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. Conversely, ‘model parameter’ exploration, compels agents to sample outcomes associated with high uncertainty, if they are informative for their representation of the task structure. ‘Hidden state’ exploration motivates agents to sample unambiguous observations to accurately infer the (hidden) state of the world. We present two distinct mechanisms for goal-directed exploration that express separable profiles of active sampling to reduce uncertainty. Here, we show how different types of information-gain emerge when casting behaviour as surprise minimisation. Successful behaviour depends on the right balance between maximising reward and soliciting information about the world.
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