TY - JOUR
T1 - Integrating when and what information in the left parietal lobe allows language rule generalization
AU - Orpella, Joan
AU - Ripollés, Pablo
AU - Ruzzoli, Manuela
AU - Amengual, Julià L.
AU - Callejas, Alicia
AU - Martinez-Alvarez, Anna
AU - Soto-Faraco, Salvador
AU - de Diego-Balaguer, Ruth
N1 - Funding Information:
Funding:Thisworkwassupportedbythe EuropeanResearchCouncilgrantERC-StG-313841 (TuningLang)andtheBFU2017-87109-PGrant fromtheSpanishMinisteriodeCienciae Innovacio ´n(RdD-B),theEuropeanResearch Council(ProofofConcept,ERC,727595(SS-F), andtheJuandelaCiervaPost-Doctorate Fellowship(JCI-2012-12335,Ministeriode EconomiayCompetividad(MR).Thefundershad noroleinstudydesign,datacollectionand analysis,decisiontopublish,orpreparationofthe manuscript.
Funding Information:
This work was supported by the European Research Council grant ERC-StG-313841 (TuningLang) and the BFU2017-87109-P Grant from the Spanish Ministerio de Ciencia e Innovaci?n (RdD-B), the European Research Council (Proof of Concept, ERC, 727595 (SS-F), and the Juan de la Cierva Post-Doctorate Fellowship (JCI-2012-12335, Ministerio de Economia y Competividad (MR). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. We are thankful to A. Correa and D. Sanabria for their helpful insights in the design of the temporal attention task.
Publisher Copyright:
© 2020 Orpella et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
PY - 2020/11/2
Y1 - 2020/11/2
N2 - 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.
AB - 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.
KW - Adult
KW - Attention/physiology
KW - Brain/physiology
KW - Brain Mapping/methods
KW - Cognition/physiology
KW - Female
KW - Frontal Lobe/physiology
KW - Functional Laterality/physiology
KW - Humans
KW - Language
KW - Learning/physiology
KW - Linguistics/methods
KW - Magnetic Resonance Imaging/methods
KW - Male
KW - Parietal Lobe/physiology
KW - Photic Stimulation/methods
KW - Reaction Time/physiology
KW - Transcranial Magnetic Stimulation/methods
KW - Young Adult
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U2 - 10.1371/journal.pbio.3000895
DO - 10.1371/journal.pbio.3000895
M3 - Article
C2 - 33137084
AN - SCOPUS:85095798306
SN - 1544-9173
VL - 18
JO - PLoS biology
JF - PLoS biology
IS - 11
M1 - e3000895
ER -