Actively learning to learn causal relationships
This addresses how humans improve long-term learning efficiency in causal reasoning, but it is incremental as it builds on existing hierarchical Bayesian models by adding overhypotheses.
The paper tackled the problem of how people actively learn causal relationships by exploring whether they pursue information about abstract causal overhypotheses across multiple situations. The results from two experiments with 14 manipulations showed that people learn and transfer these overhypotheses to facilitate long-term active learning, supported by model comparisons and behavioral trends.
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$\unicode{x2014}$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 trends 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 about these similarities. Moreover, people exploit these overhypotheses to facilitate long-term active learning.