Value-driven attentional capture
Open accessAbstract
Attention selects which aspects of sensory input are brought to awareness. To promote survival and well-being, attention prioritizes stimuli both voluntarily, according to context-specific goals (e.g., searching for car keys), and involuntarily, through attentional capture driven by physical salience (e.g., looking toward a sudden noise). Valuable stimuli strongly modulate voluntary attention allocation, but there is little evidence that high-value but contextually irrelevant stimuli capture attention as a consequence of reward learning. Here we show that visual search for a salient target is slowed by the presence of an inconspicuous, task-irrelevant item that was previously associated with monetary reward during a brief training session. Thus, arbitrary and otherwise neutral stimuli imbued with value via associative learning capture attention powerfully and persistently during extinction, independently of goals and salience. Vulnerability to such value-driven attentional capture covaries across individuals with working memory capacity and trait impulsivity. This unique form of attentional capture may provide a useful model for investigating failures of cognitive control in clinical syndromes in which value assigned to stimuli conflicts with behavioral goals (e.g., addiction, obesity).
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In order to learn a particular selection history bias, the stimuli involved must receive some attentionally prioritized processing either because they are task-relevant or stand out from the environment through their physical properties.
“In order to learn a particular selection history bias, the stimuli involved must receive some attentionally prioritized processing either because they are task-relevant (e.g., Anderson et al., 2011; Theeuwes & Belopolsky, 2012) or stand out from the environment through their physical properties (e.g., Hickey et al., 2010a; Le Pelley et al., 2016).”
From Selection history: How reward modulates selectivity of visual attention. (2018) · cites this paper as Anderson et al., 2011
The Results and Methods describe reward learning for red or green stimuli that were task-relevant search targets during training; those colors later captured attention when they became irrelevant distractors. This supports Anderson et al. as an example of a bias learned from task-relevant stimuli, but the study does not establish that attentional prioritization is necessary to learn every selection-history bias. (LLM, conf=0.83)
Experimental — accuracy not yet measuredAnderson et al. (2011) observed an increase in search time when a nontarget had a color previously associated with high reward, even though participants were instructed to ignore color and could no longer receive reward.
“During the test session, Anderson et al. observed an increase in the time to find the target when a nontarget had a color that was previously associated with a high reward relative to when none of the previously reward-associated colors was present.”
From Selection history: How reward modulates selectivity of visual attention. (2018) · cites this paper as Anderson, Laurent, & Yantis, 2011
Even a strong top-down set to select only the task-relevant stimulus cannot overcome the capturing effects of a salient stimulus, a primed stimulus, or a stimulus associated with reward.
“several studies have already shown that even a strong top-down set to select only the task-relevant stimulus cannot overcome the capturing effects of a salient stimulus (Theeuwes, 1991b, 1992, 1994), a stimulus that is primed (Theeuwes, Reimann, & Mortier, 2006; Theeuwes & van der Burg, 2011), or a stimulus associated with reward (Anderson et al., 2011).”
From Selection history: How reward modulates selectivity of visual attention. (2018) · cites this paper as Anderson et al., 2011
Individual working memory capacity is negatively correlated with capture by previously reward-associated stimuli.
“individual working memory capacity has been shown to be negatively correlated with capture by previously reward-associated stimuli (Anderson et al., 2011; Anderson et al., 2013).”
From Selection history: How reward modulates selectivity of visual attention. (2018) · cites this paper as Anderson et al., 2011
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- Authors
- Anderson, B. A., Laurent, P. A., & Yantis, S.
- Affiliations
- Johns Hopkins University (US)
- ORCID
- Anderson, Brian A.
- Venue
- Proceedings of the National Academy of Sciences
- Publisher
- National Academy of Sciences
- Published
- 2011-06-06
- Volume
- 108
- Issue
- 25
- Pages
- 10367-10371
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- PMC
- PMC3121816
- OpenAlex
- W2132274525
- Cited by
- 1,098
- References
- 45
- Citations by year
- 2026: 60 · 2025: 73 · 2024: 74 · 2023: 75 · 2022: 71 · 2021: 92 · 2020: 100 · 2019: 86 · 2018: 78 · 2017: 73 · 2016: 81 · 2015: 87 · 2014: 69 · 2013: 42 · 2012: 32
- FWCI
- 23.72
- 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)
- Funders
- National Institutes of Health
- National Institute on Drug Abuse (R01DA013165)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Visual Attention and Saliency Detection
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neurotransmitter Receptor Influence on Behavior
- Keywords
- attentional capture, value-driven attention, reward learning, visual search, salience, extinction, working memory capacity, impulsivity, cognitive control, addiction, obesity
- MeSH terms
- Attention, Discrimination, Psychological, Drive, Goals, Neuropsychological Tests, Pattern Recognition, Visual, Psychomotor Performance, Reaction Time, 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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