Quantifying curiosity: A formal approach to dissociating causes of curiosity.

Citation:

Liquin, E. G., Callaway, F., & Lombrozo, T. (2020). Quantifying curiosity: A formal approach to dissociating causes of curiosity. Proceedings of the 42nd Annual Conference of the Cognitive Science Society , Cognitive Science Society.
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Abstract:

Curiosity motivates exploration and is beneficial for learning, but curiosity is not always experienced when facing the unknown. In the present research, we address this selectivity: what causes curiosity to be experienced under some circumstances but not others? Using a Bayesian reinforcement learning model, we disentangle four possible influences on curiosity that have typically been confounded in previous research: surprise, local uncertainty/expected information gain, global uncertainty, and global expected information gain. In two experiments, we find that backward-looking influences (concerning beliefs based on prior experience) and forward-looking influences (concerning expectations about future learning) independently predict reported curiosity, and that forward-looking influences explain the most variance. These findings begin to disentangle the complex environmental features that drive curiosity.

Last updated on 10/20/2020