Evidence map›Paper›PMID 32377973›Full record

ArticleBehavior research methods2021

The processing of pseudoword form and meaning in production and comprehension: A computational modeling approach using linear discriminative learning.

Yu-Ying Chuang, Marie Lenka Vollmer, Elnaz Shafaei-Bajestan, Susanne Gahl, Peter Hendrix, R Harald Baayen

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Article in Behavior research methods, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.

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19citing papers in PubMed
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3 · Its place in the literature

Who cites it

19 citing papers in PubMed.

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  12. Exposure to co-occurrence regularities in language drives semantic integration of new words.Journal of experimental psychology. Learning, memory, and cognition · 2022
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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Yu-Ying ChuangSeminar für Sprachwissenschaft, Eberhard-Karls University of Tübingen, Tübingen, Germany. yu-ying.chuang@uni-tuebingen.de.
Marie Lenka VollmerSeminar für Sprachwissenschaft, Eberhard-Karls University of Tübingen, Tübingen, Germany.
Elnaz Shafaei-BajestanSeminar für Sprachwissenschaft, Eberhard-Karls University of Tübingen, Tübingen, Germany.
Susanne GahlDepartment of Linguistics, University of California at Berkeley, Berkeley, CA, USA.
Peter HendrixSeminar für Sprachwissenschaft, Eberhard-Karls University of Tübingen, Tübingen, Germany.
R Harald BaayenSeminar für Sprachwissenschaft, Eberhard-Karls University of Tübingen, Tübingen, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Pseudowords have long served as key tools in psycholinguistic investigations of the lexicon. A common assumption underlying the use of pseudowords is that they are devoid of meaning: Comparing words and pseudowords may then shed light on how meaningful linguistic elements are processed differently from meaningless sound strings. However, pseudowords may in fact carry meaning. On the basis of a computational model of lexical processing, linear discriminative learning (LDL Baayen et al., Complexity, 2019, 1-39, 2019), we compute numeric vectors representing the semantics of pseudowords. We demonstrate that quantitative measures gauging the semantic neighborhoods of pseudowords predict reaction times in the Massive Auditory Lexical Decision (MALD) database (Tucker et al., 2018). We also show that the model successfully predicts the acoustic durations of pseudowords. Importantly, model predictions hinge on the hypothesis that the mechanisms underlying speech production and comprehension interact. Thus, pseudowords emerge as an outstanding tool for gauging the resonance between production and comprehension. Many pseudowords in the MALD database contain inflectional suffixes. Unlike many contemporary models, LDL captures the semantic commonalities of forms sharing inflectional exponents without using the linguistic construct of morphemes. We discuss methodological and theoretical implications for models of lexical processing and morphological theory. The results of this study, complementing those on real words reported in Baayen et al., (Complexity, 2019, 1-39, 2019), thus provide further evidence for the usefulness of LDL both as a cognitive model of the mental lexicon, and as a tool for generating new quantitative measures that are predictive for human lexical processing.

Indexed as

ComprehensionDiscrimination LearningHumansPsycholinguisticsSemanticsSpeechAuditory comprehensionAuditory pseudowordsComputational modelingLinear discriminative learningMorphologySpeech production

Identifiers

PMID32377973
PMCPMC8219637

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.