A resource-rational theory of set size effects in human visual working memory
Encoding precision in visual working memory decreases with the number of encoded items. Here, we propose a normative theory for such set size effects: the brain minimizes a weighted sum of an error-based behavioral cost and a neural encoding cost. We construct a model from this theory and find that it predicts set size effects. Notably, these effects are mediated by probing probability, which aligns with previous empirical findings. The model accounts well for effects of both set size and probing probability on encoding precision in nine delayed-estimation experiments. Moreover, we find support for the prediction that the total amount of invested resource can vary non-monotonically with set size. Finally, we show that it is sometimes optimal to encode only a subset or even none of the relevant items in a task. Our findings raise the possibility that cognitive 'limitations' arise from rational cost minimization rather than from constraints.
Data from experiments E1-E7 (Table 1) and Matlab code for model fitting and simulations are available at http://dx.doi.org/10.5061/dryad.nf5dr6c.
Data from: A resource-rational theory of set size effects in human visual working memoryAvailable at Dryad Digital Repository under a CC0 Public Domain Dedication.
Article and author information
National Institutes of Health (R01EY020958)
- Wei Ji Ma
- Ronald van den Berg
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
- Stephanie Palmer, University of Chicago, United States
- Received: January 10, 2018
- Accepted: July 28, 2018
- Accepted Manuscript published: August 7, 2018 (version 1)
- Version of Record published: August 27, 2018 (version 2)
© 2018, van den Berg & Ma
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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