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Trial-to-trial effects have been found in a number of studies, indicatingthat processing a stimulus influences responses in subsequent trials. A specialcase are priming effects which have been modelled successfully witherror-driven learning (Marsolek, 2008), implying that participants arecontinuously learning during experiments. This study investigates whethertrial-to-trial learning can be detected in an unprimed lexical decisionexperiment. We used the Discriminative Lexicon Model (DLM; Baayen et al.,2019), a model of the mental lexicon with meaning representations fromdistributional semantics, which models error-driven incremental learning withthe Widrow-Hoff rule. We used data from the British Lexicon Project (BLP;Keuleers et al., 2012) and simulated the lexical decision experiment with theDLM on a trial-by-trial basis for each subject individually. Then, reactiontimes were predicted with Generalised Additive Models (GAMs), using measuresderived from the DLM simulations as predictors. We extracted measures from twosimulations per subject (one with learning updates between trials and onewithout), and used them as input to two GAMs. Learning-based models showedbetter model fit than the non-learning ones for the majority of subjects. Ourmeasures also provide insights into lexical processing and individualdifferences. This demonstrates the potential of the DLM to model behaviouraldata and leads to the conclusion that trial-to-trial learning can indeed bedetected in unprimed lexical decision. Our results support the possibility thatour lexical knowledge is subject to continuous changes.

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