Computational mechanisms of curiosity and goal-directed exploration

TLDR

Successful behaviour depends on the right balance between maximising reward and soliciting information about the world. The study demonstrates how casting behaviour as surprise minimisation reveals two distinct mechanisms of goal‑directed exploration that produce separable profiles of active sampling to reduce uncertainty. Hidden‑state exploration drives agents to sample unambiguous observations to infer the world’s hidden state, while model‑parameter exploration compels sampling of outcomes with high uncertainty that are informative about task structure. The authors illustrate that active inference and active learning emerge as distinct forms of exploration, each inducing unique patterns of Bayes‑optimal behaviour, thereby offering a computational framework for how varying uncertainty levels shape the exploration‑exploitation trade‑off.

Abstract

Successful behaviour depends on the right balance between maximising reward and soliciting information about the world. Here, we show how different types of information-gain emerge when casting behaviour as surprise minimisation. We present two distinct mechanisms for goal-directed exploration that express separable profiles of active sampling to reduce uncertainty. 'Hidden state' exploration motivates agents to sample unambiguous observations to accurately infer the (hidden) state of the world. Conversely, 'model parameter' exploration, compels agents to sample outcomes associated with high uncertainty, if they are informative for their representation of the task structure. We illustrate the emergence of these types of information-gain, termed active inference and active learning, and show how these forms of exploration induce distinct patterns of 'Bayes-optimal' behaviour. Our findings provide a computational framework for understanding how distinct levels of uncertainty systematically affect the exploration-exploitation trade-off in decision-making.