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Related Experiment Video

Updated: May 17, 2026

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

Published on: June 30, 2020

Not so primitive: context-sensitive meta-learning about unattended sound sequences.

Juanita Todd¹, Alexander Provost, Lisa R Whitson

¹School of Psychology, University of Newcastle, Callaghan, NSW, Australia. juanita.todd@newcastle.edu.au

Journal of Neurophysiology

|October 19, 2012

Summary

Mismatch negativity (MMN) magnitude is context-sensitive, influenced by deviant sound probability across multiple timescales. This suggests automatic salience filtering and meta-learning in auditory processing.

Related Experiment Videos

Last Updated: May 17, 2026

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

Published on: June 30, 2020

Area of Science:

Auditory Neuroscience
Cognitive Psychology
Neuroscience

Background:

Mismatch negativity (MMN) reflects auditory scene processing and auditory short-term memory.
MMN is an evoked response to auditory regularity violations.

Purpose of the Study:

Investigate context-sensitive modulation of MMN magnitude.
Explore the influence of deviant probability at multiple temporal scales.
Examine primacy bias and meta-learning in auditory prediction.

Main Methods:

Utilized a novel multiple-context and -timescale protocol.
Analyzed MMN magnitude in response to changing deviant probabilities.
Investigated evidence-based modulation and meta-learning effects.

Main Results:

MMN magnitude showed context-sensitive modulation based on deviant probability across timescales.
A primacy bias demonstrated asymmetric, evidence-based modulation of environmental predictions.
Meta-learning induced context-sensitive variations in predictive memory representation accessibility.

Conclusions:

Auditory prediction and MMN are modulated by context and temporal dynamics.
Primacy bias and meta-learning suggest automatic salience filtering outside awareness.
Findings offer insights into the neural basis of auditory perception and learning.