Quantifying Curiosity: A Formal Approach to Dissociating Causes of Curiosity

Emily G. Liquin, Frederick Callaway, Tania Lombrozo

Research output: Contribution to conferencePaperpeer-review

2 Scopus citations

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.

Original languageEnglish (US)
Pages309-315
Number of pages7
StatePublished - 2020
Event42nd Annual Meeting of the Cognitive Science Society: Developing a Mind: Learning in Humans, Animals, and Machines, CogSci 2020 - Virtual, Online
Duration: Jul 29 2020Aug 1 2020

Conference

Conference42nd Annual Meeting of the Cognitive Science Society: Developing a Mind: Learning in Humans, Animals, and Machines, CogSci 2020
CityVirtual, Online
Period7/29/208/1/20

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Science Applications
  • Human-Computer Interaction
  • Cognitive Neuroscience

Keywords

  • curiosity
  • expected information gain
  • learning
  • surprise
  • uncertainty

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