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.
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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.).
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Integrated supplementary information
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).
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.
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).
(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.
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.
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).
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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
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