How does working memory store more information at larger set sizes? A composite code model.

A central feature of working memory is its limited capacity in terms of the amount of information that can be simultaneously maintained. Despite this, many studies observe an increase in the total amount when more items are maintained (set size), as measured by Shannon information. We propose the composite code model which maintains this fixed capacity assumption but demonstrates increasing observed information across set sizes. This relies on the hierarchical organisation of the visual system, in which higher-order information is abstracted about simple study displays. Using Bayesian inference, target responses can be inferred from knowledge about non-targets. We tested this model against our own data from a delayed reproduction task and those of published open data sets. We found initial support for the model, with its predictions matching those of the observed effects.