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:

Neuronal remapping and circuit persistence in economic decisions

Nature Neuroscience volume 19, pages 855–861 (2016)Cite this article

Subjects

Abstract

The orbitofrontal cortex plays a central role in good-based economic decisions. When subjects make choices, neurons in this region represent the identities and values of offered and chosen goods. Notably, choices in different behavioral contexts may involve a potentially infinite variety of goods. Thus a fundamental question concerns the stability versus flexibility of the decision circuit. Here we show in rhesus monkeys that neurons encoding the identity or the subjective value of particular goods in a given context 'remap' and become associated with different goods when the context changes. At the same time, the overall organization of the decision circuit and the function of individual cells remain stable across contexts. In particular, two neurons supporting the same decision in one context also support the same decision in different contexts. These results demonstrate how the same neural circuit can underlie economic decisions involving a large variety of goods.

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: Experimental design.
Figure 2: Activity of five example neurons recorded in experimental sessions (A:B, C:D design).
Figure 3: Comparing classifications across trial blocks.
Figure 4: Classification consistency in experimental and control cells.
Figure 5: Direct comparison of experimental and control conditions.
Figure 6: Persistence of neuronal pools.

Similar content being viewed by others

References

  1. Rahman, S., J Sahakia, B., N Cardinal, R., Rogers, R. & Robbins, T. Decision making and neuropsychiatry. Trends Cogn. Sci. 5, 271–277 (2001).

    Article  CAS  Google Scholar 

  2. Volkow, N.D. & Li, T.K. Drug addiction: the neurobiology of behaviour gone awry. Nat. Rev. Neurosci. 5, 963–970 (2004).

    Article  CAS  Google Scholar 

  3. Strauss, G.P., Waltz, J.A. & Gold, J.M. A review of reward processing and motivational impairment in schizophrenia. Schizophr. Bull. 40 (suppl. 2), S107–S116 (2014).

    Article  Google Scholar 

  4. Gallagher, M., McMahan, R.W. & Schoenbaum, G. Orbitofrontal cortex and representation of incentive value in associative learning. J. Neurosci. 19, 6610–6614 (1999).

    Article  CAS  Google Scholar 

  5. Camille, N., Griffiths, C.A., Vo, K., Fellows, L.K. & Kable, J.W. Ventromedial frontal lobe damage disrupts value maximization in humans. J. Neurosci. 31, 7527–7532 (2011).

    Article  CAS  Google Scholar 

  6. Rudebeck, P.H., Saunders, R.C., Prescott, A.T., Chau, L.S. & Murray, E.A. Prefrontal mechanisms of behavioral flexibility, emotion regulation and value updating. Nat. Neurosci. 16, 1140–1145 (2013).

    Article  CAS  Google Scholar 

  7. Thorpe, S.J., Rolls, E.T. & Maddison, S. The orbitofrontal cortex: neuronal activity in the behaving monkey. Exp. Brain Res. 49, 93–115 (1983).

    Article  CAS  Google Scholar 

  8. Padoa-Schioppa, C. & Assad, J.A. Neurons in the orbitofrontal cortex encode economic value. Nature 441, 223–226 (2006).

    Article  CAS  Google Scholar 

  9. Raghuraman, A.P. & Padoa-Schioppa, C. Integration of multiple determinants in the neuronal computation of economic values. J. Neurosci. 34, 11583–11603 (2014).

    Article  CAS  Google Scholar 

  10. Howard, J.D., Gottfried, J.A., Tobler, P.N. & Kahnt, T. Identity-specific coding of future rewards in the human orbitofrontal cortex. Proc. Natl. Acad. Sci. USA 112, 5195–5200 (2015).

    Article  CAS  Google Scholar 

  11. Zhou, J., Jia, C., Feng, Q., Bao, J. & Luo, M. Prospective coding of dorsal raphe reward signals by the orbitofrontal cortex. J. Neurosci. 35, 2717–2730 (2015).

    Article  CAS  Google Scholar 

  12. Klein-Flügge, M.C., Barron, H.C., Brodersen, K.H., Dolan, R.J. & Behrens, T.E. Segregated encoding of reward-identity and stimulus-reward associations in human orbitofrontal cortex. J. Neurosci. 33, 3202–3211 (2013).

    Article  Google Scholar 

  13. Lara, A.H., Kennerley, S.W. & Wallis, J.D. Encoding of gustatory working memory by orbitofrontal neurons. J. Neurosci. 29, 765–774 (2009).

    Article  CAS  Google Scholar 

  14. McDannald, M.A. et al. Orbitofrontal neurons acquire responses to 'valueless' Pavlovian cues during unblocking. eLife 3, e02653 (2014).

    Article  Google Scholar 

  15. Critchley, H.D. & Rolls, E.T. Hunger and satiety modify the responses of olfactory and visual neurons in the primate orbitofrontal cortex. J. Neurophysiol. 75, 1673–1686 (1996).

    Article  CAS  Google Scholar 

  16. Gottfried, J.A., O'Doherty, J. & Dolan, R.J. Encoding predictive reward value in human amygdala and orbitofrontal cortex. Science 301, 1104–1107 (2003).

    Article  CAS  Google Scholar 

  17. Sul, J.H., Kim, H., Huh, N., Lee, D. & Jung, M.W. Distinct roles of rodent orbitofrontal and medial prefrontal cortex in decision making. Neuron 66, 449–460 (2010).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  19. Rustichini, A. & Padoa-Schioppa, C. A neuro-computational model of economic decisions. J. Neurophysiol. 114, 1382–1398 (2015).

    Article  Google Scholar 

  20. Padoa-Schioppa, C. Neuronal origins of choice variability in economic decisions. Neuron 80, 1322–1336 (2013).

    Article  CAS  Google Scholar 

  21. Agresti, A. An Introduction to Categorical Data Analysis (Wiley, 2007).

  22. Gottfried, J.A. & Zelano, C. The value of identity: olfactory notes on orbitofrontal cortex function. Ann. NY Acad. Sci. 1239, 138–148 (2011).

    Article  Google Scholar 

  23. Pritchard, T.C. et al. Satiety-responsive neurons in the medial orbitofrontal cortex of the macaque. Behav. Neurosci. 122, 174–182 (2008).

    Article  Google Scholar 

  24. Rolls, E.T. The orbitofrontal cortex and reward. Cereb. Cortex 10, 284–294 (2000).

    Article  CAS  Google Scholar 

  25. Small, D.M. Flavor is in the brain. Physiol. Behav. 107, 540–552 (2012).

    Article  CAS  Google Scholar 

  26. Verhagen, J.V. & Engelen, L. The neurocognitive bases of human multimodal food perception: sensory integration. Neurosci. Biobehav. Rev. 30, 613–650 (2006).

    Article  Google Scholar 

  27. Cisek, P. Making decisions through a distributed consensus. Curr. Opin. Neurobiol. 22, 927–936 (2012).

    Article  CAS  Google Scholar 

  28. Glimcher, P.W. Foundations of Neuroeconomic Analysis (Oxford University Press, 2011).

  29. Rangel, A. & Hare, T. Neural computations associated with goal-directed choice. Curr. Opin. Neurobiol. 20, 262–270 (2010).

    Article  CAS  Google Scholar 

  30. Rushworth, M.F., Kolling, N., Sallet, J. & Mars, R.B. Valuation and decision-making in frontal cortex: one or many serial or parallel systems? Curr. Opin. Neurobiol. 22, 946–955 (2012).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  32. Muller, R.U. & Kubie, J.L. The effects of changes in the environment on the spatial firing of hippocampal complex-spike cells. J. Neurosci. 7, 1951–1968 (1987).

    Article  CAS  Google Scholar 

  33. Wills, T.J., Lever, C., Cacucci, F., Burgess, N. & O'Keefe, J. Attractor dynamics in the hippocampal representation of the local environment. Science 308, 873–876 (2005).

    Article  CAS  Google Scholar 

  34. Padoa-Schioppa, C. Range-adapting representation of economic value in the orbitofrontal cortex. J. Neurosci. 29, 14004–14014 (2009).

    Article  CAS  Google Scholar 

  35. Kobayashi, S., Pinto de Carvalho, O. & Schultz, W. Adaptation of reward sensitivity in orbitofrontal neurons. J. Neurosci. 30, 534–544 (2010).

    Article  CAS  Google Scholar 

  36. Padoa-Schioppa, C. & Rustichini, A. Rational attention and adaptive coding: a puzzle and a solution. Am. Econ. Rev. 104, 507–513 (2014).

    Article  Google Scholar 

  37. Tremblay, L. & Schultz, W. Relative reward preference in primate orbitofrontal cortex. Nature 398, 704–708 (1999).

    Article  CAS  Google Scholar 

  38. Schultz, W. Neuronal reward and decision signals: from theories to data. Physiol. Rev. 95, 853–951 (2015).

    Article  CAS  Google Scholar 

  39. Morrison, S.E., Saez, A., Lau, B. & Salzman, C.D. Different time courses for learning-related changes in amygdala and orbitofrontal cortex. Neuron 71, 1127–1140 (2011).

    Article  CAS  Google Scholar 

  40. Padoa-Schioppa, C. & Assad, J.A. The representation of economic value in the orbitofrontal cortex is invariant for changes of menu. Nat. Neurosci. 11, 95–102 (2008).

    Article  CAS  Google Scholar 

  41. Cai, X. & Padoa-Schioppa, C. Contributions of orbitofrontal and lateral prefrontal cortices to economic choice and the good-to-action transformation. Neuron 81, 1140–1151 (2014).

    Article  CAS  Google Scholar 

  42. Saez, A., Rigotti, M., Ostojic, S., Fusi, S. & Salzman, C.D. Abstract context representations in primate amygdala and prefrontal cortex. Neuron 87, 869–881 (2015).

    Article  CAS  Google Scholar 

  43. Neter, J., Wasserman, W. & Kutner, M.H. Applied Linear Statistical Models: Regression, Analysis of Variance, and Experimental Designs (Irwin, Homewood, Illinois, USA, 1990).

Download references

Acknowledgements

We thank H. Schoknecht for help with animal training, A. Raghuraman for help with recording and X. Cai, K. Conen, E. Han, I. Monosov and L. Snyder for comments on the manuscript. This work was supported by the National Institutes of Health (grant numbers R01-DA032758 and R01-MH104494 to C.P.-S.) and by the McDonnell Center for Systems Neuroscience (predoctoral fellowship to J.X.).

Author information

Authors and Affiliations

  1. Department of Neuroscience, Washington University in St. Louis, St. Louis, Missouri, USA

    Jue Xie & Camillo Padoa-Schioppa

  2. Department of Economics, Washington University in St. Louis, St. Louis, Missouri, USA

    Camillo Padoa-Schioppa

  3. Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, Missouri, USA

    Camillo Padoa-Schioppa

Authors
  1. Jue Xie
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Camillo Padoa-Schioppa
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

J.X. and C.P.-S. designed the study; J.X. collected and analyzed the data; J.X. and C.P.-S. wrote the manuscript.

Corresponding author

Correspondence to Camillo Padoa-Schioppa.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 Correlation between task-related activity across trial blocks.

In this analysis, we examined whether individual neurons typically met the ANOVA criterion in the same time windows in both trial blocks. For each neuron in each block, we obtained a vector of binary variables indicating whether the cell passed the criterion in different time windows. We then calculated the correlation coefficient between the two vectors obtained for the two trial blocks. Finally, we examined the distribution of correlation coefficients across the population, separately for experimental and control cells. Correlation coefficients were typically positive, indicating that neurons were generally task-related in the same time windows. This result held true both in the experimental condition (median = 0.45) and in the control condition (median = 0.49). Moreover, the distributions obtained in the two conditions did not differ significantly from one another (p = 0.83, Kolmogorov-Smirnov test).

Supplementary Figure 2 Analysis based on neuronal responses.

The analyses presented in Fig. 3 were based on a classification involving multiple time windows (see Online Methods). For a control, we repeated the analysis comparing the classifications obtained for neuronal responses (as opposed to neurons) across trial blocks. As a reminder, a neuronal response was defined as the activity of one cell, in one time window, in one trial block, as a function of the trial type. In this analysis, we compared the two responses obtained from the same cell, in the same time window, for the two trial blocks. (Thus each cell might contribute more than one response to the figure.) (a,b,c) Control sessions (N = 553 responses). (d,e,f) Experimental sessions (N = 1,171 responses). Data are presented in the same format used for Fig. 3. Panels illustrate the response counts (a, d), the analysis of odds ratios (b, e) and the results of the bootstrap analysis (c, f). All statistical criteria are as in Fig. 3. For odds ratios, exact p values are shown in Supplementary Figs. 6e (relative to panel b) and 6f (relative to panel e). The results obtained in this analysis confirmed those obtained for neurons (Fig. 3). In other words, response counts were significantly above chance level for all positions on the main diagonal, and this results held true both in the control condition and in the experimental condition.

Supplementary Figure 3 Analysis of response similarity across trial blocks based on the R2.

We conducted a control analysis to examine whether the R2 obtained for one response in one block predicted the R2 obtained for the corresponding response in the other block. In essence, this approach avoided the classification procedure. The analysis included all pairs of responses that met the ANOVA criterion in both blocks. We first examined experimental cells. For each block, we obtained four R2 from the linear regressions on each of the 4 variables. We identified the variable providing the highest R2 in one block, and we considered the "corresponding" variable in the other block. For variables offer value A and offer value B, the corresponding variables were offer value C and offer value D, respectively. Independently of the value measured for the R2 in the A:B block, we predicted that the R2 measured for the corresponding variable in the C:D block would be typically higher than that measured for the other, non-corresponding variables (and vice versa). This is indeed what we observed. (a) Neuronal population recorded in experimental sessions (A:B, C:D). The x-axis and y-axis in the scatter plot represent the R2 obtained in the second block for the corresponding variable and other variables, respectively. While data are quite scattered, the population as a whole lies below the identity line, indicating that the variable encoded in one block is predictive of the variable encoded in the other block. Each response provided 3 data points to this plot, one for each of the "other" variables. Furthermore, we repeated the analysis using for reference the second block, and we pooled the results obtained with the two procedures. (b) For each data point in panel a we computed the difference in R2 obtained for the corresponding and other variables. The distribution obtained for the population was significantly shifted towards positive values (median = 0.24, p<10-10, Wilcoxon signed rank test). (c,d) Same analysis as in panels ab performed for the population recorded in control sessions (A:B, A:B). In panel d, the distribution of ΔR2 obtained for the population was significantly shifted towards positive values (median = 0.32, p<10-10, Wilcoxon signed rank test). (e,f,g,h) Analysis restricted to offer value responses. The four panels illustrate the results obtained when the analysis was restricted to neuronal responses classified as offer value in the reference block. The results were very similar to those obtained for the entire population. The distribution of ΔR2 was significantly shifted towards positive values for both experimental (median = 0.15, p<10-10, Wilcoxon signed rank test) and control sessions (median = 0.24, p<10-10, Wilcoxon signed rank test).

Supplementary Figure 4 Analysis of individual animals (experimental cells).

(a,b,c) Monkey V (N = 353 task-related cells). (d,e,f) Monkey C (N = 151 task-related cells). Data are presented in the same format used for Fig. 3. Panels illustrate the cell counts (a, d), the analysis of odds ratios (b, e) and the results of the bootstrap analysis (c, f). All statistical criteria are as in Fig. 3. For odds ratios, exact p values are shown in Supplementary Figs. 6g (relative to panel b) and 6h (relative to panel e). For monkey V, odds ratios were significantly above chance for all locations on the main diagonal. For monkey C, odds ratios on the main diagonal were all above chance, but the effects reached significance level only for chosen value+ cells and chosen juice cells (lower statistical power). Notably, cells classified as offer value or chosen juice in one block were not more likely to be classified as untuned in the other block, for either monkey.

Supplementary Figure 5 Analysis of conditional odds ratios.

In the analysis illustrated in Fig. 5e and based on the Breslow-Day statistics, two association strengths, namely [offer value, untuned] and [untuned, chosen juice], approached significance level. We thus conducted an additional analysis on these two associations. Specifically, we computed the conditional odds ratios for fixed levels of one dimension (Agresti, An Introduction to Categorical Data Analysis, 2007). (a) Association [offer value, untuned]. Numbers in the table indicate cell counts. Only neurons classified as offer value in the first trial block enter this table. The two columns indicate the results of the classification obtained in the second block (untuned or any of the three other classes). For this 2x2 table, odds ratio = 0.51 (p = 0.13, Fisher's exact test). In other words, the strength of the association [offer value, untuned] did not differ between conditions. Conditioning cell counts on the classification obtained in the second block provided similar results (odds ratio = 0.48; p = 0.09, Fisher's exact test). (b) Association [untuned, chosen juice]. Same format as in panel a. Only neurons classified as untuned in the first trial block enter this table. For this table, odds ratio = 0.38 (p = 0.10, Fisher's exact test). In other words, the strength of the association [untuned, chosen juice] did not differ between conditions. Conditioning cell counts on the classification obtained in the second block provided similar results (odds ratio = 0.37; p = 0.09, Fisher's exact test). In conclusion, the association strengths measured in the experimental condition were statistically indistinguishable from those measured in the control condition.

Supplementary Figure 6 Exact P values for odds ratios.

The figure shows the p values obtained using Fisher's exact test (two tails) on odds ratios throughout the paper. The correspondence between the panels of this figures and those of other figures is as follows: (a) Fig. 3b. (b) Fig. 3e. (c) Fig. 5b. (d) Fig. 5d. (e) Supplementary Fig. 2b. (f) Supplementary Fig. 2e. (g) Supplementary Fig. 4b. (h) Supplementary Fig. 4e. In each panel, red/cyan asterisks indicate that the cell counts were significantly above/below chance level (p<0.01).

Supplementary Figure 7 Chi-square values.

The table indicates the chi2 values obtained for each of the Breslow-Day tests performed for Fig. 5e.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–7 (PDF 1052 kb)

Supplementary Methods Checklist

(PDF 454 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Xie, J., Padoa-Schioppa, C. Neuronal remapping and circuit persistence in economic decisions. Nat Neurosci 19, 855–861 (2016). https://doi.org/10.1038/nn.4300

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nn.4300

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