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Change in a probabilistic representation of meaning can account for N400 effects on articles: a neural network model

  • Increased N400 amplitudes on indefinite articles (a/an) incompatible with expected nouns have been initially taken as strong evidence for probabilistic pre-activation of phonological word forms, and recently been intensely debated because they have been difficult to replicate. Here, these effects are simulated using a neural network model of sentence comprehension that we previously used to simulate a broad range of empirical N400 effects. The model produces the effects when the cue validity of the articles concerning upcoming noun meaning in the learning environment is high, but fails to produce the effects when the cue validity of the articles is low due to adjectives presented between articles and nouns during training. These simulations provide insight into one of the factors potentially contributing to the small size of the effects in empirical studies and generate predictions for cross-linguistic differences in article induced N400 effects based on articles’ cue validity. The model accounts for article induced N400 effectsIncreased N400 amplitudes on indefinite articles (a/an) incompatible with expected nouns have been initially taken as strong evidence for probabilistic pre-activation of phonological word forms, and recently been intensely debated because they have been difficult to replicate. Here, these effects are simulated using a neural network model of sentence comprehension that we previously used to simulate a broad range of empirical N400 effects. The model produces the effects when the cue validity of the articles concerning upcoming noun meaning in the learning environment is high, but fails to produce the effects when the cue validity of the articles is low due to adjectives presented between articles and nouns during training. These simulations provide insight into one of the factors potentially contributing to the small size of the effects in empirical studies and generate predictions for cross-linguistic differences in article induced N400 effects based on articles’ cue validity. The model accounts for article induced N400 effects without assuming pre-activation of word forms, and instead simulates these effects as the stimulus-induced change in a probabilistic representation of meaning corresponding to an implicit semantic prediction error.zeige mehrzeige weniger

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Metadaten
Verfasserangaben:Milena RabovskyORCiDGND
URN:urn:nbn:de:kobv:517-opus4-526988
DOI:https://doi.org/10.25932/publishup-52698
ISSN:1866-8364
Titel des übergeordneten Werks (Deutsch):Postprints der Universität Potsdam : Humanwissenschaftliche Reihe
Schriftenreihe (Bandnummer):Zweitveröffentlichungen der Universität Potsdam : Humanwissenschaftliche Reihe (731)
Publikationstyp:Postprint
Sprache:Englisch
Datum der Erstveröffentlichung:17.03.2019
Erscheinungsjahr:2020
Veröffentlichende Institution:Universität Potsdam
Datum der Freischaltung:17.11.2021
Freies Schlagwort / Tag:ERPs; N400; cue validity; meaning; neural networks; prediction
Aufsatznummer:107466
Seitenanzahl:9
Quelle:Neuropsychologia, Volume 143, 2020, 107466, ISSN 0028-3932, https://doi.org/10.1016/j.neuropsychologia.2020.107466
Organisationseinheiten:Humanwissenschaftliche Fakultät / Strukturbereich Kognitionswissenschaften / Department Psychologie
DDC-Klassifikation:1 Philosophie und Psychologie / 15 Psychologie / 150 Psychologie
Peer Review:Referiert
Publikationsweg:Open Access / Green Open-Access
Lizenz (Deutsch):License LogoCC-BY-NC-ND - Namensnennung, nicht kommerziell, keine Bearbeitungen 4.0 International
Externe Anmerkung:Bibliographieeintrag der Originalveröffentlichung/Quelle
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