Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

The effects of neural gain on attention and learning


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.

This is a preview of subscription content, access via your institution

Access options

Buy this article

Prices may be subject to local taxes which are calculated during checkout

Figure 1: The effect of gain on attention and learning.
Figure 2: Relationship between learning performance and ILS scores.
Figure 3: Relationship between pupil diameter and BOLD response to task-relevant and task-irrelevant stimuli.
Figure 4: Simulation of the effect of global changes in gain on functional connectivity strength and clustering.
Figure 5: Global fluctuations in local functional connectivity.
Figure 6: Pupil diameter and whole-brain functional connectivity.
Figure 7: Pupil diameter and local functional connectivity.
Figure 8: The clustering of functional connections, pupil diameter and task performance.

Similar content being viewed by others


  1. Felder, R.M. & Silverman, L.K. Learning and teaching styles in engineering education. Eng. Educ. 78, 674–681 (1988).

    Google Scholar 

  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).

  3. Felder, R.M. & Spurlin, J. Application, reliability and validity of the index of learning styles. Int. J. Eng. Educ. 21, 103–112 (2005).

    Google Scholar 

  4. Servan-Schreiber, D., Printz, H. & Cohen, J.D. A network model of catecholamine effects: gain, signal-to-noise ratio, and behavior. Science 249, 892–895 (1990).

    Article  CAS  Google Scholar 

  5. Aston-Jones, G. & Cohen, J.D. An integrative theory of locus coeruleus–norepinephrine function: adaptive gain and optimal performance. Annu. Rev. Neurosci. 28, 403–450 (2005).

    Article  CAS  Google Scholar 

  6. Gilzenrat, M.S., Nieuwenhuis, S., Jepma, M. & Cohen, J.D. Pupil diameter tracks changes in control state predicted by the adaptive gain theory of locus coeruleus function. Cogn. Affect. Behav. Neurosci. 10, 252–269 (2010).

    Article  Google Scholar 

  7. Jepma, M. & Nieuwenhuis, S. Pupil diameter predicts changes in the exploration-exploitation trade-off: evidence for the adaptive gain theory. J. Cogn. Neurosci. 23, 1587–1596 (2011).

    Article  Google Scholar 

  8. Waterhouse, B.D., Moises, H.C. & Woodward, D.J. Noradrenergic modulation of somatosensory cortical neuronal responses to lontophoretically applied putative neurotransmitters. Exp. Neurol. 69, 30–49 (1980).

    Article  CAS  Google Scholar 

  9. Waterhouse, B.D., Moises, H.C., Yeh, H.H., Geller, H.M. & Woodward, D.J. Comparison of norepinephrine- and benzodiazepine-induced augmentation of Purkinje cell responses to gamma-aminobutyric acid (GABA). J. Pharmacol. Exp. Ther. 228, 257–267 (1984).

    CAS  PubMed  Google Scholar 

  10. Waterhouse, B.D. & Woodward, D.J. Interaction of norepinephrine with cerebrocortical activity evoked by stimulation of somatosensory afferent pathways in the rat. Exp. Neurol. 67, 11–34 (1980).

    Article  CAS  Google Scholar 

  11. Koss, M.C. Pupillary dilation as an index of central nervous system α2-adrenoceptor activation. J. Pharmacol. Methods 15, 1–19 (1986).

    Article  CAS  Google Scholar 

  12. Einhäuser, W., Stout, J., Koch, C. & Carter, O.L. Pupil dilation reflects perceptual selection and predicts subsequent stability in perceptual rivalry. Proc. Natl. Acad. Sci. USA 105, 1704–1709 (2008).

    Article  Google Scholar 

  13. Murphy, P.R., Robertson, I.H., Balsters, J.H. & O'Connell, R.G. Pupillometry and P3 index the locus coeruleus-noradrenergic arousal function in humans. Psychophysiology 48, 1532–1543 (2011).

    Article  Google Scholar 

  14. Salinas, E. Fast remapping of sensory stimuli onto motor actions on the basis of contextual modulation. J. Neurosci. 24, 1113–1118 (2004).

    Article  CAS  Google Scholar 

  15. Salinas, E. & Bentley, N.M. Gain modulation as a mechanism for switching reference frames, tasks and targets. Coherent Behav. Neuronal Netw. 3, 121–142 (2009).

    Article  Google Scholar 

  16. Haider, B. & McCormick, D.A. Rapid neocortical dynamics: cellular and network mechanisms. Neuron 62, 171–189 (2009).

    Article  CAS  Google Scholar 

  17. Eguíluz, V.M., Chialvo, D.R., Cecchi, G.A., Baliki, M. & Apkarian, A.V. Scale-free brain functional networks. Phys. Rev. Lett. 94, 018102 (2005).

    Article  Google Scholar 

  18. Luce, R.D. & Perry, A. A method of matrix analysis of group structure. Psychometrika 14, 95–116 (1949).

    Article  CAS  Google Scholar 

  19. Easterbrook, J.A. The effect of emotion on cue utilization and the organization of behavior. Psychol. Rev. 66, 183–201 (1959).

    Article  CAS  Google Scholar 

  20. Staal, M.A. Stress, Cognition, and Human Performance: a Literature Review and Conceptual Framework (NASA STI Program, 2004).

  21. Dias-Ferreira, E. et al. Chronic stress causes frontostriatal reorganization and affects decision-making. Science 325, 621–625 (2009).

    Article  CAS  Google Scholar 

  22. Schwabe, L. & Wolf, O.T. Stress-induced modulation of instrumental behavior: from goal-directed to habitual control of action. Behav. Brain Res. 219, 321–328 (2011).

    Article  Google Scholar 

  23. Schwabe, L., Tegenthoff, M., Höffken, O. & Wolf, O.T. Concurrent glucocorticoid and noradrenergic activity shifts instrumental behavior from goal-directed to habitual control. J. Neurosci. 30, 8190–8196 (2010).

    Article  CAS  Google Scholar 

  24. Schwabe, L., Höffken, O., Tegenthoff, M. & Wolf, O.T. Preventing the stress-induced shift from goal-directed to habit action with a β-adrenergic antagonist. J. Neurosci. 31, 17317–17325 (2011).

    Article  CAS  Google Scholar 

  25. Alexander, J.K., Hillier, A., Smith, R., Tivarus, M. & Beversdorf, D. Beta-adrenergic modulation of cognitive flexibility during stress. J. Cogn. Neurosci. 19, 468–478 (2007).

    Article  Google Scholar 

  26. Campbell, H.L., Tivarus, M.E., Hillier, A. & Beversdorf, D.Q. Increased task difficulty results in greater impact of noradrenergic modulation of cognitive flexibility. Pharmacol. Biochem. Behav. 88, 222–229 (2008).

    Article  CAS  Google Scholar 

  27. Maier, A. et al. Divergence of fMRI and neural signals in V1 during perceptual suppression in the awake monkey. Nat. Neurosci. 11, 1193–1200 (2008).

    Article  CAS  Google Scholar 

  28. Sirotin, Y.B. & Das, A. Anticipatory haemodynamic signals in sensory cortex not predicted by local neuronal activity. Nature 457, 475–479 (2009).

    Article  CAS  Google Scholar 

  29. Logothetis, N.K. What we can do and what we cannot do with fMRI. Nature 453, 869–878 (2008).

    Article  CAS  Google Scholar 

  30. Dayan, P. & Yu, A.J. Norepinephrine and neural interrupts. in Advances in Neural Information Processing Systems 18 (eds. Weiss, Y., Schölkopf, B. & Platt, J.) 243–250 (MIT Press, Cambridge, Massachusetts, 2006).

  31. Bullmore, E. & Sporns, O. Complex brain networks: graph theoretical analysis of structural and functional systems. Nat. Rev. Neurosci. 10, 186–198 (2009).

    Article  CAS  Google Scholar 

  32. Bullmore, E. & Sporns, O. The economy of brain network organization. Nat. Rev. Neurosci. 13, 336–349 (2012).

    Article  CAS  Google Scholar 

  33. Bassett, D.S. et al. Dynamic reconfiguration of human brain networks during learning. Proc. Natl. Acad. Sci. USA 108, 7641–7646 (2011).

    Article  CAS  Google Scholar 

  34. Kitzbichler, M.G., Henson, R.N., Smith, M.L., Nathan, P.J. & Bullmore, E.T. Cognitive effort drives workspace configuration of human brain functional networks. J. Neurosci. 31, 8259–8270 (2011).

    Article  CAS  Google Scholar 

  35. Nicol, R.M. et al. Fast reconfiguration of high-frequency brain networks in response to surprising changes in auditory input. J. Neurophysiol. 107, 1421–1430 (2012).

    Article  Google Scholar 

  36. Reas, C. & Fry, B. Processing: a Programming Handbook for Visual Designers and Artists (MIT Press, Cambridge, Massachusetts, 2007).

  37. McClelland, J.L. & Rumelhart, D.E. Explorations in Parallel Distributed Processing: A Handbook of Models, Programs and Exercises (MIT Press, Cambridge, Massachusetts, USA, 1988).

  38. Lambert, A., Wells, I. & Kean, M. Do isoluminant color changes capture attention? Percept. Psychophys. 65, 495–507 (2003).

    Article  Google Scholar 

  39. Brainard, D.H. The psychophysics toolbox. Spat. Vis. 10, 433–436 (1997).

    Article  CAS  Google Scholar 

  40. Hoeks, B. & Levelt, W.J.M. Pupillary dilation as a measure of attention: a quantitative system analysis. Behav. Res. Methods Instrum. Comput. 25, 16–26 (1993).

    Article  Google Scholar 

  41. Fisher, R.A. On the “probable error” of a coefficient of correlation deduced from a small sample. Metron 1, 3–32 (1921).

    Google Scholar 

Download references


We thank N. Turk-Browne and P. Dayan for helpful comments on earlier versions of the manuscript. This research was funded by US National Institutes of Health grants R03 DA029073 and R01 MH098861, a Howard Hughes Medical Institute International Student Research fellowship to E.E. and a Sloan Research Fellowship to Y.N. The authors also wish to thank the generous support of the Regina and John Scully Center for the Neuroscience of Mind and Behavior in the Princeton Neuroscience Institute.

Author information

Authors and Affiliations



E.E. and Y.N. designed the study with consultation from J.D.C. E.E. and Y.N. analyzed the data, and all of the authors contributed to discussion and interpretation of the findings and writing the manuscript.

Corresponding author

Correspondence to Eran Eldar.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–7 and Supplementary Table 1 (PDF 2798 kb)

Rights and permissions

Reprints and permissions

About this article

Cite this article

Eldar, E., Cohen, J. & Niv, Y. The effects of neural gain on attention and learning. Nat Neurosci 16, 1146–1153 (2013).

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI:

This article is cited by


Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing