The attention schema theory in a neural network agent: Controlling visuospatial attention using a descriptive model of attention
Open accessAbstract
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.
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Two notable studies have implemented simple attention schemas in artificial systems.
“Two notable studies have implemented simple attention schemas in artificial systems.”
From Consciousness in Artificial Intelligence: Insights from the Science of Consciousness (2023) · cites this paper as Wilterson and Graziano's (2021)
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BackgroundMethodUnexpected benefits of self-modelling in neural systems
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Self-citation
- Authors
- Wilterson, A. I., & Graziano, M. S. A.
- Affiliations
- Princeton University (US)
- Corresponding author
- Graziano, Michael S. A.
- Venue
- Proceedings of the National Academy of Sciences
- Publisher
- National Academy of Sciences
- Published
- 2021-08-12
- Volume
- 118
- Issue
- 33
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- PMC
- PMC8379943
- OpenAlex
- W3127825725
- Cited by
- 26
- References
- 81
- Citations by year
- 2026: 3 · 2025: 3 · 2024: 6 · 2023: 5 · 2022: 8 · 2021: 1
- FWCI
- 1.80
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Proceedings of the National Academy of Sciences (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Visual Attention and Saliency Detection
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Philosophy and Theoretical Science
- Keywords
- attention schema theory, visuospatial attention, deep Q-learning, computational modeling of attention
- MeSH terms
- Deep Learning, Attention, Neural Networks, Computer, Spatial Processing
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