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Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
Published on: February 15, 2017
Invariant neural representation of parts of speech in the human brain
Pranav Misra1, Yen-Cheng Shih2, Hsiang-Yu Yu2
1Harvard University, Cambridge, MA 02138, USA; Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, USA.
Abstract:
Elucidating the internal representation of language in the brain has major implications for cognitive science, brain disorders, and artificial intelligence. A pillar of linguistic studies is the notion that words have defined functions, often referred to as parts of speech. Here, we recorded invasive neurophysiological responses from 1,801 electrodes in 20 patients with pharmacologically resistant epilepsy while they were presented with two-word phrases consisting of an adjective and a noun. We observed neural signals that distinguished between these two parts of speech. The representation of parts of speech showed invariance across visual and auditory presentation modalities; robustness to word properties such as length, order, frequency, and semantics; and even generalization across different languages. The selective signals were circumscribed within a small region in the left lateral orbitofrontal cortex. This selective, invariant, and localized representation of parts of speech extends classic fronto-temporal language models, providing a target for mechanistic and causal tests of how the brain represents the basic building blocks of language, and introduces a systematic approach to elucidate the orchestration of more complex aspects of language.
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