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  Hierarchy in language interpretation: Evidence from behavioural experiments and computational modelling

Coopmans, C. W., De Hoop, H., Kaushik, K., Hagoort, P., & Martin, A. E. (2022). Hierarchy in language interpretation: Evidence from behavioural experiments and computational modelling. Language, Cognition and Neuroscience, 37(4), 420-439. doi:10.1080/23273798.2021.1980595.

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Coopmans_etal_2022_hierarchy in language interpretation.pdf (Verlagsversion), 3MB
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© 2021 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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 Urheber:
Coopmans, Cas W.1, 2, 3, Autor           
De Hoop, Helen3, Autor
Kaushik, Karthikeya4, Autor
Hagoort, Peter1, 4, Autor           
Martin, Andrea E.4, 5, Autor           
Affiliations:
1Neurobiology of Language Department, MPI for Psycholinguistics, Max Planck Society, ou_792551              
2International Max Planck Research School for Language Sciences, MPI for Psycholinguistics, Max Planck Society, Nijmegen, NL, ou_1119545              
3Center for Language Studies, External Organizations, ou_55238              
4Donders Institute for Brain, Cognition and Behaviour, External Organizations, ou_55236              
5Language and Computation in Neural Systems, MPI for Psycholinguistics, Max Planck Society, ou_3217300              

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 Zusammenfassung: It has long been recognised that phrases and sentences are organised hierarchically, but many computational models of language treat them as sequences of words without computing constituent structure. Against this background, we conducted two experiments which showed that participants interpret ambiguous noun phrases, such as second blue ball, in terms of their abstract hierarchical structure rather than their linear surface order. When a neural network model was tested on this task, it could simulate such “hierarchical” behaviour. However, when we changed the training data such that they were not entirely unambiguous anymore, the model stopped generalising in a human-like way. It did not systematically generalise to novel items, and when it was trained on ambiguous trials, it strongly favoured the linear interpretation. We argue that these models should be endowed with a bias to make generalisations over hierarchical structure in order to be cognitively adequate models of human language.

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Sprache(n): eng - English
 Datum: 20212021-09-282022
 Publikationsstatus: Erschienen
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 Art der Begutachtung: Expertenbegutachtung
 Identifikatoren: DOI: 10.1080/23273798.2021.1980595
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Titel: Language, Cognition and Neuroscience
Genre der Quelle: Zeitschrift
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Ort, Verlag, Ausgabe: London : Routledge
Seiten: - Band / Heft: 37 (4) Artikelnummer: - Start- / Endseite: 420 - 439 Identifikator: Anderer: ISSN
CoNE: https://pure.mpg.de/cone/journals/resource/2327-3798