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Updated: Apr 5, 2026

Interaction between Phonological and Semantic Processes in Visual Word Recognition using Electrophysiology
Published on: June 29, 2021
Aphasias and theories of linguistic representation: representing frequency, hierarchy, constructions, and sequential
Lise Menn1, Cecily Jill Duffield1
1Department of Linguistics, Institute of Cognitive Science, University of Colorado, Boulder, CO, USA.
Abstract:
Error and preservation patterns in aphasic speech show that the brain makes use of the frequencies of words, constructions, and collocations, as well as category membership and hierarchical structure, during language processing. Frequency effects are evident along two quasi-independent axes: syntagmatic (the sequential context, e.g., deploying correct functors, categories, and utterance-level intonation) and paradigmatic (the choice at any given linguistic level, e.g., selecting content words and modifying structures). Frequency along the syntagmatic axis is shown to play a role in errors involving idioms, constructions, and collocations that cross major phrasal boundaries. Along the paradigmatic axis, frequency affects errors involving lexical selection, competing functors and inflected forms (e.g., using plural where singular is required). An account of language representation and processing that encompasses frequency as well as categorization and structure is compatible with what we know about how the brain works: increased experience with a linguistic structure results in increased activation-and strengthening-of the neural networks involved in processing that structure. These claims are supported by the literature on experimental work in normal speakers. Parsimony, plus the unexamined assumption that mental representation is like a written record (entries either present or absent, structure displayable in two dimensions), has been a misleading guide to modeling language representation. The substantial redundancy in representations and processing that is introduced by incorporating both frequency-based and hierarchy-based information is in fact appropriate for the brain as a fast, reliable, massively parallel error-correcting network with very large storage capacity and gradient representation strength. WIREs Cogn Sci 2013, 4:651-663. doi: 10.1002/wcs.1257 CONFLICT OF INTEREST: The authors have declared no conflicts of interest for this article. For further resources related to this article, please visit the WIREs website.
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