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.

翻译：多项研究发现试验间效应，表明处理一个刺激会影响后续试验中的反应。启动效应是其中的一个特例，已成功通过误差驱动学习建模（Marsolek, 2008），这意味着参与者在实验过程中持续学习。本研究探讨在无启动的词汇判断实验中能否检测到试验间学习。我们使用判别性词典模型（DLM；Baayen 等人，2019），该模型采用分布语义学中的意义表征来构建心理词典，并通过Widrow-Hoff规则模拟误差驱动的增量学习。我们使用英国词汇项目（BLP；Keuleers 等人，2012）的数据，针对每个被试逐试验地使用DLM模拟词汇判断实验。然后，利用广义加性模型（GAM）预测反应时间，以DLM模拟得出的指标作为预测因子。我们从每个被试的两个模拟（一个包含试验间的学习更新，另一个不包含）中提取指标，并将其作为两个GAM的输入。对于大多数被试，基于学习的模型比非学习模型具有更好的拟合度。我们的指标还揭示了词汇加工和个体差异。这展示了DLM模拟行为数据的潜力，并得出结论：在无启动的词汇判断中确实可以检测到试验间学习。我们的结果支持了词汇知识可能持续变化的观点。