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.

该研究探讨试验中单次学习效应对无提示词汇决策的影响，使用 DLM 模型和 GAM 预测反应时间，发现基于学习的模型在大多数实验中有更好的拟合效果。结果表明，试验中的单次学习可以被探测到，说明我们的词汇知识会不断变化。

试次学习如何塑造心理词汇表中的映射：用线性判别学习建模词汇决策