Skip to main content

Thank you for visiting nature.com. 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:

Mechanisms underlying cortical activity during value-guided choice

Nature Neuroscience volume 15, pages 470–476 (2012)Cite this article

Subjects

Abstract

When choosing between two options, correlates of their value are represented in neural activity throughout the brain. Whether these representations reflect activity that is fundamental to the computational process of value comparison, as opposed to other computations covarying with value, is unknown. We investigated activity in a biophysically plausible network model that transforms inputs relating to value into categorical choices. A set of characteristic time-varying signals emerged that reflect value comparison. We tested these model predictions using magnetoencephalography data recorded from human subjects performing value-guided decisions. Parietal and prefrontal signals matched closely with model predictions. These results provide a mechanistic explanation of neural signals recorded during value-guided choice and a means of distinguishing computational roles of different cortical regions whose activity covaries with value.

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

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

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

Figure 1: Predictions of neural activity from cortical attractor network model.
Figure 2: Value-based decision task.
Figure 3: Subject behavior.
Figure 4: Main effect of task performance on activity in the 2–10-Hz frequency range.
Figure 5: pSPL (MNI 18, –44, 62 mm) and VMPFC (MNI 6, 28, –8 mm) show several value-related hallmarks of the biophysical network model.

Similar content being viewed by others

References

  1. von Neumann, J. & Morgenstern, O. Theory of Games and Economic Behavior (Princeton University Press, 1944).

  2. Dickinson, A. & Balleine, B. Motivational control of goal-directed action. Learn. Behav. 22, 1–18 (1994).

    Article  Google Scholar 

  3. Stephens, D.W. & Krebs, J.R. Foraging Theory (Princeton University Press, 1986).

  4. Sutton, R. & Barto, A. Reinforcement Learning: an Introduction (MIT Press, 1998).

  5. Rushworth, M.F. & Behrens, T.E.J. Choice, uncertainty and value in prefrontal and cingulate cortex. Nat. Neurosci. 11, 389–397 (2008).

    Article  CAS  Google Scholar 

  6. Kable, J.W. & Glimcher, P.W. The neurobiology of decision: consensus and controversy. Neuron 63, 733–745 (2009).

    Article  CAS  Google Scholar 

  7. Padoa-Schioppa, C. Neurobiology of economic choice: a good-based model. Annu. Rev. Neurosci. 34, 333–359 (2011).

    Article  CAS  Google Scholar 

  8. Rangel, A., Camerer, C.F. & Montague, P.R. A framework for studying the neurobiology of value-based decision making. Nat. Rev. Neurosci. 9, 545–556 (2008).

    Article  CAS  Google Scholar 

  9. Platt, M.L. & Glimcher, P.W. Neural correlates of decision variables in parietal cortex. Nature 400, 233–238 (1999).

    Article  CAS  Google Scholar 

  10. Sugrue, L.P., Corrado, G.S. & Newsome, W.T. Matching behavior and the representation of value in the parietal cortex. Science 304, 1782–1787 (2004).

    Article  CAS  Google Scholar 

  11. Tom, S.M., Fox, C.R., Trepel, C. & Poldrack, R.A. The neural basis of loss aversion in decision-making under risk. Science 315, 515–518 (2007).

    Article  CAS  Google Scholar 

  12. Plassmann, H., O'Doherty, J.P. & Rangel, A. Orbitofrontal cortex encodes willingness to pay in everyday economic transactions. J. Neurosci. 27, 9984–9988 (2007).

    Article  CAS  Google Scholar 

  13. Knutson, B., Taylor, J., Kaufman, M., Peterson, R. & Glover, G. Distributed neural representation of expected value. J. Neurosci. 25, 4806–4812 (2005).

    Article  CAS  Google Scholar 

  14. Boorman, E.D., Behrens, T.E.J., Woolrich, M.W. & Rushworth, M.S.F. How green is the grass on the other side? Frontopolar cortex and the evidence in favor of alternative courses of action. Neuron 62, 733–743 (2009).

    Article  CAS  Google Scholar 

  15. Blair, K. et al. Choosing the lesser of two evils, the better of two goods: specifying the roles of ventromedial prefrontal cortex and dorsal anterior cingulate in object choice. J. Neurosci. 26, 11379–11386 (2006).

    Article  CAS  Google Scholar 

  16. Kable, J.W. & Glimcher, P.W. The neural correlates of subjective value during intertemporal choice. Nat. Neurosci. 10, 1625–1633 (2007).

    Article  CAS  Google Scholar 

  17. Cai, X., Kim, S. & Lee, D. Heterogeneous coding of temporally discounted values in the dorsal and ventral striatum during intertemporal choice. Neuron 69, 170–182 (2011).

    Article  CAS  Google Scholar 

  18. Kennerley, S.W., Dahmubed, A.F., Lara, A.H. & Wallis, J.D. Neurons in the frontal lobe encode the value of multiple decision variables. J. Cogn. Neurosci. 21, 1162–1178 (2009).

    Article  Google Scholar 

  19. Usher, M. & McClelland, J.L. The time course of perceptual choice: the leaky, competing accumulator model. Psychol. Rev. 108, 550–592 (2001).

    Article  CAS  Google Scholar 

  20. Wang, X.J. Probabilistic decision making by slow reverberation in cortical circuits. Neuron 36, 955–968 (2002).

    Article  CAS  Google Scholar 

  21. Shadlen, M.N. & Newsome, W.T. Neural basis of a perceptual decision in the parietal cortex (area LIP) of the rhesus monkey. J. Neurophysiol. 86, 1916–1936 (2001).

    Article  CAS  Google Scholar 

  22. Soltani, A. & Wang, X.J. A biophysically based neural model of matching law behavior: melioration by stochastic synapses. J. Neurosci. 26, 3731–3744 (2006).

    Article  CAS  Google Scholar 

  23. Wong, K.F., Huk, A.C., Shadlen, M.N. & Wang, X.J. Neural circuit dynamics underlying accumulation of time-varying evidence during perceptual decision making. Front. Comput. Neurosci. 1, 6 (2007).

    Article  Google Scholar 

  24. Wong, K.F. & Wang, X.J. A recurrent network mechanism of time integration in perceptual decisions. J. Neurosci. 26, 1314–1328 (2006).

    Article  CAS  Google Scholar 

  25. Hämäläinen, M.S., Hari, R., Ilmoniemi, R.J. & Knuutila, J. Magnetoencephalography: theory, instrumentation and applications to noninvasive studies of the human brain. Rev. Mod. Phys. 65, 413–497 (1993).

    Article  Google Scholar 

  26. Tallon-Baudry, C., Bertrand, O., Delpuech, C. & Permier, J. Oscillatory gamma-band (30–70 Hz) activity induced by a visual search task in humans. J. Neurosci. 17, 722–734 (1997).

    Article  CAS  Google Scholar 

  27. Tversky, A. & Kahneman, D. Advances in prospect theory: cumulative representation of uncertainty. J. Risk Uncertain. 5, 297–323 (1992).

    Article  Google Scholar 

  28. Van Veen, B.D., van Drongelen, W., Yuchtman, M. & Suzuki, A. Localization of brain electrical activity via linearly constrained minimum variance spatial filtering. IEEE Trans. Biomed. Eng. 44, 867–880 (1997).

    Article  CAS  Google Scholar 

  29. Johnson, P.B., Ferraina, S., Bianchi, L. & Caminiti, R. Cortical networks for visual reaching: physiological and anatomical organization of frontal and parietal lobe arm regions. Cereb. Cortex 6, 102–119 (1996).

    Article  CAS  Google Scholar 

  30. Gershman, S.J., Pesaran, B. & Daw, N.D. Human reinforcement learning subdivides structured action spaces by learning effector-specific values. J. Neurosci. 29, 13524–13531 (2009).

    Article  CAS  Google Scholar 

  31. Serences, J.T. Value-based modulations in human visual cortex. Neuron 60, 1169–1181 (2008).

    Article  CAS  Google Scholar 

  32. Noonan, M.P. et al. Separate value comparison and learning mechanisms in macaque medial and lateral orbitofrontal cortex. Proc. Natl. Acad. Sci. U S A 107, 20547–20552 (2010).

    Article  CAS  Google Scholar 

  33. Hillebrand, A. & Barnes, G.R. A quantitative assessment of the sensitivity of whole-head MEG to activity in the adult human cortex. Neuroimage 16, 638–650 (2002).

    Article  CAS  Google Scholar 

  34. Marinkovic, K., Cox, B., Reid, K. & Halgren, E. Head position in the MEG helmet affects the sensitivity to anterior sources. Neurol. Clin. Neurophysiol. 2004, 30 (2004).

    CAS  PubMed  PubMed Central  Google Scholar 

  35. Sigman, M. & Dehaene, S. Parsing a cognitive task: a characterization of the mind′s bottleneck. PLoS Biol. 3, e37 (2005).

    Article  Google Scholar 

  36. Milosavljevic, M., Malmaud, J., Huth, A., Koch, C. & Rangel, A. The drift diffusion model can account for the accuracy and reaction time of value-based choices under high and low time pressure. Judgm. Decis. Mak. 5, 437–449 (2010).

    Google Scholar 

  37. deCharms, R.C. & Zador, A. Neural representation and the cortical code. Annu. Rev. Neurosci. 23, 613–647 (2000).

    Article  CAS  Google Scholar 

  38. Scheperjans, F. et al. Observer-independent cytoarchitectonic mapping of the human superior parietal cortex. Cereb. Cortex 18, 846–867 (2008).

    Article  Google Scholar 

  39. Mars, R.B. et al. Diffusion weighted imaging tractography-based parcellation of the human parietal cortex and comparison with human and macaque resting state functional connectivity. J. Neurosci. 31, 4087–4100 (2011).

    Article  CAS  Google Scholar 

  40. Haber, S.N., Kim, K.S., Mailly, P. & Calzavara, R. Reward-related cortical inputs define a large striatal region in primates that interface with associative cortical connections, providing a substrate for incentive-based learning. J. Neurosci. 26, 8368–8376 (2006).

    Article  CAS  Google Scholar 

  41. Kim, S. & Lee, D. Prefrontal cortex and impulsive decision making. Biol. Psychiatry 69, 1140–1146 (2011).

    Article  Google Scholar 

  42. Fellows, L.K. Deciding how to decide: ventromedial frontal lobe damage affects information acquisition in multi-attribute decision making. Brain 129, 944–952 (2006).

    Article  Google Scholar 

  43. Basten, U., Biele, G., Heekeren, H.R. & Fiebach, C.J. How the brain integrates costs and benefits during decision making. Proc. Natl. Acad. Sci. USA 107, 21767–21772 (2010).

    Article  CAS  Google Scholar 

  44. Rolls, E.T., Grabenhorst, F. & Deco, G. Choice, difficulty, and confidence in the brain. Neuroimage 53, 694–706 (2010).

    Article  Google Scholar 

  45. Kiebel, S.J., Garrido, M.I., Moran, R., Chen, C.C. & Friston, K.J. Dynamic causal modeling for EEG and MEG. Hum. Brain Mapp. 30, 1866–1876 (2009).

    Article  Google Scholar 

  46. Compte, A., Brunel, N., Goldman-Rakic, P.S. & Wang, X.J. Synaptic mechanisms and network dynamics underlying spatial working memory in a cortical network model. Cereb. Cortex 10, 910–923 (2000).

    Article  CAS  Google Scholar 

  47. Meltzer, J.A. et al. Effects of working memory load on oscillatory power in human intracranial EEG. Cereb Cortex 18, 1843–1855 (2008).

    Article  Google Scholar 

  48. Muthukumaraswamy, S.D., Edden, R.A., Jones, D.K., Swettenham, J.B. & Singh, K.D. Resting GABA concentration predicts peak gamma frequency and fMRI amplitude in response to visual stimulation in humans. Proc. Natl. Acad. Sci. USA 106, 8356–8361 (2009).

    Article  CAS  Google Scholar 

  49. Hsu, M., Krajbich, I., Zhao, C. & Camerer, C.F. Neural response to reward anticipation under risk is nonlinear in probabilities. J. Neurosci. 29, 2231–2237 (2009).

    Article  CAS  Google Scholar 

  50. Berg, P. & Scherg, M. A multiple source approach to the correction of eye artifacts. Electroencephalogr. Clin. Neurophysiol. 90, 229–241 (1994).

    Article  CAS  Google Scholar 

Download references

Acknowledgements

We thank V. Litvak and G. Barnes for many helpful discussions, C. Stagg and K. Friston for comments on the manuscript, T. Nichols for assistance implementing four-dimensional cluster-based permutation testing, and S. Braeutigam and A. Rao for help with data collection. This work was supported by the Wellcome Trust (L.T.H., N.K., M.W.W. and T.E.J.B., grant reference numbers WT088312 and WT080540), the Consortium Of Neuroimagers for Non-invasive Exploration of Brain Connectivity and Tracts (CONNECT; L.T.H. and T.E.J.B.), the UK Medical Research Council (M.F.S.R.) and the UK Engineering and Physical Sciences Research Council (M.W.W.). The project CONNECT acknowledges the financial support of the Future and Emerging Technologies (FET) program in the Seventh Framework Program for Research of the European Commission, under FET-Open grant number 238292.

Author information

Authors and Affiliations

  1. Department of Experimental Psychology, University of Oxford, South Parks Road, Oxford, UK

    Laurence T Hunt, Nils Kolling & Matthew F S Rushworth

  2. FMRIB Centre, University of Oxford, John Radcliffe Hospital, Oxford, UK

    Laurence T Hunt, Mark W Woolrich, Matthew F S Rushworth & Timothy E J Behrens

  3. Howard Hughes Medical Institute and Department of Neurobiology, Stanford University School of Medicine, Stanford, California, USA

    Alireza Soltani

  4. University Department of Psychiatry, Oxford Centre for Human Brain Activity (OHBA), Warneford Hospital, Oxford, UK

    Mark W Woolrich

  5. Wellcome Trust Centre for Neuroimaging, University College London, London, UK

    Timothy E J Behrens

Authors
  1. Laurence T Hunt
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nils Kolling
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Alireza Soltani
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Mark W Woolrich
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Matthew F S Rushworth
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Timothy E J Behrens
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

L.T.H., T.E.J.B. and M.F.S.R. designed experiment. L.T.H. and N.K. collected data. A.S. and L.T.H. built models and analyzed model predictions. M.W.W. wrote code for source reconstruction. L.T.H., T.E.J.B., N.K. and M.W.W. analyzed data. L.T.H., M.F.S.R. and T.E.J.B. wrote the paper. All of the authors discussed the results and commented on the manuscript.

Corresponding authors

Correspondence to Laurence T Hunt or Timothy E J Behrens.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–13, Supplementary Tables 1–3 and Supplementary Discussion (PDF 1806 kb)

Supplementary Movie 1

Temporal evolution of stimulus-locked activity (MOV 4476 kb)

Supplementary Movie 2

Temporal evolution of response-locked activity (MOV 5726 kb)

Rights and permissions

Reprints and permissions

About this article

Cite this article

Hunt, L., Kolling, N., Soltani, A. et al. Mechanisms underlying cortical activity during value-guided choice. Nat Neurosci 15, 470–476 (2012). https://doi.org/10.1038/nn.3017

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nn.3017

This article is cited by

Search

Advanced search

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