Actively Learning to Learn Causal Relationships

• Published in: Computational brain & behavior Vol. 7; no. 1; pp. 80 - 105
• Main Authors: Jiang, Chentian, Lucas, Christopher G.
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.03.2024
• Subjects: Behavioral Science and Psychology; Cognitive Psychology; Mathematical Models of Cognitive Processes and Neural Networks; Original Paper; Psychological Methods/Evaluation; Psychology; Overhypotheses; Causal learning; Generalization; Active learning; Transfer learning
• ISSN: 2522-0861; 2522-087X
• DOI: 10.1007/s42113-023-00195-0

How do people actively learn to learn? That is, how and when do people choose actions that facilitate long-term learning and choosing future actions that are more informative? We explore these questions in the domain of active causal learning. We propose a hierarchical Bayesian model that goes beyond past models by predicting that people pursue information not only about the causal relationship at hand but also about causal overhypotheses—abstract beliefs about causal relationships that span multiple situations and constrain how we learn the specifics in each situation. In two active “blicket detector” experiments with 14 between-subjects manipulations, our model was supported by both qualitative patterns in participant behavior and an individual differences-based model comparison. Our results suggest when there are abstract similarities across active causal learning problems, people readily learn and transfer overhypotheses reflecting these similarities. Moreover, people exploit these overhypotheses to facilitate long-term active learning.