locked response that follow the higher-order statistics. These results shed light on the brain’s ability to process stochastic sound sequences.
Highlights
IntroductionThe brain parses incoming sounds into distinct sources and tracks these sources through time
To understand soundscapes, the brain parses incoming sounds into distinct sources and tracks these sources through time
To understand our auditory surroundings, the brain extracts invariant representations from sounds over time that are robust to the randomness inherent in real-world sound sources, while staying flexible to adapt to a dynamic environment
Summary
The brain parses incoming sounds into distinct sources and tracks these sources through time This process relies on the brain’s ability to sequentially collect information from sounds as they evolve over time, building representations of the underlying sources that are invariant to the randomness present in real-world sounds, while being flexible to adapt to changes in the acoustic scene. Extracting these representations from ongoing sounds is automatic and effortless for the average listener, but the underlying computations in the brain are largely unknown. The signature trait of deterministic regularities is the absence of ambiguity: a new sound can immediately be interpreted as a continuation of or a deviation from the regularity with certainty
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