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Published on: June 29, 2021
Semantic composition of sentences word by word: MEG evidence for shared processing of conceptual and logical elements
Linmin Zhang 张琳敏1, Liina Pylkkänen2
1Department of Linguistics, New York University, New York, NY 10003, USA; NYU-ECNU Institute of Brain & Cognitive Science, New York University Shanghai, Shanghai, China.
Abstract:
Human language is a mixture of many types of elements, some clearly conceptual, like dog and run, and others more functional/logical, such as negation or quantificational elements (not, many, all). While theories are emerging for the neurobiology of conceptual combination, the neural mechanisms of integrating concepts with logical information remain largely unstudied. Do neural correlates of concept composition also reflect the composition of concepts with logical elements? In a previous MEG study, we have shown that in noun-noun compounds (e.g., tomato soup), the conceptual specificity of the first word modulates left anterior temporal lobe (LATL) amplitudes elicited on the second word, suggesting an effect of conceptual specificity, or informativeness, on the process of conceptual combination. Here we tested how this pattern is affected by negation, which has the ability to reverse informativeness relations: for example, while poodle is conceptually more informative than dog, no dog negates more possibilities and is therefore more informative than no poodle. We manipulated the informativeness of sentential subjects by fully crossing conceptual specificity (poodle vs. dog) with the presence of negation (no vs. a) to create positive and negative sentences (e.g., no/a-(green)-lizard-is-sleeping) and tested whether the effect of conceptual specificity was reversed for the integration of negative as compared to positive subjects. Exactly this pattern was observed in the LATL and surrounding fronto-temporal cortex during the processing of the sentence-final verb, suggesting a shared mechanism that tracks informativeness in integrating conceptual and logical elements in this network.
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