LAUSR

Significance We demonstrate that three of the most prominent accounts of visual working memory in the psychology and neuroscience literature—the slots+averaging model, the variable precision model, and the population coding model—can all be expressed in the common mathematical framework of sampling. This reformulation allows us to pinpoint the key differences between these models, and to determine which factors are critical to account for the observed patterns of recall errors across different human psychophysical experiments. Moreover, the sampling framework provides a possible neural grounding for these models in the spiking activity of neuronal populations, as well as a link to existing theories of capacity limits in visual attention. 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.