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Updated: Mar 26, 2026

Foreign Accent and Forensic Speaker Identification in Voice Lineups: The Influence of Acoustic Features Based on Prosody

Foreign Accent and Forensic Speaker Identification in Voice Lineups: The Influence of Acoustic Features Based on Prosody

Published on: September 27, 2024

Resting-state low-frequency fluctuations reflect individual differences in spoken language learning.

Zhizhou Deng¹, Bharath Chandrasekaran², Suiping Wang³

¹Center for the Study of Applied Psychology and Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou, China; Department of Linguistics and Modern Languages, The Chinese University of Hong Kong, Hong Kong.

Cortex; a Journal Devoted to the Study of the Nervous System and Behavior

|February 12, 2016

Summary

Resting-state brain activity, specifically low-frequency fluctuations, can predict spoken language learning success. Higher activity in the left superior temporal gyrus and efficient network interactions predict better learning outcomes.

Keywords:

Individual differences Low-frequency fluctuations Resting-state Spoken language learning fMRI

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Infant Auditory Processing and Event-related Brain Oscillations

Infant Auditory Processing and Event-related Brain Oscillations

Published on: July 1, 2015

Area of Science:

Neuroscience
Cognitive Science
Psycholinguistics

Background:

Identifying objective predictors of language learning success is crucial.
Spontaneous brain activity variations, measured by resting-state functional magnetic resonance imaging (RS-fMRI), correlate with cognitive abilities.

Purpose of the Study:

To investigate the relationship between resting-state spontaneous brain activity and individual differences in spoken language learning.
To determine if pre-training brain activity can predict learning outcomes in a novel sound-to-word paradigm.

Main Methods:

Acquired RS-fMRI data from participants before language training.
Applied amplitude of spontaneous low-frequency fluctuation (ALFF) analysis, graph theory, and independent component analysis (ICA).
Trained participants on a Mandarin Chinese pitch-based sound-to-word learning task.

Main Results:

Higher regional ALFF in the left superior temporal gyrus correlated positively with learning performance.
Lower ALFF in default mode network (DMN) regions correlated positively with learning performance.
Increased local efficiency and degree in the left superior temporal gyrus predicted better learning.
Negative correlation (competition) between the DMN and dorsal attention network predicted poorer learning.

Conclusions:

Spontaneous brain activity, analyzed regionally and at the network level, can predict future spoken language learning success without pre-defined regions of interest.
Both regional brain dynamics and large-scale network interactions during rest are significant factors in individual differences in language acquisition.