How trial-to-trial learning shapes mappings in the mental lexicon: Modelling Lexical Decision with Linear Discriminative Learning

Abstract

Priming and antipriming can be modelled with error-driven learning (Marsolek, 2008), by assuming that the learning of the prime influences processing of the target stimulus. This implies that participants are continuously learning in priming studies, and predicts that they are also learning in each trial of other psycholinguistic experiments. This study investigates whether trial-to-trial learning can be detected in lexical decision experiments. 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 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 for words and nonwords were predicted with Generalised Additive Models, using measures derived from the DLM simulations as predictors. Models were developed with the data of two subjects and tested on all other subjects. We extracted measures from two simulations for each 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 provided insights into lexical processing and enabled us to explore individual differences with Linear Mixed Models. 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 psycholinguistic experiments.

A meta-analytic review of morphological priming in Semitic languages

“…In contrast, in whole-word approaches, words are represented as unanalyzed sequences in the mental lexicon, without any internal structure (e.g., Blevins, 2016;Lukatela et al, 1980;Milin et al, 2017;Seidenberg & Gonnerman, 2000). In such models, two lexical representations with shared morphology are typically related by analogy (e.g., Bybee & McClelland, 2005) or overlap in meaning and form (Baayen et al, 2019;Gonnerman et al, 2007;Heitmeier et al, 2022). Thus, in whole-word models, although morphemes are not represented in the lexicon, effects of morphological relatedness emerge due to the fact that words that are morphologically related typically share meaning, and are similar in form.…”