Abstract
Understanding how people generalize and extrapolate from limited amounts of data remains an outstanding challenge. We study this question in the domain of scalar function learning, and propose a simple model based on the Principle of Maximum Entropy (Jaynes, 1957). Through computational modeling, we demonstrate that the theory makes two specific predictions about peoples' extrapolation judgments, that we validate through experiments. Moreover, we show that existing Gaussian Process models of function learning cannot account for these effects.
| Original language | English (US) |
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| Pages | 1462-1468 |
| Number of pages | 7 |
| 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 |
All Science Journal Classification (ASJC) codes
- Artificial Intelligence
- Computer Science Applications
- Human-Computer Interaction
- Cognitive Neuroscience
Keywords
- Computational Modeling
- Learning
- Pattern recognition