Open AccessStochastic sampling provides a unifying account of visual working memory limits
Author(s) -
Sebastian Schneegans,
Robert Taylor,
Paul M. Bays
Publication year - 2020
Publication title -
proceedings of the national academy of sciences of the united states of america
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 5.011
H-Index - 771
eISSN - 1091-6490
pISSN - 0027-8424
DOI - 10.1073/pnas.2004306117
Subject(s) - computer science , limit (mathematics) , sampling (signal processing) , probabilistic logic , working memory , coding (social sciences) , point process , property (philosophy) , theoretical computer science , point (geometry) , artificial intelligence , mathematics , statistics , psychology , cognition , filter (signal processing) , computer vision , mathematical analysis , philosophy , geometry , epistemology , neuroscience
Research into human working memory limits has been shaped by the competition between different formal models, with a central point of contention being whether internal representations are continuous or discrete. Here we describe a sampling approach derived from principles of neural coding as a framework to understand working memory limits. Reconceptualizing existing models in these terms reveals strong commonalities between seemingly opposing accounts, but also allows us to identify specific points of difference. We show that the discrete versus continuous nature of sampling is not critical to model fits, but that, instead, random variability in sample counts is the key to reproducing human performance in both single- and whole-report tasks. A probabilistic limit on the number of items successfully retrieved is an emergent property of stochastic sampling, requiring no explicit mechanism to enforce it. These findings resolve discrepancies between previous accounts and establish a unified computational framework for working memory that is compatible with neural principles.