Disentangling meaning in the brain: Left temporal involvement in agreement processing
Hernandez-Cabrera, Juan A.
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Simona Mancini, Ileana Quiñones, Nicola Molinaro, Juan A. Hernandez-Cabrera, Manuel Carreiras, Disentangling meaning in the brain: Left temporal involvement in agreement processing, In Cortex, Volume 86, 2017, Pages 140-155, ISSN 0010-9452, https://doi.org/10.1016/j.cortex.2016.11.008.
Sentence comprehension is successfully accomplished by means of a form-to-meaning mapping procedure that relies on the extraction of morphosyntactic information from the input and its mapping to higher-level semantic–discourse representations. In this study, we sought to determine whether neuroanatomically distinct brain regions are involved in the processing of different types of information contained in the propositional meaning of a sentence, namely person and number. While person information indexes the role that an individual has in discourse (i.e., the speaker, the addressee or the entity being talked about by speaker and addressee), number indicates its cardinality (i.e., a single entity vs a multitude of entities). An event-related functional magnetic resonance imaging (fMRI) experiment was run using agreement-Correct and Person- and Number-violated sentences in Spanish, to disentangle the processing mechanisms and neural substrates associated with the building of discourse and cardinality representations. The contrast between Person and Number Violations showed qualitative and quantitative differences. A greater response for person compared to number was found in the left middle temporal gyrus (LMTG). However, critically, a posterior-to-anterior functional gradient emerged within this region. While the posterior portion of the LMTG was sensitive to both Person and Number Violations, the anterior portion of this region showed selective response for Person Violations. These results confirm that the comprehension of the propositional meaning of a sentence results from a composite, feature-sensitive mechanism of form-to-meaning mapping in which the nodes of the language network are differentially involved.