  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 Generalised 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.
