The effects of neural gain on attention and learning.
TLDR
It is 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.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.read more
Citations
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An opportunity cost model of subjective effort and task performance.
TL;DR: It is argued that the phenomenology of effort can be understood as the felt output of these cost/benefit computations of the costs and benefits associated with task performance and motivates reduced deployment of these computational mechanisms in the service of the present task.
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Relationships between Pupil Diameter and Neuronal Activity in the Locus Coeruleus, Colliculi, and Cingulate Cortex.
TL;DR: It is shown that LC activation reliably anticipates changes in pupil diameter that either fluctuate naturally or are driven by external events during near fixation, as in many psychophysical tasks.
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Waking State: Rapid Variations Modulate Neural and Behavioral Responses.
Matthew J. McGinley,Martin Vinck,Jacob Reimer,Renata Batista-Brito,Edward Zagha,Cathryn R. Cadwell,Andreas S. Tolias,Jessica A. Cardin,David A. McCormick +8 more
TL;DR: By taking fluctuations in state into account, neural response (co)variability is significantly reduced, revealing the brain to be more reliable and predictable than previously thought.
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Arousal and Locomotion Make Distinct Contributions to Cortical Activity Patterns and Visual Encoding
TL;DR: These findings suggest complementary roles of arousal and locomotion in promoting functional flexibility in cortical circuits and that arousal suppressed spontaneous firing and strongly altered the temporal patterning of population activity.
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The Dynamics of Functional Brain Networks: Integrated Network States during Cognitive Task Performance
James M. Shine,James M. Shine,Patrick G. Bissett,Peter T. Bell,Oluwasanmi Koyejo,Joshua H. Balsters,Krzysztof J. Gorgolewski,Craig A. Moodie,Russell A. Poldrack +8 more
TL;DR: Time-resolved network analysis of fMRI data shows a direct link between cognitive performance and the dynamic reorganization of the network structure of the brain, suggesting that ascending neuromodulatory systems may govern the transition between these alternative modes of brain function.
References
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Complex brain networks: graph theoretical analysis of structural and functional systems
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Learning and Teaching Styles in Engineering Education.
TL;DR: A self-scoring web-based instrument called the Index of Learning Styles that assesses preferences on four scales of the learning style model developed in the paper currently gets about 100,000 hits a year and has been translated into half a dozen languages.
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An Integrative Theory of Locus Coeruleus-Norepinephrine Function: Adaptive Gain and Optimal Performance.
TL;DR: In this article, the locus coeruleus-norepinephrine (LC-NE) system plays a more complex and specific role in the control of behavior than investigators previously thought.
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What we can do and what we cannot do with fMRI
TL;DR: An overview of the current state of fMRI is given, and the current understanding of the haemodynamic signals and the constraints they impose on neuroimaging data interpretation are presented.