ArticlePLoS biology2020
Integrating when and what information in the left parietal lobe allows language rule generalization.
Article in PLoS biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Mapping shared and specific cortical after-effects of repetitive TMS on brain function.BMC medicine · 2026Article
- "What" and "When" Predictions Jointly Modulate Speech Processing.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025Article
- Auditory cortical regions show resting-state functional connectivity with the default mode-like network in echolocating bats.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Elucidating a statistical learning brain network: Coordinate-based meta-analyses and functional connectivity profiles of artificial grammar learning in healthy adults.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Editorial: The musical brain, volume II.Frontiers in neuroscience · 2024Article
- "What" and "when" predictions modulate auditory processing in a mutually congruent manner.Frontiers in neuroscience · 2023Article
- What made us "hunter-gatherers of words".Frontiers in neuroscience · 2023Article
- Differential activation of a frontoparietal network explains population-level differences in statistical learning from speech.PLoS biology · 2022Article
- Language statistical learning responds to reinforcement learning principles rooted in the striatum.PLoS biology · 2021Article
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A crucial aspect when learning a language is discovering the rules that govern how words are combined in order to convey meanings. Because rules are characterized by sequential co-occurrences between elements (e.g., "These cupcakes are unbelievable"), tracking the statistical relationships between these elements is fundamental. However, purely bottom-up statistical learning alone cannot fully account for the ability to create abstract rule representations that can be generalized, a paramount requirement of linguistic rules. Here, we provide evidence that, after the statistical relations between words have been extracted, the engagement of goal-directed attention is key to enable rule generalization. Incidental learning performance during a rule-learning task on an artificial language revealed a progressive shift from statistical learning to goal-directed attention. In addition, and consistent with the recruitment of attention, functional MRI (fMRI) analyses of late learning stages showed left parietal activity within a broad bilateral dorsal frontoparietal network. Critically, repetitive transcranial magnetic stimulation (rTMS) on participants' peak of activation within the left parietal cortex impaired their ability to generalize learned rules to a structurally analogous new language. No stimulation or rTMS on a nonrelevant brain region did not have the same interfering effect on generalization. Performance on an additional attentional task showed that this rTMS on the parietal site hindered participants' ability to integrate "what" (stimulus identity) and "when" (stimulus timing) information about an expected target. The present findings suggest that learning rules from speech is a two-stage process: following statistical learning, goal-directed attention-involving left parietal regions-integrates "what" and "when" stimulus information to facilitate rapid rule generalization.
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