Abstract
Question asking is a powerful means by which humans learn. However, asking a question requires searching through a massive space of possible questions to find a single question that is relevant and informative. How do humans efficiently accomplish this task? Drawing on prior research on other decision problems, we propose that the search for new questions is constrained by those encountered in the past, so that people frequently reuse questions (or parts of questions) rather than generating new questions “from scratch.” We find empirical support for this prediction, and we find that this “question reuse” has consequences for the informational value of people's questions. Taken together, this research sheds new light on the mechanisms behind human question asking abilities and, more generally, how we narrow down a large space of possibilities to find a single solution.
Original language | English (US) |
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Pages | 1160-1167 |
Number of pages | 8 |
State | Published - 2022 |
Event | 44th Annual Meeting of the Cognitive Science Society: Cognitive Diversity, CogSci 2022 - Toronto, Canada Duration: Jul 27 2022 → Jul 30 2022 |
Conference
Conference | 44th Annual Meeting of the Cognitive Science Society: Cognitive Diversity, CogSci 2022 |
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Country/Territory | Canada |
City | Toronto |
Period | 7/27/22 → 7/30/22 |
Keywords
- active learning
- expected information gain
- information search
- question asking
ASJC Scopus subject areas
- Artificial Intelligence
- Computer Science Applications
- Human-Computer Interaction
- Cognitive Neuroscience