Learning Type I and Type II regularities between multiple sequentially presented stimulus categories.

Classification is a common cognitive task, which requires assigning objects or events to categories based on shared features or rules (e.g., red objects are fruit, brown objects are mushrooms). In everyday scenarios, however, objects usually belong to more than one category (e.g., red objects can also be classified as edible, and brown objects could be poisonous). This study investigates whether humans can learn corresponding regularities between outcomes of such multiple categorizations when performed in a series of decisions for each stimulus. We therefore translated classical category learning designs, known as Type I (one-dimensional rule) and Type II (disjunctive rule), into a temporal context. We compared these cases to conditions in which no correlations existed between the series of categorization outcomes, and only the visual stimulus predicted each category outcome. Besides the structural complexity, we also tested in Type I scenarios whether learning and generalization were moderated by the temporal proximity of the successive decisions (adjacent vs. non-adjacent categorizations). The results show that participants can abstract away from the visual stimulus with a temporal Type I regularity, but there was no evidence for a corresponding effect with a temporal Type II regularity. The role of adjacency was not clear-cut, but there was no strong evidence favoring stronger performance with adjacent relative to non-adjacent categorizations. We discuss these findings before the background of category- and artificial grammar-learning research, and expand on potential moderating factors such as the cognitive effort of keeping the necessary amount of information in working memory and the modality of category predictors when determining whether people will extract rules or rely on memory-based learning.