ORCID Profile
0000-0001-5551-8171
Current Organisation
University of Padua
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Publisher: Cold Spring Harbor Laboratory
Date: 28-08-2019
DOI: 10.1101/747816
Abstract: A crucial aspect when learning a language is discovering the rules that govern how words are combined in order to convey meanings. Since rules are characterized by sequential co-occurrences between elements (e.g. ‘ These cupcake s are un believ able ’ ), tracking the statistical relationships between these elements is fundamental. However, 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, fMRI analyses of late learning stages showed left parietal activity within a broad bilateral dorsal fronto-parietal network. Critically, 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 non-relevant brain region did not have the same interfering effect on generalization. Performance on an additional attentional task showed that rTMS on the same 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.
Publisher: Public Library of Science (PLoS)
Date: 02-11-2020
DOI: 10.1371/JOURNAL.PBIO.3000895
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 cupcake s are un believ able ”), 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.
No related grants have been discovered for ANNA MARTINEZ-ALVAREZ.