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  Why Higher Working Memory Capacity May Help You Learn: Sampling, Search, and Degrees of Approximation

Lloyd, K., Sanborn, A., Leslie, D., & Lewandowsky, S. (2019). Why Higher Working Memory Capacity May Help You Learn: Sampling, Search, and Degrees of Approximation. Cognitive Science, 43(12), 1-43. doi:10.1111/cogs.12805.

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Item Permalink: http://hdl.handle.net/21.11116/0000-0005-64FE-D Version Permalink: http://hdl.handle.net/21.11116/0000-0005-64FF-C
Genre: Journal Article

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Lloyd, K1, 2, Author              
Sanborn, A, Author
Leslie, D, Author
Lewandowsky, S, Author
Affiliations:
1Department of Computational Neuroscience, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_3017468              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 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.

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 Dates: 2019-12
 Publication Status: Published in print
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 Identifiers: DOI: 10.1111/cogs.12805
eDoc: e12805
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Title: Cognitive Science
Source Genre: Journal
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Publ. Info: Kidlington, Oxford, UK [etc.] : No longer published by Elsevier
Pages: - Volume / Issue: 43 (12) Sequence Number: - Start / End Page: 1 - 43 Identifier: ISSN: 0364-0213
CoNE: https://pure.mpg.de/cone/journals/resource/954925523741