How trial-to-trial learning shapes mappings in the mental lexicon

Modelling lexical decision with linear discriminative learning

Bibliographic Data

ID21307448
AuthorsMaria Heitmeier (0000-0002-6515-7450, University of Tübingen, corresponding author), Yu-Ying Chuang, Yu‐ying Chuang (0000-0002-2733-2748, National Taiwan Normal University), R Harald Baayen (0000-0003-3178-3944, University of Tübingen)
Year2023
Volume146
Pages101598
Publication date2023-11-01
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueCognitive Psychology (JOURNAL)
Journal identifiersISSN: 0010-0285 • E-ISSN: 1095-5623
PublisherElsevier BV (PUBLISHER)
DOI10.1016/j.cogpsych.2023.101598
PMID37716109
OpenAlexW4386790829
LanguageEN
Citations received8
References cited120

Trial-to-trial effects have been found in a number of studies, indicating that processing a stimulus influences responses in subsequent trials. A special case are priming effects which have been modelled successfully with error-driven learning (Marsolek, 2008), implying that participants are continuously learning during experiments. This study investigates whether trial-to-trial learning can be detected in an unprimed lexical decision experiment. We used the Discriminative Lexicon Model (DLM; Baayen et al., 2019), a model of the mental lexicon with meaning representations from distributional semantics, which models error-driven incremental learning with the Widrow-Hoff rule. We used data from the British Lexicon Project (BLP; Keuleers et al., 2012) and simulated the lexical decision experiment with the DLM on a trial-by-trial basis for each subject individually. Then, reaction times were predicted with Generalized Additive Models (GAMs), using measures derived from the DLM simulations as predictors. We extracted measures from two simulations per subject (one with learning updates between trials and one without), and used them as input to two GAMs. Learning-based models showed better model fit than the non-learning ones for the majority of subjects. Our measures also provide insights into lexical processing and individual differences. This demonstrates the potential of the DLM to model behavioural data and leads to the conclusion that trial-to-trial learning can indeed be detected in unprimed lexical decision. Our results support the possibility that our lexical knowledge is subject to continuous changes

Cognition · Cognitive psychology · Discriminative model · Lexical decision task · Lexicon · Machine learning · Mental lexicon · Natural language processing · Priming (agriculture) · Artificial Intelligence · Categorization, perception, and language · Computer Science · Neurobiology of Language and Bilingualism · Psychology · Reading and Literacy Development

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