Sustained EEG responses to rapidly unfolding stochastic sounds reflect Bayesian inferred reliability tracking.

How does the brain track and process rapidly changing sensory information? Current computational accounts suggest that our sensations and decisions arise from the intricate interplay between bottom-up sensory signals and constantly changing expectations regarding the statistics of the surrounding world. A significant focus of recent research is determining which statistical properties are tracked by the brain as it monitors the rapid progression of sensory information. Here, by combining EEG (three experiments N ≥ 22 each) and computational modelling, we examined how the brain processes rapid and stochastic sound sequences that simulate key aspects of dynamic sensory environments.

Neural Encoding of Auditory Statistics.

The human brain extracts statistical regularities embedded in real-world scenes to sift through the complexity stemming from changing dynamics and entwined uncertainty along multiple perceptual dimensions (e.g., pitch, timbre, location).

Computational framework for investigating predictive processing in auditory perception.

Background: The brain tracks sound sources as they evolve in time, collecting contextual information to predict future sensory inputs. Previous work in predictive coding typically focuses on the perception of predictable stimuli, leaving the implementation of these same neural processes in more complex, real-world environments containing randomness and uncertainty up for debate.

New Method: To facilitate investigation into the perception of less tightly-controlled listening scenarios, we present a computational model as a tool to ask targeted questions about the underlying predictive processes that connect complex sensory inputs to listener behavior and neural responses.

A Model for Statistical Regularity Extraction from Dynamic Sounds.

To understand our surroundings, we effortlessly parse our sound environment into sound sources, extracting invariant information-or regularities-over time to build an internal representation of the world around us. Previous experimental work has shown the brain is sensitive to many types of regularities in sound, but theoretical models that capture underlying principles of regularity tracking across diverse sequence structures have been few and far between. Existing efforts often focus on sound patterns rather the stochastic nature of sequences.

Detecting change in stochastic sound sequences.

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.