Abstract
Across the lifespan, humans direct their learning towards information they are curious to know. However, it is unclear what elicits curiosity, and whether and how this changes across development. Is curiosity triggered by surprise and uncertainty, as prior research suggests, or by expected learning, which is often confounded with these features? In the present research, we use a Bayesian reinforcement learning model to quantify and disentangle surprise, uncertainty, and expected learning. We use the resulting model-estimated features to predict curiosity ratings from 5- to 9-year-olds and adults in an augmented multi-armed bandit task. Like adults’ curiosity, children’s curiosity was best predicted by expected learning. However, after accounting for expected learning, children (but not adults) were also more curious when uncertainty was higher and surprise lower. This research points to developmental changes in what elicits curiosity and calls for a reexamination of research that confounds these elicitors.
Original language | English (US) |
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Pages | 1360-1366 |
Number of pages | 7 |
State | Published - 2021 |
Event | 43rd Annual Meeting of the Cognitive Science Society: Comparative Cognition: Animal Minds, CogSci 2021 - Virtual, Online, Austria Duration: Jul 26 2021 → Jul 29 2021 |
Conference
Conference | 43rd Annual Meeting of the Cognitive Science Society: Comparative Cognition: Animal Minds, CogSci 2021 |
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Country/Territory | Austria |
City | Virtual, Online |
Period | 7/26/21 → 7/29/21 |
All Science Journal Classification (ASJC) codes
- Cognitive Neuroscience
- Artificial Intelligence
- Computer Science Applications
- Human-Computer Interaction
Keywords
- curiosity
- development
- exploration
- learning