2017
DOI: 10.1007/978-3-319-60066-6
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Psychosyntax

Abstract: the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific … Show more

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Cited by 14 publications
(3 citation statements)
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“…See also Davies 1989, 1995 and Peacocke 1986. This account has been endorsed more recently in Pereplyotchik 2017 (pp. 169–80) and Rey 2020 (pp.…”
mentioning
confidence: 92%
“…See also Davies 1989, 1995 and Peacocke 1986. This account has been endorsed more recently in Pereplyotchik 2017 (pp. 169–80) and Rey 2020 (pp.…”
mentioning
confidence: 92%
“…As Firestone (2020) points out, performance measures are often unreliable guides in assessing the psychological plausibility of a DCNN, whether in vision or in any other domain. In psycholinguistics, performance has long ceased to be a reliable sign of human competence (Pereplyotchik, 2017), and computational linguists disagree about what performance measures to use (e.g., Sellam et al, 2022), even in DCNNs that make no claim to psychological plausibility. Thus, in order to make their case for the inadequacy of DCNN modelsagain, in vision or any other domainthe authors would need to cite evidence that evaluates the competence of such models.…”
Section: Introductionmentioning
confidence: 99%
“…As Firestone (2020) points out, performance measures are often unreliable guides in assessing the psychological plausibility of a DCNN, whether in vision or in any other domain. In psycholinguistics, performance has long ceased to be a reliable sign of human competence (Pereplyotchik, 2017), and computational linguists disagree about what performance measures to use (e.g., Sellam et al, 2022), even in DCNNs that make no claim to psychological plausibility. Thus, in order to make their case for the inadequacy of DCNN models – again, in vision or any other domain – the authors would need to cite evidence that evaluates the competence of such models.…”
mentioning
confidence: 99%