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The processing of pseudoword form and meaning in production and comprehension: A computational modeling approach using Linear Discriminative Learning
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. Electronic supplementary material The online version of this article (10.3758/s13428-020-01356-w) contains supplementary material, which is available to authorized users.
- University of California, Berkeley United States
- University of Tübingen Germany
Microsoft Academic Graph classification: Cognitive model Speech production Computer science computer.software_genre Semantics Lexicon Morpheme Lexical decision task Mental lexicon business.industry Pseudoword Artificial intelligence business computer Natural language processing
Morphology, Auditory pseudowords, Experimental and Cognitive Psychology, Article, Discrimination Learning, Auditory comprehension, Arts and Humanities (miscellaneous), Developmental and Educational Psychology, Humans, Speech, General Psychology, Speech production, Psycholinguistics, Computational modeling, Semantics, Linear discriminative learning, Psychology (miscellaneous), Comprehension
Morphology, Auditory pseudowords, Experimental and Cognitive Psychology, Article, Discrimination Learning, Auditory comprehension, Arts and Humanities (miscellaneous), Developmental and Educational Psychology, Humans, Speech, General Psychology, Speech production, Psycholinguistics, Computational modeling, Semantics, Linear discriminative learning, Psychology (miscellaneous), Comprehension
Microsoft Academic Graph classification: Cognitive model Speech production Computer science computer.software_genre Semantics Lexicon Morpheme Lexical decision task Mental lexicon business.industry Pseudoword Artificial intelligence business computer Natural language processing
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).28 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 10% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Average impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Top 10% visibility views 20 download downloads 46 citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).28 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 10% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Average impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Top 10% Powered byBIP!- 20views46downloads