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Rewards teach visual selective attention
Open accessAbstract
Visual selective attention is the brain function that modulates ongoing processing of retinal input in order for selected representations to gain privileged access to perceptual awareness and guide behavior. Enhanced analysis of currently relevant or otherwise salient information is often accompanied by suppressed processing of the less relevant or salient input. Recent findings indicate that rewards exert a powerful influence on the deployment of visual selective attention. Such influence takes different forms depending on the specific protocol adopted in the given study. In some cases, the prospect of earning a larger reward in relation to a specific stimulus or location biases attention accordingly in order to maximize overall gain. This is mediated by an effect of reward acting as a type of incentive motivation for the strategic control of attention. In contrast, reward delivery can directly alter the processing of specific stimuli by increasing their attentional priority, and this can be measured even when rewards are no longer involved, reflecting a form of reward-mediated attentional learning. As a further development, recent work demonstrates that rewards can affect attentional learning in dissociable ways depending on whether rewards are perceived as feedback on performance or instead are registered as random-like events occurring during task performance. Specifically, it appears that visual selective attention is shaped by two distinct reward-related learning mechanisms: one requiring active monitoring of performance and outcome, and a second one detecting the sheer association between objects in the environment (whether attended or ignored) and the more-or-less rewarding events that accompany them. Overall this emerging literature demonstrates unequivocally that rewards "teach" visual selective attention so that processing resources will be allocated to objects, features and locations which are likely to optimize the organism's interaction with the surrounding environment and maximize positive outcome.
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“A distinction between both has been demonstrated in behavioral studies (Della Libera & Chelazzi, 2009; Le Pelley et al., 2015; see also Chelazzi et al., 2013)”
From Selection history: How reward modulates selectivity of visual attention. (2018) · cites this paper as Chelazzi et al., 2013
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Metadata
- Authors
- Chelazzi, L., Perlato, A., Santandrea, E., & Della Libera, C.
- Affiliations
- University of Verona (IT)
- Corresponding author
- Chelazzi, Leonardo
- Venue
- Vision Research
- Publisher
- Elsevier BV
- Published
- 2013-06
- Volume
- 85
- Pages
- 58-72
- ISSN
- 0042-6989 (ISSN-L)
- OpenAlex
- W2105590614
- Cited by
- 354
- References
- 145
- Citations by year
- 2026: 14 · 2025: 15 · 2024: 12 · 2023: 23 · 2022: 24 · 2021: 45 · 2020: 34 · 2019: 25 · 2018: 36 · 2017: 28 · 2016: 32 · 2015: 30 · 2014: 27 · 2013: 7
- FWCI
- 1.62
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- www.elsevier.com/legal/tdmrep-license (tdm)
- www.elsevier.com/open-access/userlicense/1.0/ (vor, 554 d)
- doi.org/10.15223/policy-017 (stm-asf)
- doi.org/10.15223/policy-037 (stm-asf)
- doi.org/10.15223/policy-012 (stm-asf)
- doi.org/10.15223/policy-029 (stm-asf)
- doi.org/10.15223/policy-004 (stm-asf)
- Locations
- Vision Research (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Fondazione Cassa di Risparmio di Verona Vicenza Belluno e Ancona
- 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
- visual selective attention, reward, incentive motivation, attentional learning, reward feedback, attentional priority, stimulus salience, performance monitoring, attentional control
- MeSH terms
- Association Learning, Attention, Humans, Reward, Visual Perception
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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