Using Sequential Dependencies in Category Learning  Exploring the Role of Adjacency and Structure

Sequential decision-making is a common cognitive task where subsequent decisions often depend on the outcomes of earlier ones. While sequence learning research demonstrates humans' ability to learn regularities in sequentially presented information, investigations are sparse regarding complex decision-making tasks, such as category learning. This study connects both domains and explores whether individuals can detect and utilize regularities between sequential categorization task outcomes to enhance learning and categorize novel targets. For this, we extended a classical category learning paradigm (Study 1: Type I, Study 2: Type II category structures), where the outcome of one categorization task depends on the outcome(s) of previous tasks in a sequence. We compared performance in each study to a control condition without dependencies (Type VI). Connecting the design to sequential grammar learning, in Study 1, we further manipulated the adjacency of the relevant outcomes (consecutive or separated by an irrelevant task).The results of Study 1 showed that with a Type I dependency, participants learned the second task's outcome more rapidly than in the control condition. During the transfer phase, participants successfully applied the dependency to categorize novel targets in both adjacent and non-adjacent conditions. In contrast, in Study 2, we found no evidence of effects on learning or generalization of a Type II dependency, as performance was equal to the control condition. We discuss these findings from category learning and statistical learning perspectives and how investigations intersecting both domains can contribute to the broader understanding of complex sequential decision-making processes. We also highlight open questions for future research.