Why Higher Working Memory Capacity May Help You Learn: Sampling, Search, and Degrees of Approximation.
Approximate Bayesian inference
Category learning
Knowledge partitioning
Particle filtering
Strategy switching
Working memory
Journal
Cognitive science
ISSN: 1551-6709
Titre abrégé: Cogn Sci
Pays: United States
ID NLM: 7708195
Informations de publication
Date de publication:
12 2019
12 2019
Historique:
received:
11
02
2018
revised:
05
09
2019
accepted:
07
11
2019
entrez:
21
12
2019
pubmed:
21
12
2019
medline:
3
10
2020
Statut:
ppublish
Résumé
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.
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
e12805Informations de copyright
© 2019 The Authors. Cognitive Science published by Wiley Periodicals, Inc. on behalf of Cognitive Science Society (CSS).
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