Actively learning to learn causal relationships

TL;DR

This paper introduces a hierarchical Bayesian model for active causal learning, showing that people learn and transfer overhypotheses about causal relationships across different situations, thereby enhancing long-term learning.

Contribution

It proposes a novel hierarchical Bayesian model that accounts for how people learn and transfer overhypotheses in active causal learning tasks, extending previous models.

Findings

Participants learn and transfer overhypotheses across situations.

The model accurately predicts participant behavior in experiments.

People use overhypotheses to facilitate long-term active learning.

Abstract

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.