ArticlePLoS biology2022
Differential activation of a frontoparietal network explains population-level differences in statistical learning from speech.
Article in PLoS biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Mapping the neural patterns of verbal repetition: an activation likelihood estimation meta-analysis.Brain structure & function · 2026Pooled it
- Altered Inter-Network Functional Integration Underlies Speech Impairment in Parkinson's Disease: A Resting-State fMRI Analysis.Brain and behavior · 2026Article
- When attention matters: Domain-specific disruption of explicit but not implicit memory in statistical learning.Memory & cognition · 2026Article
- Rhythmic skills mediate the link between music training and cognition via attention and phonological processing.Communications psychology · 2026Article
- The Effects of Cognitive Fatigue and Articulatory Suppression on Statistical Language Learning Depend on the Strength of Cognitive Resources.Cognitive science · 2026Article
- Statistical learning across cognitive and affective domains: a multidimensional review.Frontiers in integrative neuroscience · 2025Review
- Refined analysis of the Speech-to-Speech Synchronization task reveals subharmonic synchronization.Frontiers in neuroscience · 2025Article
- Rats synchronize predictively to metronomes.iScience · 2024Article
- Limited but specific engagement of the mature language network during linguistic statistical learning.Cerebral cortex (New York, N.Y. : 1991) · 2024Article
- Auditory-motor synchronization and perception suggest partially distinct time scales in speech and music.Communications psychology · 2024Article
- Specificity of Motor Contributions to Auditory Statistical Learning.Journal of cognition · 2024Article
- The Domain-Specific Neural Basis of Auditory Statistical Learning in 5-7-Year-Old Children.Neurobiology of language (Cambridge, Mass.) · 2024Article
- Replication of population-level differences in auditory-motor synchronization ability in a Norwegian-speaking population.Communications psychology · 2023Article
- Auditory-motor synchronization varies among individuals and is critically shaped by acoustic features.Communications biology · 2023Article
- Explaining flexible continuous speech comprehension from individual motor rhythms.Proceedings. Biological sciences · 2023Article
- Studying rhythm processing in speech through the lens of auditory-motor synchronization.Frontiers in neuroscience · 2023Review
- Speech-to-Speech Synchronization protocol to classify human participants as high or low auditory-motor synchronizers.STAR protocols · 2022Article
- Musical Sophistication and Speech Auditory-Motor Coupling: Easy Tests for Quick Answers.Frontiers in neuroscience · 2021Article
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7 authors.
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Abstract
People of all ages display the ability to detect and learn from patterns in seemingly random stimuli. Referred to as statistical learning (SL), this process is particularly critical when learning a spoken language, helping in the identification of discrete words within a spoken phrase. Here, by considering individual differences in speech auditory-motor synchronization, we demonstrate that recruitment of a specific neural network supports behavioral differences in SL from speech. While independent component analysis (ICA) of fMRI data revealed that a network of auditory and superior pre/motor regions is universally activated in the process of learning, a frontoparietal network is additionally and selectively engaged by only some individuals (high auditory-motor synchronizers). Importantly, activation of this frontoparietal network is related to a boost in learning performance, and interference with this network via articulatory suppression (AS; i.e., producing irrelevant speech during learning) normalizes performance across the entire sample. Our work provides novel insights on SL from speech and reconciles previous contrasting findings. These findings also highlight a more general need to factor in fundamental individual differences for a precise characterization of cognitive phenomena.
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