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., 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. (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.

Morpho-Phonetic Effects in Speech Production: Modeling the Acoustic Duration of English Derived Words With Linear Discriminative Learning

“…The present study thus contributes to the growing literature that demonstrates that LDL is a promising alternative approach to speech production which can explain the variation in fine phonetic detail we find in different kinds of words, be they simplex, complex, or non-words (cf. Baayen et al, 2019b ; Chuang et al, 2020 ).…”

Modeling Morphological Priming in German With Naive Discriminative Learning

“…What Baayen et al (2019) and Baayen et al (2018) have shown for LDL is that accurate mappings between form vectors and distributional semantic vectors can be set up with linear transformations, i.e., with simple two-layer networks (and no hidden layers). The more comprehensive model of the mental lexicon developed in Baayen et al (2019) and Chuang et al (2019) makes use of multiple such networks to generate quantitative predictions for auditory comprehension (with audio as input), visual comprehension, and speech production. A proof of concept that inflected forms of rich paradigms can be predicted from their corresponding semantic vectors without requiring sublexical form units, such as stems and exponents is provided by Baayen et al (2018).…”

“…The theoretical construct of the morpheme as smallest sign of the language system has met with substantial criticism (see e.g., Matthews, 1974;Beard, 1977;Aronoff, 1994;Stump, 2001;Blevins, 2016). Whereas the morpheme-as-sign appears a reasonably useful construct for agglutinating languages, such as Turkish, as well as for morphologically simple languages, such as English (but see Blevins, 2003), it fails to provide much insight for typologically very dissimilar languages, such as Latin, Estonian, or Navajo (see e.g., Baayen et al, 2018Baayen et al, , 2019Chuang et al, 2019, for detailed discussion). One important insight from theoretical morphology is that systematicities in form are not coupled in a straightforward one-to-one way with systematicities in meaning.…”

LDL-AURIS: A computational model, grounded in error-driven learning, for the comprehension of single spoken words

“…Baayen et al (2019) tested their model on 130,000 words extracted from 20 hours of speech sampled from the UCLA Library Broadcast NewsScape data. Chuang et al (2020) trained an LDL model on the audio files of the MALD database (Tucker et al, 2017), and used this model to predict the acoustic durations and auditory lexical decision latencies to the auditory nonwords in this database.…”

“…A final challenge that we address in this study is whether further optimization of model performance is possible by enhancing the representation of words' meanings. Whereas models such as Distributed Cohort Model and Earshot assign randomlygenerated semantic representations to words, and Diana uses localist representations for word meanings, Baayen et al (2019) and Chuang et al (2020) made use of semantic vectors (aka word embeddings, see Gaskell and Marslen-Wilson, 1999, for a similar approach) derived from the TASA corpus (Ivens and Koslin, 1991;Landauer et al, 1998) using the algorithm described in Baayen et al (2019Baayen et al ( , 2016a. 4 The TASA corpus, which with 10 million words is very small compared to the volumes of texts that standard methods from machine learning such as word2vec (Mikolov et al, 2013) are trained on (typically billions of words).…”