Cited by 402Neural dynamics and brain functionNeural and Behavioral Psychology StudiesVisual perception and processing mechanisms

The effects of neural gain on attention and learning

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Eldar, Eran, Cohen, Jonathan D, Niv, Yael·2013·Nature Neuroscience

Abstract

Attention is commonly thought to be manifest through local variations in neural gain. However, what would be the effects of brain-wide changes in gain? We hypothesized that global fluctuations in gain modulate the breadth of attention and the degree to which processing is focused on aspects of the environment to which one is predisposed to attend. We found that measures of pupil diameter, which are thought to track levels of locus coeruleus norepinephrine activity and neural gain, were correlated with the degree to which learning was focused on stimulus dimensions that individual human participants were more predisposed to process. In support of our interpretation of this effect in terms of global changes in gain, we found that the measured pupillary and behavioral variables were strongly correlated with global changes in the strength and clustering of functional connectivity, as brain-wide fluctuations of gain would predict.

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  • Global fluctuations in network structure over time may have been driven by ongoing dynamic alterations in ascending neuromodulatory input to cortex and subcortex, which through neural gain modulation may have mediated increases in connectivity between otherwise segregated regions.

    “these results suggest that the observed global fluctuations in network structure over time may have been driven by ongoing dynamic alterations in ascending neuromodulatory input to the cortex and subcortex, which through the modulation of neural gain, may have mediated increases in connectivity between otherwise segregated regions of the brain.”
  • The locus coeruleus may modulate fluctuations in global network topology through phasic alterations in neural gain.

    “The locus coeruleus (Aston-Jones and Cohen, 2005) is known to modulate pupil diameter (Joshi et al., 2016), and thus by inference, may play a role in the modulation of fluctuations in global network topology through phasic alterations in neural gain (Eldar et al., 2013).”

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Research works printed but not linked: 2

  1. [2]Coffield F Moseley D Hall E Ecclestone K Learning Styles and Pedagogy in Post-16 Learning: A Systematic and Critical Review Learning and Skills Research Centre London 2004
  2. [20]Staal MA 2004 Stress, cognition, and human performance: A literature review and conceptual framework Ames Research Center

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Authors
Eldar, E., Cohen, J. D., & Niv, Y.
Affiliations
Princeton University (US)
Corresponding author
Eldar, Eran
Venue
Nature Neuroscience
Published
2013-06-16
Volume
16
Issue
8
Pages
1146-1153
ISSN
  • 1097-6256 (ISSN-L)
  • 1546-1726
OpenAlex
W2090305817
Cited by
402
References
38
Citations by year
2026: 17 · 2025: 20 · 2024: 23 · 2023: 31 · 2022: 36 · 2021: 45 · 2020: 35 · 2019: 29 · 2018: 39 · 2017: 45 · 2016: 38 · 2015: 23 · 2014: 17 · 2013: 4
FWCI
14.72
Type
journal article
Language
en
Open access
Locations
  • Nature Neuroscience (publishedVersion)
  • PubMed (publishedVersion)
  • Europe PMC (PubMed Central) (acceptedVersion)
  • PubMed Central (acceptedVersion)
Funders
  • Howard Hughes Medical Institute
  • National Institute of Mental Health (R01MH098861)
  • National Institute on Drug Abuse (R03DA029073)
Topics
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Visual perception and processing mechanisms
Keywords
neural gain, pupil size, locus coeruleus, norepinephrine, attention, learning, functional connectivity, pupillometry
MeSH terms
Adolescent, Adult, Attention, Discrimination Learning, Female, Humans, Locus Coeruleus, Magnetic Resonance Imaging, Male, Middle Aged, Muscle Contraction, Norepinephrine, Pattern Recognition, Visual, Photic Stimulation, Psychomotor Performance, Pupil, Reward, Semantics, Neural Networks, Computer, Young Adult

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