The neural basis of combinatory syntax and semantics

There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

Abstract

Human language allows us to create an infinitude of ideas from a finite set of basic building blocks. What is the neurobiology of this combinatory system? Research has begun to dissect the neural basis of natural language syntax and semantics by analyzing the basics of meaning composition, such as two-word phrases. This work has revealed a system of composition that involves rapidly peaking activity in the left anterior temporal lobe and later engagement of the medial prefrontal cortex. Both brain regions show evidence of shared processing between comprehension and production, as well as between spoken and signed language. Both appear to compute meaning, not syntactic structure. This Review discusses how language builds meaning and lays out directions for future neurobiological research on the combinatory system.

Related collections

Most cited references 36

Record: found
Abstract: found
Article: not found

Characterizing the dynamics of mental representations: the temporal generalization method.

J-R. King, S Dehaene (2014)

Parsing a cognitive task into a sequence of operations is a central problem in cognitive neuroscience. We argue that a major advance is now possible owing to the application of pattern classifiers to time-resolved recordings of brain activity [electroencephalography (EEG), magnetoencephalography (MEG), or intracranial recordings]. By testing at which moment a specific mental content becomes decodable in brain activity, we can characterize the time course of cognitive codes. Most importantly, the manner in which the trained classifiers generalize across time, and from one experimental condition to another, sheds light on the temporal organization of information-processing stages. A repertoire of canonical dynamical patterns is observed across various experiments and brain regions. This method thus provides a novel way to understand how mental representations are manipulated and transformed. Copyright © 2014 Elsevier Ltd. All rights reserved.

0 comments Cited 299 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: found
Article: not found

Cortical representation of the constituent structure of sentences.

Christophe Pallier, Anne-Dominique Devauchelle, Stanislas Dehaene (2011)

Linguistic analyses suggest that sentences are not mere strings of words but possess a hierarchical structure with constituents nested inside each other. We used functional magnetic resonance imaging (fMRI) to search for the cerebral mechanisms of this theoretical construct. We hypothesized that the neural assembly that encodes a constituent grows with its size, which can be approximately indexed by the number of words it encompasses. We therefore searched for brain regions where activation increased parametrically with the size of linguistic constituents, in response to a visual stream always comprising 12 written words or pseudowords. The results isolated a network of left-hemispheric regions that could be dissociated into two major subsets. Inferior frontal and posterior temporal regions showed constituent size effects regardless of whether actual content words were present or were replaced by pseudowords (jabberwocky stimuli). This observation suggests that these areas operate autonomously of other language areas and can extract abstract syntactic frames based on function words and morphological information alone. On the other hand, regions in the temporal pole, anterior superior temporal sulcus and temporo-parietal junction showed constituent size effect only in the presence of lexico-semantic information, suggesting that they may encode semantic constituents. In several inferior frontal and superior temporal regions, activation was delayed in response to the largest constituent structures, suggesting that nested linguistic structures take increasingly longer time to be computed and that these delays can be measured with fMRI.