Cookies on this website

We use cookies to ensure that we give you the best experience on our website. If you click 'Accept all cookies' we'll assume that you are happy to receive all cookies and you won't see this message again. If you click 'Reject all non-essential cookies' only necessary cookies providing core functionality such as security, network management, and accessibility will be enabled. Click 'Find out more' for information on how to change your cookie settings.

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.

Original publication




Journal article

Publication Date