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Why higher working memory capacity may help you learn : sampling, search, and degrees of approximation
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Lloyd, Kevin, Sanborn, Adam N., Leslie, David and Lewandowsky, Stephan (2019) Why higher working memory capacity may help you learn : sampling, search, and degrees of approximation. Cognitive Science, 43 (12). e12805. doi:10.1111/cogs.12805 ISSN 0364-0213.
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Official URL: https://doi.org/10.1111/cogs.12805
Abstract
Algorithms for approximate Bayesian inference, such as those based on sampling (i.e., Monte Carlo methods), provide a natural source of models of how people may deal with uncertainty with limited cognitive resources. Here, we consider the idea that individual differences in working memory capacity (WMC) may be usefully modeled in terms of the number of samples, or “particles,” available to perform inference. To test this idea, we focus on two recent experiments that report positive associations between WMC and two distinct aspects of categorization performance: the ability to learn novel categories, and the ability to switch between different categorization strategies (“knowledge restructuring”). In favor of the idea of modeling WMC as a number of particles, we show that a single model can reproduce both experimental results by varying the number of particles—increasing the number of particles leads to both faster category learning and improved strategy‐switching. Furthermore, when we fit the model to individual participants, we found a positive association between WMC and best‐fit number of particles for strategy switching. However, no association between WMC and best‐fit number of particles was found for category learning. These results are discussed in the context of the general challenge of disentangling the contributions of different potential sources of behavioral variability.
| Item Type: | Journal Article | |||||||||||||||
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| Subjects: | B Philosophy. Psychology. Religion > BF Psychology | |||||||||||||||
| Divisions: | Faculty of Science, Engineering and Medicine > Science > Psychology | |||||||||||||||
| Library of Congress Subject Headings (LCSH): | Short-term memory, Memory, Bayesian statistical decision theory | |||||||||||||||
| Journal or Publication Title: | Cognitive Science | |||||||||||||||
| Publisher: | Wiley | |||||||||||||||
| ISSN: | 0364-0213 | |||||||||||||||
| Official Date: | December 2019 | |||||||||||||||
| Dates: |
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| Volume: | 43 | |||||||||||||||
| Number: | 12 | |||||||||||||||
| Article Number: | e12805 | |||||||||||||||
| DOI: | 10.1111/cogs.12805 | |||||||||||||||
| Status: | Peer Reviewed | |||||||||||||||
| Publication Status: | Published | |||||||||||||||
| Reuse Statement (publisher, data, author rights): | "This is the peer reviewed version of the following article: [FULL CITE], which has been published in final form at [Link to final article using the DOI]. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions." | |||||||||||||||
| Access rights to Published version: | Restricted or Subscription Access | |||||||||||||||
| Date of first compliant deposit: | 21 November 2019 | |||||||||||||||
| Date of first compliant Open Access: | 21 November 2019 | |||||||||||||||
| RIOXX Funder/Project Grant: |
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