Skip to content Skip to navigation
University of Warwick
  • Study
  • |
  • Research
  • |
  • Business
  • |
  • Alumni
  • |
  • News
  • |
  • About

University of Warwick
Publications service & WRAP

Highlight your research

  • WRAP
    • Home
    • Search WRAP
    • Browse by Warwick Author
    • Browse WRAP by Year
    • Browse WRAP by Subject
    • Browse WRAP by Department
    • Browse WRAP by Funder
    • Browse Theses by Department
  • Publications Service
    • Home
    • Search Publications Service
    • Browse by Warwick Author
    • Browse Publications service by Year
    • Browse Publications service by Subject
    • Browse Publications service by Department
    • Browse Publications service by Funder
  • Help & Advice
University of Warwick

The Library

  • Login
  • Admin

Why higher working memory capacity may help you learn : sampling, search, and degrees of approximation

Tools
- Tools
+ Tools

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.

[img]
Preview
PDF
WRAP-Why-higher-working-memory-learn-Sanborn-2019.pdf - Accepted Version - Requires a PDF viewer.

Download (3252Kb) | Preview
Official URL: https://doi.org/10.1111/cogs.12805

Request Changes to record.

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
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:
DateEvent
December 2019Published
15 December 2019Available
15 November 2019Accepted
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:
Project/Grant IDRIOXX Funder NameFunder ID
UNSPECIFIED[EPSRC] Engineering and Physical Sciences Research Councilhttp://dx.doi.org/10.13039/501100000266
ES/K004948/1 [ESRC] Economic and Social Research Councilhttp://dx.doi.org/10.13039/501100000269
UNSPECIFIED[RS] Royal Societyhttp://dx.doi.org/10.13039/501100000288
EP/I032622/1[ESRC] Economic and Social Research Councilhttp://dx.doi.org/10.13039/501100000269
Related URLs:
  • Publisher

Request changes or add full text files to a record

Repository staff actions (login required)

View Item View Item

Downloads

Downloads per month over past year

View more statistics

twitter

Email us: wrap@warwick.ac.uk
Contact Details
About Us