The DOI matches its Crossref record; 13 of the 13 references it cites were found in a registry or an index.
Curiosity and reward: Valence predicts choice and information prediction errors enhance learning.
Open accessAbstract
Curiosity drives many of our daily pursuits and interactions; yet, we know surprisingly little about how it works. Here, we harness an idea implied in many conceptualizations of curiosity: that information has value in and of itself. Reframing curiosity as the motivation to obtain reward-where the reward is information-allows one to leverage major advances in theoretical and computational mechanisms of reward-motivated learning. We provide new evidence supporting 2 predictions that emerge from this framework. First, we find an asymmetric effect of positive versus negative information, with positive information enhancing both curiosity and long-term memory for information. Second, we find that it is not the absolute value of information that drives learning but, rather, the gap between the reward expected and reward received, an "information prediction error." These results support the idea that information functions as a reward, much like money or food, guiding choices and driving learning in systematic ways.
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Cites
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Cited by
Towards a neuroscience of active sampling and curiosity
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A neural network for information seeking
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Neural circuitry of information seeking
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Interest: A unique affective and cognitive motivational variable that develops
(2022) · Advances in Motivation Science · doi:10.1016/bs.adms.2021.12.004 · Cited by 81
Lighting the wick in the candle of learning: generating a prediction stimulates curiosity
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Metadata
- Authors
- Marvin, C. B., & Shohamy, D.
- Venue
- Journal of Experimental Psychology: General
- Published
- 2016-03
- Volume
- 145
- Issue
- 3
- Pages
- 266-272
- ISSN
- 0096-3445 (ISSN-L)
- 1939-2222
- OpenAlex
- W2433308118
- Cited by
- 264
- References
- 13
- FWCI
- 23.29
- Type
- journal article
- Language
- en
- Open access
- hybrid
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
