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：多项研究发现逐次效应，表明处理某个刺激会影响后续试验中的反应。一个特例是启动效应，该效应已通过误差驱动学习成功建模（Marsolek, 2008），这意味着参与者在实验过程中持续学习。本研究旨在探究在无启动的词汇决策实验中能否检测到逐次学习。我们使用了判别性词汇模型（Discriminative Lexicon Model, DLM；Baayen等人，2019），该模型是一个心理词典模型，包含来自分布语义学的意义表征，并通过Widrow-Hoff规则实现误差驱动的增量学习。我们采用了英国词汇项目（British Lexicon Project, BLP；Keuleers等人，2012）的数据，并针对每个受试者以逐次试验方式，使用DLM模拟词汇决策实验。随后，利用广义加性模型（Generalised Additive Models, GAMs），以DLM模拟中提取的指标作为预测变量来预测反应时。我们为每个受试者进行了两次模拟（一次包含试验间的学习更新，另一次不包含），并从中提取指标作为两个GAMs的输入。对于大多数受试者，基于学习的模型比非学习模型表现出更好的拟合度。我们的指标还揭示了词汇加工和个体差异。这展示了DLM在模拟行为数据方面的潜力，并得出结论：在无启动的词汇决策中确实能检测到逐次学习。我们的结果支持词汇知识可能处于持续变化中的这一观点。