English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Significance Attention, the deep processing of select items, is one of the most important cognitive operations in the brain. But how does the brain control its attention? One proposed part of the mechanism is that the brain builds a model, or attention schema, that helps monitor and predict the changing state of attention. Here, we show that an artificial neural network agent can be trained to control visual attention when it is given an attention schema, but its performance is greatly reduced when the schema is not available. We suggest that the brain may have evolved a model of attention because of the profound practical benefit for the control of attention.

The attention schema theory in a neural network agent: Controlling visuospatial attention using a descriptive model of attention