A unified selection signal for attention and reward in primary visual cortex
Open accessAbstract
Stimuli associated with high rewards evoke stronger neuronal activity than stimuli associated with lower rewards in many brain regions. It is not well understood how these reward effects influence activity in sensory cortices that represent low-level stimulus features. Here, we investigated the effects of reward information in the primary visual cortex (area V1) of monkeys. We found that the reward value of a stimulus relative to the value of other stimuli is a good predictor of V1 activity. Relative value biases the competition between stimuli, just as has been shown for selective attention. The neuronal latency of this reward value effect in V1 was similar to the latency of attentional influences. Moreover, V1 neurons with a strong value effect also exhibited a strong attention effect, which implies that relative value and top-down attention engage overlapping, if not identical, neuronal selection mechanisms. Our findings demonstrate that the effects of reward value reach down to the earliest sensory processing levels of the cerebral cortex and imply that theories about the effects of reward coding and top-down attention on visual representations should be unified.
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The AGREL networks and variants like KickBack give a way to compute an approximation to the full gradient in a reinforcement learning context using a feedback-based attention mechanism for credit assignment within the multi-layer network.
“The “attention-gated reinforcement learning” (AGREL) networks of Stanisor et al. (2013), Brosch et al. (2015), and Roelfsema and van Ooyen (2005), and variants like KickBack (Balduzzi, 2014), give a way to compute an approximation to the full gradient in a reinforcement learning context using a feedback-based attention mechanism for credit assignment within the multi-layer network.”
From Toward an integration of deep learning and neuroscience (2016) · cites this paper as Stanisor et al., 2013
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- Authors
- Stănişor, L., van der Togt, C., Pennartz, C. M. A., & Roelfsema, P. R.
- Affiliations
- Netherlands Institute for Neuroscience (NL)
- Department of Vision and Cognition, Netherlands Institute for Neuroscience, Royal Netherlands Academy of Arts and Sciences, 1105 BA Amsterdam, The Netherlands;
- University of Amsterdam (NL)
- Venue
- Proceedings of the National Academy of Sciences
- Publisher
- National Academy of Sciences
- Published
- 2013-05-15
- Volume
- 110
- Issue
- 22
- Pages
- 9136-9141
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- PMC
- PMC3670348
- OpenAlex
- W2128585328
- Cited by
- 158
- References
- 49
- Citations by year
- 2026: 5 · 2025: 6 · 2024: 10 · 2023: 10 · 2022: 13 · 2021: 10 · 2020: 15 · 2019: 11 · 2018: 16 · 2017: 12 · 2016: 15 · 2015: 22 · 2014: 12 · 2013: 1
- FWCI
- 9.11
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Proceedings of the National Academy of Sciences (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- UvA-DARE (University of Amsterdam) (publishedVersion)
- KNAW Research Portal (The Royal Netherlands Academy of Arts and Sciences) (publishedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion)
- Pure Amsterdam UMC (submittedVersion)
- VU Research Portal (submittedVersion)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Visual perception and processing mechanisms
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Visual Attention and Saliency Detection
- Keywords
- primary visual cortex, reward value, top-down attention, relative value, stimulus competition, attentional modulation, reward coding, visual representation, neuronal latency, monkeys
- MeSH terms
- Action Potentials, Analysis of Variance, Animals, Haplorhini, Attention, Color, Cues, Psychomotor Performance, Reaction Time, Reward, Saccades, Visual Cortex
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