Identificador persistente para citar o vincular este elemento: http://hdl.handle.net/10553/129008
Título: Similarity-Based Interference in Sentence Comprehension in Aphasia: a Computational Evaluation of Two Models of Cue-Based Retrieval
Autores/as: Lissón Hernández, Paula José 
Paape, Dario
Pregla, Dorothea
Burchert, Frank
Stadie, Nicole
Vasishth, Shravan
Clasificación UNESCO: 5701 Lingüística aplicada
Palabras clave: Aphasia
Cognitive modeling
Computational modeling
Cue-based retrieval
Sentence comprehension
Fecha de publicación: 2023
Publicación seriada: Computational Brain and Behavior 
Resumen: Sentence comprehension requires the listener to link incoming words with short-term memory representations in order to build linguistic dependencies. The cue-based retrieval theory of sentence processing predicts that the retrieval of these memory representations is affected by similarity-based interference. We present the first large-scale computational evaluation of interference effects in two models of sentence processing — the activation-based model and a modification of the direct-access model — in individuals with aphasia (IWA) and control participants in German. The parameters of the models are linked to prominent theories of processing deficits in aphasia, and the models are tested against two linguistic constructions in German: pronoun resolution and relative clauses. The data come from a visual-world eye-tracking experiment combined with a sentence-picture matching task. The results show that both control participants and IWA are susceptible to retrieval interference, and that a combination of theoretical explanations (intermittent deficiencies, slow syntax, and resource reduction) can explain IWA’s deficits in sentence processing. Model comparisons reveal that both models have a similar predictive performance in pronoun resolution, but the activation-based model outperforms the direct-access model in relative clauses.
URI: http://hdl.handle.net/10553/129008
ISSN: 2522-0861
DOI: 10.1007/s42113-023-00168-3
Fuente: Computational Brain and Behavior [2522-0861], nº 6, p. 473–502
Colección:Artículos
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