Contributions of linguistic typology to psycholinguistics

Clahsen, Harald

doi:10.1515/lingty-2016-0031

Cited by 8 publications

(4 citation statements)

References 30 publications

(40 reference statements)

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“…While these results suggest possible problems with the ED model, English may not be the best test case to fully understand these, since the sole regular inflectional class is also by far the most frequent. In contrast, many languages have multiple inflectional classes which can act 'regular' under various conditions (Seidenberg and Plaut, 2014;Clahsen, 2016).…”

Section: Introductionmentioning

confidence: 99%

Inflecting When There’s No Majority: Limitations of Encoder-Decoder Neural Networks as Cognitive Models for German Plurals

McCurdy¹,

Goldwater²,

Lopez³

2020

Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

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Can artificial neural networks learn to represent inflectional morphology and generalize to new words as human speakers do? Kirov and Cotterell (2018) argue that the answer is yes: modern Encoder-Decoder (ED) architectures learn human-like behavior when inflecting English verbs, such as extending the regular past tense form /-(e)d/ to novel words. However, their work does not address the criticism raised by Marcus et al. (1995): that neural models may learn to extend not the regular, but the most frequent class -and thus fail on tasks like German number inflection, where infrequent suffixes like /-s/ can still be productively generalized. To investigate this question, we first collect a new dataset from German speakers (production and ratings of plural forms for novel nouns) that is designed to avoid sources of information unavailable to the ED model. The speaker data show high variability, and two suffixes evince 'regular' behavior, appearing more often with phonologically atypical inputs. Encoder-decoder models do generalize the most frequently produced plural class, but do not show human-like variability or 'regular' extension of these other plural markers. We conclude that modern neural models may still struggle with minority-class generalization.

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Section: Introductionmentioning

confidence: 99%

Inflecting When There’s No Majority: Limitations of Encoder-Decoder Neural Networks as Cognitive Models for German Plurals

McCurdy¹,

Goldwater²,

Lopez³

2020

Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

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“…In recent years, experimental psycholinguistics has substantially gone beyond its traditional focus on English by including a wider range of languages as well as cross-linguistic comparisons between different languages into its research agenda; for recent reviews, see, for example, Norcliffe, Harris, and Jaeger (2015) and Clahsen (2016). Nevertheless, there isapart from a few exceptions (e.g.…”

Section: Introductionmentioning

confidence: 99%

Morphological decomposition in Bantu: a masked priming study on Setswana prefixation

Ciaccio

Kgolo

Clahsen

2020

Language, Cognition and Neuroscience

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African languages have rarely been the subject of psycholinguistic experimentation. The current study employs a masked visual priming experiment to investigate morphological processing in a Bantu language, Setswana. Our study takes advantage of the rich system of prefixes in Bantu languages, which offers the opportunity of testing morphological priming effects from prefixed inflected words and directly comparing them to priming effects from prefixed derived words on the same targets. We found significant priming effects of similar magnitude for both prefixed inflected and derived word forms, which were clearly dissociable from prime-target relatedness in both meaning and (orthographic) form. These findings provide support for a (possibly universal) mechanism of morphological decomposition applied during early visual word recognition that segments both (prefixed) inflected and derived word forms into their morphological constituents. ARTICLE HISTORY

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“…One important line of this research examines how linguistic diversity shapes language processing. There are numerous reports in the experimental literature that the human language processor is affected by a particular language's linguistic properties; see Clahsen (2016) for a review. Another recent line of psycholinguistic research that focuses on variability investigates individual differences in language processing, for example with respect to morphological priming (Andrews & Lo, 2013;Beyersmann, Casalis, Ziegler & Grainger, 2015).…”

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confidence: 99%

Variability and its limits in bilingual word recognition

Clahsen

Jessen

2020

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Abstract This study examines the processing of morphologically complex words focusing on how morphological (in addition to orthographic and semantic) factors affect bilingual word recognition. We report findings from a large experimental study with groups of bilingual (Turkish/German) speakers using the visual masked-priming technique. We found morphologically mediated effects on the response speed and the inter-individual variability within the bilingual participant group. We conclude that the grammar (qua morphological parsing) not only enhances speed of processing in bilingual language processing but also yields more uniform performance and thereby constrains variability within a group of otherwise heterogeneous individuals.

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Contributions of linguistic typology to psycholinguistics

Cited by 8 publications

References 30 publications

Inflecting When There’s No Majority: Limitations of Encoder-Decoder Neural Networks as Cognitive Models for German Plurals

Inflecting When There’s No Majority: Limitations of Encoder-Decoder Neural Networks as Cognitive Models for German Plurals

Morphological decomposition in Bantu: a masked priming study on Setswana prefixation

Variability and its limits in bilingual word recognition

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