A neurobiologically inspired model of sentence comprehension.
While AI models of sentence comprehension reach high, human-like performance levels, the architecture and neural functioning of these network models are not biologically plausible. We propose a neurobiologically inspired neural network model for sentence comprehension that includes modules represent...
| Publicado en: | Language, Cognition & Neuroscience Vol. 40; no. 5; pp. 616 - 640 |
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| Autores principales: | , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Taylor & Francis Ltd
Jun2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=185256481&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 185256481 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23273798 IQ6T jtl: Language, Cognition & Neuroscience issn: 23273798 maglogo: N pubinfo: dt: Jun2025 vid: 40 iid: 5 pid: 377 pub: Taylor & Francis Ltd place: Philadelphia, Pennsylvania artinfo: ui: 185256481 183430184 185256481 185256481 10.1080/23273798.2025.2473537 185256481 ppf: 616 ppct: 24 formats: tig: atl: A neurobiologically inspired model of sentence comprehension. aug: au: Kröger, Bernd J. Bekolay, Trevor affil: Department of Phoniatrics, Pedaudiology and Communication Disorders, Medical School, RWTH Aachen University, Aachen, Germany sug: subj: Neurobiology Language Processing Models, Biological Neural Networks (Computer) Human Semantics Boosting Machine Learning Algorithms Information Resources ab: While AI models of sentence comprehension reach high, human-like performance levels, the architecture and neural functioning of these network models are not biologically plausible. We propose a neurobiologically inspired neural network model for sentence comprehension that includes modules representing a mental lexicon, syntactic processing, and semantic processing. Alongside the hierarchical module structure, the developmental trial-and-error (or iterative engineering) process for building the model resulted in the need for two parallel processing pathways, a content-related path that directly forwards lexical information to semantic sentence processing, and a sequence-related path, unfolding the syntactic structure of the sentence. A semantic processing module integrates the information from both pathways. At the model's highest processing level, the information processed in both pathways allows for thematic role assignment, or semantic event specification, for the sentence. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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