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.

Values encoded in orbitofrontal cortex are causally related to economic choices


In the eighteenth century, Daniel Bernoulli, Adam Smith and Jeremy Bentham proposed that economic choices rely on the computation and comparison of subjective values1. This hypothesis continues to inform modern economic theory2 and research in behavioural economics3, but behavioural measures are ultimately not sufficient to verify the proposal4. Consistent with the hypothesis, when agents make choices, neurons in the orbitofrontal cortex (OFC) encode the subjective value of offered and chosen goods5. Value-encoding cells integrate multiple dimensions6,7,8,9, variability in the activity of each cell group correlates with variability in choices10,11 and the population dynamics suggests the formation of a decision12. However, it is unclear whether these neural processes are causally related to choices. More generally, the evidence linking economic choices to value signals in the brain13,14,15 remains correlational16. Here we show that neuronal activity in the OFC is causal to economic choices. We conducted two experiments using electrical stimulation in rhesus monkeys (Macaca mulatta). Low-current stimulation increased the subjective value of individual offers and thus predictably biased choices. Conversely, high-current stimulation disrupted both the computation and the comparison of subjective values, and thus increased choice variability. These results demonstrate a causal chain linking subjective values encoded in OFC to valuation and choice.

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

Access options

Rent or buy this article

Prices vary by article type



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

Fig. 1: High-current stimulation of OFC disrupts valuation.
Fig. 2: Effects of electrical stimulation at different current levels.
Fig. 3: Prediction of range-dependent choice bias induced by electrical stimulation.
Fig. 4: Range-dependent choice bias induced by neuronal facilitation of OFC.

Similar content being viewed by others

Data availability

The complete dataset is available at data are provided with this paper.

Code availability

The MATLAB code used for the analysis is available at


  1. Niehans, J. A History of Economic Theory: Classic Contributions, 1720–1980 (Johns Hopkins Univ. Press, 1990).

  2. Kreps, D. M. A Course in Microeconomic Theory (Princeton Univ. Press, 1990).

  3. Kahneman, D. & Tversky, A. (eds) Choices, Values and Frames (Russell Sage Foundation–Cambridge Univ. Press, 2000).

  4. Camerer, C. F., Loewenstein, G. & Prelec, D. Neuroeconomics: how neuroscience can inform economics. J. Econ. Lit. 43, 9–64 (2005).

    Article  Google Scholar 

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

    Article  ADS  CAS  Google Scholar 

  6. Roesch, M. R. & Olson, C. R. Neuronal activity in primate orbitofrontal cortex reflects the value of time. J. Neurophysiol. 94, 2457–2471 (2005).

    Article  Google Scholar 

  7. 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 

  8. Pastor-Bernier, A., Stasiak, A. & Schultz, W. Orbitofrontal signals for two-component choice options comply with indifference curves of Revealed Preference Theory. Nat. Commun. 10, 4885 (2019).

    Article  ADS  Google Scholar 

  9. Hare, T. A., O’Doherty, J., Camerer, C. F., Schultz, W. & Rangel, A. Dissociating the role of the orbitofrontal cortex and the striatum in the computation of goal values and prediction errors. J. Neurosci. 28, 5623–5630 (2008).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  11. Conen, K. E. & Padoa-Schioppa, C. Neuronal variability in orbitofrontal cortex during economic decisions. J. Neurophysiol. 114, 1367–1381 (2015).

    Article  CAS  Google Scholar 

  12. Rich, E. L. & Wallis, J. D. Decoding subjective decisions from orbitofrontal cortex. Nat. Neurosci. 19, 973–980 (2016).

    Article  CAS  Google Scholar 

  13. Roesch, M. R. & Olson, C. R. Neuronal activity related to anticipated reward in frontal cortex: does it represent value or reflect motivation? Ann. NY Acad. Sci. 1121, 431–446 (2007).

    Article  ADS  Google Scholar 

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

    Article  CAS  Google Scholar 

  15. Bartra, O., McGuire, J. T. & Kable, J. W. The valuation system: a coordinate-based meta-analysis of BOLD fMRI experiments examining neural correlates of subjective value. Neuroimage 76, 412–427 (2013).

    Article  Google Scholar 

  16. Stalnaker, T. A., Cooch, N. K. & Schoenbaum, G. What the orbitofrontal cortex does not do. Nat. Neurosci. 18, 620–627 (2015).

    Article  CAS  Google Scholar 

  17. Cohen, M. R. & Newsome, W. T. What electrical microstimulation has revealed about the neural basis of cognition. Curr. Opin. Neurobiol. 14, 169–177 (2004).

    Article  CAS  Google Scholar 

  18. Clark, K. L., Armstrong, K. M. & Moore, T. Probing neural circuitry and function with electrical microstimulation. Proc. R. Soc. Lond. B 278, 1121–1130 (2011).

    Google Scholar 

  19. Salzman, C. D., Britten, K. H. & Newsome, W. T. Cortical microstimulation influences perceptual judgements of motion direction. Nature 346, 174–177 (1990).

    Article  ADS  CAS  Google Scholar 

  20. Murasugi, C. M., Salzman, C. D. & Newsome, W. T. Microstimulation in visual area MT: effects of varying pulse amplitude and frequency. J. Neurosci. 13, 1719–1729 (1993).

    Article  CAS  Google Scholar 

  21. Kuwabara, M., Kang, N., Holy, T. E. & Padoa-Schioppa, C. Neural mechanisms of economic choices in mice. eLife 9, e49669 (2020).

    Article  CAS  Google Scholar 

  22. Wolff, S. B. & Ölveczky, B. P. The promise and perils of causal circuit manipulations. Curr. Opin. Neurobiol. 49, 84–94 (2018).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  24. 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 

  25. Rustichini, A., Conen, K. E., Cai, X. & Padoa-Schioppa, C. Optimal coding and neuronal adaptation in economic decisions. Nat. Commun. 8, 1208 (2017).

    Article  ADS  Google Scholar 

  26. Histed, M. H., Bonin, V. & Reid, R. C. Direct activation of sparse, distributed populations of cortical neurons by electrical microstimulation. Neuron 63, 508–522 (2009).

    Article  CAS  Google Scholar 

  27. Ballesta, S. & Padoa-Schioppa, C. Economic decisions through circuit inhibition. Curr. Biol. 29, 3814–3824 (2019).

    Article  CAS  Google Scholar 

  28. Conen, K. E. & Padoa-Schioppa, C. Partial adaptation to the value range in the macaque orbitofrontal cortex. J. Neurosci. 39, 3498–3513 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  Google Scholar 

  30. Padoa-Schioppa, C. & Conen, K. E. Orbitofrontal cortex: a neural circuit for economic decisions. Neuron 96, 736–754 (2017).

    Article  CAS  Google Scholar 

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

    Article  Google Scholar 

  32. 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 

  33. Merrill, D. R., Bikson, M. & Jefferys, J. G. Electrical stimulation of excitable tissue: design of efficacious and safe protocols. J. Neurosci. Methods 141, 171–198 (2005).

    Article  Google Scholar 

  34. Kim, S. et al. Behavioral assessment of sensitivity to intracortical microstimulation of primate somatosensory cortex. Proc. Natl Acad. Sci. USA 112, 15202–15207 (2015).

    Article  ADS  CAS  Google Scholar 

  35. Lee, S. W., Eddington, D. K. & Fried, S. I. Responses to pulsatile subretinal electric stimulation: effects of amplitude and duration. J. Neurophysiol. 109, 1954–1968 (2013).

    Article  Google Scholar 

  36. Hussin, A. T., Boychuk, J. A., Brown, A. R., Pittman, Q. J. & Teskey, G. C. Intracortical microstimulation (ICMS) activates motor cortex layer 5 pyramidal neurons mainly transsynaptically. Brain Stimul. 8, 742–750 (2015).

    Article  Google Scholar 

  37. Tolias, A. S. et al. Mapping cortical activity elicited with electrical microstimulation using FMRI in the macaque. Neuron 48, 901–911 (2005).

    Article  CAS  Google Scholar 

  38. Stoney, S. D. Jr, Thompson, W. D. & Asanuma, H. Excitation of pyramidal tract cells by intracortical microstimulation: effective extent of stimulating current. J. Neurophysiol. 31, 659–669 (1968).

    Article  Google Scholar 

  39. Arsiero, M., Lüscher, H. R., Lundstrom, B. N. & Giugliano, M. The impact of input fluctuations on the frequency-current relationships of layer 5 pyramidal neurons in the rat medial prefrontal cortex. J. Neurosci. 27, 3274–3284 (2007).

    Article  CAS  Google Scholar 

  40. La Camera, G., Giugliano, M., Senn, W. & Fusi, S. The response of cortical neurons to in vivo-like input current: theory and experiment. I. Noisy inputs with stationary statistics. Biol. Cybern. 99, 279–301 (2008).

    Article  Google Scholar 

  41. Ethier, C., Brizzi, L., Darling, W. G. & Capaday, C. Linear summation of cat motor cortex outputs. J. Neurosci. 26, 5574–5581 (2006).

    Article  CAS  Google Scholar 

  42. Griffin, D. M., Hudson, H. M., Belhaj-Saïf, A. & Cheney, P. D. Hijacking cortical motor output with repetitive microstimulation. J. Neurosci. 31, 13088–13096 (2011).

    Article  CAS  Google Scholar 

  43. Van Acker, G. M. III et al. Effective intracortical microstimulation parameters applied to primary motor cortex for evoking forelimb movements to stable spatial end points. J. Neurophysiol. 110, 1180–1189 (2013).

    Article  Google Scholar 

Download references


We thank H. Schoknecht for help with animal training and J. Assad, E. Bromberg-Martin, E. Fehr, D. Freedman, I. Monosov and L. Snyder for comments on the manuscript. This research was supported by the National Institutes of Health (grants number R01-DA032758 and R01-MH104494 to C.P.-S. and grant number F31-MH107111 to K.E.C.) and by the McDonnell Center for Systems Neuroscience (pre-doctoral fellowship to W.S.).

Author information

Authors and Affiliations



S.B. and W.S. collected and analysed the data; K.E.C. designed experiment 2; and C.P.-S. supervised the project and wrote the manuscript. All of the authors edited the manuscript.

Corresponding author

Correspondence to Camillo Padoa-Schioppa.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature thanks Tianming Yang and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data figures and tables

Extended Data Fig. 1 Experiment 2, control for choice frequency.

We noticed that across sessions the difference in value range (ΔVA–ΔVB) was correlated with the fraction of trials in which the animal chose juice A (% A choice) and with the relative value (ρ). In principle, these correlations could represent confounding factors. Indeed, 50 μA stimulation could partly disrupt the valuation process. As a result, the animal might respond by defaulting to the juice type most frequently chosen in that session, or to the preferred juice type. If so, the range-dependent bias would be akin to the order bias (experiment 1), in the sense that it would result from functional disruption as opposed to facilitation. To address this concern, we identified a subset of sessions for which choices between the two juices were split almost evenly. In this subset of sessions, the difference in value range and the fraction of A choices were not correlated. We reasoned that if the range-dependent bias observed for the whole dataset was driven by a default to the most frequently chosen option, the bias should disappear when the analysis was restricted to this subset of sessions. However, this was not the case. In fact, the range-dependent bias measured for the selected subset was larger than that measured for the entire population. We concluded that range-dependent biases did not reflect simple heuristics. a, Correlation between the difference in value range and the fraction of A choices. Each data point represents one session. Considering the entire dataset (black data points, N = 96 sessions), the two measures were significantly correlated (r ≥ 0.71, P < 10−15, Pearson and Spearman correlation tests). We defined a small ellipse centred on coordinates [0, 50] (axes = [9, 14]). The ellipse identified a subset of data (pink data points, N = 31 sessions) for which the difference in value range and the fraction of A choices were not correlated (P ≥ 0.69, Pearson and Spearman correlation tests). b, Correlation between the difference in value range and the relative value. Considering the entire dataset, the two measures were significantly correlated (r ≥ 0.33, P ≤ 0.001, Pearson and Spearman correlation tests). However, when the analysis was restricted to the subset of sessions identified in a (pink data points), the correlation changed sign. c, Range-dependent bias, same data as in Fig. 4c, d. Considering the entire dataset, the change in relative value was significantly correlated with the difference in value range (r ≥ 0.34, P ≤ 0.0007, Pearson and Spearman correlation tests). The correlation did not dissipate when the analysis was restricted to the subset of sessions identified in a (pink data points; r ≥ 0.45, P ≤ 0.01, Pearson and Spearman correlation tests). In this figure, data from the two animals are combined. Grey and pink lines in the three panels were obtained from Deming regressions.

Source data

Extended Data Fig. 2 Experiment 2, results obtained in paired sessions.

In N = 33 instances, we ran two back-to-back sessions offering the same two juices and leaving the electrode in place, but changing the quantity ranges such that ΔVA–ΔVB would differ. a, Example of paired sessions. b, Population analysis. Each pair of sessions in the scatter plot is connected by a line, of which we computed the slope. Data points filled in green correspond to sessions in a. Data from the two monkeys are pooled. Across the population, slopes were typically >0 (P = 0.007, two-tailed Wilcoxon signed-rank test). Hence, range-dependent biases were not dictated by the juice pair or by the location of the electrode within OFC.

Extended Data Fig. 3 Experiment 2, analysis of response times (RTs).

a, Example session 1. Each data point represents one trial type and the two lines were obtained from linear regressions. Under normal conditions (stimOFF, black), RTs decreased as a function of the chosen value (x axis). Electrical stimulation (stimON, red) generally reduced RTs. Linear fits reveal that lower RTs were due to a lower intercept, as opposed to a steeper (that is, more negative) slope. b, c, Population analysis, monkey D (N = 35). For each session, we regressed RTs onto the chosen value, separately for stimOFF and stimON trials. We then compared the intercepts and the slopes at the population level. The picture emerging from a was confirmed for the population. In b (intercept), each data point represents one session. The population is significantly displaced below the identity line (P = 0.018, two-tailed Wilcoxon test). In c (slope), it can be noticed that the slope under stimulation was shallower (less negative), probably due to a floor effect. Filled data points correspond to the session shown in a. d, Example session 2. Same format as in a. e, f, Population analysis, monkey G (N = 61). Same format as in b, c. Electrical stimulation significantly lowered the intercept but did not significantly alter the slope. Filled data points correspond to the session shown in d. In b, c, e, f, values indicated in the insert refer to the difference between the stimON measure and the stimOFF measure, averaged across the population. All P values are from two-tailed Wilcoxon tests, and t-tests provided very similar results.

Source data

Extended Data Fig. 4 Experiment 1, range-dependent choice biases.

ac, Results obtained when electric current was delivered at 25 μA, 50 μA and ≥ 100 μA. In each panel, x and y axes represent the difference in value range (in uB) and the difference in relative value, respectively. Each data point represents one session. Sessions from the two animals and with different stimulation times (offer 1 or offer 2) were pooled. Grey lines were obtained from linear regressions. Each panel indicates the P values obtained from Pearson and Spearman correlation tests. In essence, the choice bias imposed by the stimulation (δρ) was correlated with the difference in value ranges (ΔVA–ΔVB) at low current (25 μA; weakly) and intermediate current (50 μA), but not at high current (≥100 μA).

Source data

Extended Data Fig. 5 Stimulation in experiment 2 did not systematically alter the sigmoid steepness.

For this analysis, the two groups of trials (stimOFF, stimON) were examined separately (see Methods). The two axes represent the sigmoid steepness in the two conditions. Sessions from the two animals were pooled (N = 95, 2 outliers removed), and each data point represents one session. The grey ellipse represents the 90% confidence interval. The P value is from a Wilcoxon test and similar results were obtained with a t-test.

Source data

Extended Data Fig. 6 Experiment 1, interpretation of the order bias.

a, Decelerating response function. The black line represents an ideal response function, which relates the number of spikes emitted by a cell in a given time window (y axis) to the synaptic current entering the cell (x axis). In the condition highlighted in yellow, IO is the synaptic current due to the offer on the monitor, r is the corresponding response, IS is the synaptic current due to the stimulation, and δr is the corresponding increase in the number of spikes. The condition highlighted in blue is similar, except that IO is larger (IO,blue > IO,yellow). Because the response function is decelerating, δr in the blue condition is smaller (δrblue < δryellow). In experiment 1, only one good was presented at the time. Neurons associated with that good were naturally more active (higher IO) than neurons associated with the other good. Thus deceleration in the response function induced a bias favouring the good not offered during the stimulation (order bias). For given IO,yellow and IO,blue, the difference δryellow – δrblue increases with IS. Hence, higher stimulation currents induced larger order biases. b, Concurrent presence of order bias and range-dependent bias. The cartoon illustrates an ideal session in experiment 1. We assume that under normal conditions there is no order bias (stimOFF, continuous lines). Thus the two sigmoids for AB trials and BA trials coincide. We also assume that stimulation is delivered during offer 1, and that ΔVA–ΔVB > 0. The order bias separates the two sigmoids such that under stimulation the sigmoid for AB trials is on the left of that for BA trials (stimON, dashed lines). The range-dependent bias imposes a shift on the total sigmoid, including both AB and BA trials (not shown), which moves to the right compared to normal conditions. The two choice biases are complementary and independent.

Extended Data Table 1 Exact P values for the statistical tests used for Fig. 2
Extended Data Table 2 Dataset for experiment 1

Supplementary information

Source data

Rights and permissions

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ballesta, S., Shi, W., Conen, K.E. et al. Values encoded in orbitofrontal cortex are causally related to economic choices. Nature 588, 450–453 (2020).

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI:

This article is cited by


By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.


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