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

ABSTRACT: 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 o

SUBMITTER: Shah VB 

PROVIDER: S-EPMC12446112 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature