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Article . 2020
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Behavior Research Methods
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Behavior Research Methods
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The processing of pseudoword form and meaning in production and comprehension: A computational modeling approach using Linear Discriminative Learning

Authors: Yu-Ying Chuang; Marie-lenka Voller; Elnaz Shafaei-Bajestan; Susanne Gahl; Peter Hendrix; R. H. Baayen;

The processing of pseudoword form and meaning in production and comprehension: A computational modeling approach using Linear Discriminative Learning

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.

Related Organizations
Subjects by Vocabulary

Microsoft Academic Graph classification: Cognitive model Speech production Computer science Semantics computer.software_genre Lexicon Morpheme Lexical decision task Mental lexicon business.industry Pseudoword Artificial intelligence business computer Natural language processing

Keywords

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

  • BIP!
    Impact byBIP!
    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%
  • 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!BIP!
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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).
BIP!Citations provided by BIP!
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.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
28
Top 10%
Average
Top 10%
Green
hybrid
Funded by
EC| WIDE
Project
WIDE
Wide Incremental learning with Discrimination nEtworks
  • Funder: European Commission (EC)
  • Project Code: 742545
  • Funding stream: H2020 | ERC | ERC-ADG
Validated by funder
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