CoChat
Sign Up
Cited by 354Visual perception and processing mechanismsNeural and Behavioral Psychology StudiesVisual Attention and Saliency Detection

The DOI matches its Crossref record; 148 of the 148 references it cites were found in a registry or an index.

Rewards teach visual selective attention

Open access
Chelazzi, Leonardo, Perlato, Andrea, Santandrea, Elisa, Della Libera, Chiara·2013·Vision Research
DOI 10.1016/j.visres.2012.12.005
Open this paper in CoChat — free account

Abstract

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.

Overview

Integrity signals

Registry

Crossref · 10.1016/j.visres.2012.12.005

Cites

148 of 148 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Verification

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Citations

Cites

Flagged first, then by citations

Show all 145 references

Cited by

Sorted by citations

Items 101–200 of 354

Related

See all

Metadata

Authors
Chelazzi, L., Perlato, A., Santandrea, E., & Della Libera, C.
Affiliations
University of Verona (IT)
Corresponding author
Chelazzi, Leonardo
ORCID
Venue
Vision Research
Publisher
Elsevier BV
Published
2013-06
Volume
85
Pages
58-72
ISSN
0042-6989 (ISSN-L)
DOI
10.1016/j.visres.2012.12.005
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
Licence
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 ›

Are you an author, or do you see a problem with this page? Request a review