ArticlePLoS computational biology2018
Detecting change in stochastic sound sequences.
Article in PLoS computational biology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Measuring surprisal in sound sequences.Behavior research methods · 2026Article
- The Effect of Previously Encountered Sensory Information on Neural Representations of Predictability: Evidence From Human EEG.The European journal of neuroscience · 2025Article
- Article
- Feasibility Trial Protocol for a Remote Intentional Music Listening Intervention to Support Mental Health in Individuals with Chronic Stroke.medRxiv : the preprint server for health sciences · 2025Article
- Humans can find rhythm in randomly timed sounds.Royal Society open science · 2025Article
- Bayesian prior uncertainty and surprisal elicit distinct neural patterns during sound localization in dynamic environments.Scientific reports · 2025Article
- Brain responses vary in duration-modeling strategies and challenges.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Article
- Rational arbitration between statistics and rules in human sequence processing.Nature human behaviour · 2022Article
- Neural Encoding of Auditory Statistics.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2021Article
- Computational framework for investigating predictive processing in auditory perception.Journal of neuroscience methods · 2021Article
- PPM-Decay: A computational model of auditory prediction with memory decay.PLoS computational biology · 2020Article
- Article
- Mice tune out not in: violation of prediction drives auditory saliency.Proceedings. Biological sciences · 2020Article
- Overcoming randomness does not rule out the importance of inherent randomness for functionality.Journal of biosciences · 2019Review
- Music predictability and liking enhance pupil dilation and promote motor learning in non-musicians.Scientific reports · 2019Article
- A Model for Statistical Regularity Extraction from Dynamic Sounds.Acta acustica united with acustica : the journal of the European Acoustics Association (EEIG)Article
Corrections and comments
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Authors and funding
2 authors.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.