When people make decisions, these are often sequential, and later decisions frequently depend on the outcome of the previous decision. For instance, a doctor will first decide on a patient's treatment and then its duration. Grammar learning shows that humans can learn temporal regularities between such sequential information. However, little is known about sequential category learning tasks. Here, we investigate learning of regularities between two categorization tasks and generalization to novel objects. In an experiment, we varied whether a contingency between the outcome of two categorization tasks existed and whether they were adjacent (the tasks followed each other) or non-adjacent (an intervening task in between). Participants learned to categorize the objects of the second task in adjacent condition better than other conditions. However, both regularity conditions were beneficial to categorize novel objects (generalization). The results show the importance of considering temporal regularities between decision tasks in theories of category learning.

When people make decisions, these often do not stand alone but are made in a sequence of decisions. For instance, a doctor will first decide on a patient's treatment and then about the duration of the treatment, such that later decisions frequently depend on the outcome of the first decision. While there is research on how humans discover inter-relations between sequentially presented information (e.g., grammar), little is known about learning complex inter-category relations in decision sequences. Hence, we present an experiment in which we embedded an Exclusive-Or (Type-II) structure, as known in category learning, in a sequence of three categorization tasks where the outcomes of tasks 1 and 2 predicted the outcome of task 3. We hypothesized that embedded structure would facilitate learning and generalization compared to sequence without regularity. Instead, the evidence favored the Null hypothesis in both cases, contrasting with the findings in the visual categorization domain.