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Behavioral read-out from population value signals in primate orbitofrontal cortex

Nature Neuroscience volume 26, pages 2203–2212 (2023)Cite this article

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Abstract

The primate orbitofrontal cortex (OFC) has long been recognized for its role in value-based decisions; however, the exact mechanism linking value representations in the OFC to decision outcomes has remained elusive. Here, to address this question, we show, in non-human primates, that trial-wise variability in choices can be explained by variability in value signals decoded from many simultaneously recorded OFC neurons. Mechanistically, this relationship is consistent with the projection of activity within a low-dimensional value-encoding subspace onto a potentially higher-dimensional, behaviorally potent output subspace. Identifying this neural–behavioral link answers longstanding questions about the role of the OFC in economic decision-making and suggests population-level read-out mechanisms for the OFC similar to those recently identified in sensory and motor cortex.

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Fig. 1: Decision-making task and performance in two monkeys.
Fig. 2: OFC recording and population decoding of choice.
Fig. 3: Decoding choice over time, neuron dropping and controls.
Fig. 4: Correlations between LASSO regression weights and between NVEs.
Fig. 5: Relationship among value encoding weights, choice subspace weights and single-cell choice probabilities.
Fig. 6: Properties of the OFC choice subspace.

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Data availability

The data are being prepared for upload to a public repository. Once uploaded, the data will be provided without restrictions. Links to the data will be available on the laboratory website (https://www.mcgintylab.org/). Before being uploaded, the data will be available from the corresponding author upon reasonable request.

Code availability

The code used to analyze the data and generate figures are being prepared for upload to a public repository. Once uploaded, the code will be provided without restrictions. Links to the code will be available on the laboratory website (https://www.mcgintylab.org/). Before being uploaded, the code will be available from the corresponding author upon reasonable request.

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Acknowledgements

We acknowledge W. T. Newsome for funding, material support and thoughtful discussions; A. Rangel for thoughtful discussions; J. Brown, E. Carson, S. Fong, A. McCormick, M. Ortiz, J. Powell, J. Sanders and D. Siegel for technical assistance; and M. Cohen, J. Corbo, R. Ebitz, D. Headley, G. Karpov, D. Kimmel, M. Rosenberg-Lee, K. Peterson and D. Sharma for helpful comments on the manuscript. This work was supported by the Howard Hughes Medical Institute (W. T. Newsome), National Institutes of Health grant K01-DA-036659-01 (V.B.M.), the Busch Biomedical Foundation (V.B.M.), a Whitehall Foundation Fellowship (V.B.M.), the Rutgers Dean’s Dissertation Fellowship (S.M.L.) and the Rutgers Academic Advancement Fund (S.M.L.).

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Authors and Affiliations

  1. Center for Molecular and Behavioral Neuroscience, Rutgers University–Newark, Newark, NJ, USA

    Vincent B. McGinty & Shira M. Lupkin

  2. Behavioral and Neural Sciences Graduate Program, Rutgers University–Newark, Newark, NJ, USA

    Shira M. Lupkin

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Contributions

V.B.M. designed the research. V.B.M. and S.M.L. performed the research. V.B.M. and S.M.L. analyzed the data. V.B.M. and S.M.L. wrote the paper.

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Correspondence to Vincent B. McGinty.

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Extended data

Extended Data Fig. 1 Task sequence and target shape primitives.

(a) Illustration of the complete task sequence. Note that for illustration purposes the background is shown as white, and the surrounding crowders are shown at reduced saturation relative to the centrally located targets. However, on the actual task display, the background was dark, and target luminance was reduced relative to the crowders (see Fig. 1c), in order to better obscure the targets when they are not being viewed directly. (b) The 7 shape primitives used to generate choice stimuli. To create a new stimulus set, four shapes are selected without replacement from the 7 primitives; then each is rotated randomly by 0, 90, 180, 270 degrees. The four rotated shapes are then combined with four colors selected randomly from equally-spaced points on a color wheel defined within the RGB gamut in the CIELUV color space. A sub-set of 12 targets is then selected, and assigned to reward values ranging from 1–5 drops (Fig. 1b).

Extended Data Fig. 2 Recoding locations.

OFC recordings targeted the medial orbital gyrus, as well as the lateral bank of the medial orbital sulcus, over an anterior-posterior range of approximately +32 to +38 mm relative to the intra-aural landmark. A representative anterior and posterior MR section (T2-weighted) is shown for each animal, with representative array trajectories shown in green. In monkey C, recordings were from the left hemisphere only; in monkey K, recordings were typically obtained bi-laterally. Scale bars are 10 mm.

Extended Data Fig. 3 Comparing results computed using order vs. spatial reference frame.

(a) The encoding of spatial variables by single cells was measured using CPD, as in Fig. 2d,e (see Methods). The variables were the value of the left offer (1–5 drops of juice), value of the right offer (1–5 drops), whether the left or right offer was chosen (Boolean), and the chosen value (1–5 drops). Means and s.e.m. are over 1450 single cells. (b) Data reproduced from Fig. 2e showing encoding of order-based variables in 1450 single cells. (c) Encoding of the variable ‘1st chosen’ from panel (b) plotted with the encoding of the variable ‘left/right chosen’ from panel (a). (d) Decoding of choice outcomes in a spatial reference frame (green) compared to order-based decoding (gray, data taken from Fig. 6c, ‘all dims’). Decoding was performed using the same approach as in Fig. 6c, except using spatial variables, that is: data were first residualized with respect to the identities of the left and right offer values, and held out trials were then decoded in terms of whether the left or right offer was chosen. Filled significance indicators: corrected p < 0.05 compared to 0.5; open significance indicators: uncorrected p < 0.05 comparing the gray and green lines. Otherwise, conventions are the same as in Fig. 6c.

Extended Data Fig. 4 Decoding of offer values in held-out test trials.

Decoding of offer values in held out test trials, using decoding models fit to ‘1st value’ and ‘2nd value’. Unlike the decoding of choice outcomes, decoding of values was performed using the non-normalized \({\hat{\bf{Y}}}\) from equation (3), rather than the condition-normalized NVEs. Within each session, classification was performed for adjacent levels of offer values (1 drop vs. 2 drops, 2 vs. 3, 3 vs. 4, and 4 vs. 5) and then averaged across the four adjacent-level classifications. The lines and shading indicate the mean and s.e.m. of decoding accuracy across 32 sessions. The solid lines give the results of fitting, decoding, and classification performed over the native spiking data, utilizing all of the neural dimensions. The dashed lines give the results when fitting, decoding, and classifying data that were first projected into the subspace orthogonal to subspaces for ‘1st value’ and ‘2nd value’ (that is the subspace orthogonal to the plane in Fig. 6a, defined by weights obtained from equation (2)) The fact that value cannot be decoded outside of the two main value dimensions indicates that the representation of value is low-dimensional. In comparison, significant decoding of choice outcomes is evident outside of the two main value dimensions (Fig. 6c, orange).

Extended Data Fig. 5 Results from individual monkeys and various control conditions.

(a-h) Choice decoding and neuron dropping analyses for individual monkeys. Same format and conventions as Fig. 3. In panels (c) and (g), p-values indicate significant differences (uncorrected). For boxplots, thick lines and boxes show the median and IQR; whisker minima/maxima are up to three times the IQR. (i) The same data used to generate Fig. 3a,b, except time-locked to the decision RT, defined as the lift of the center lever. Note that when the data are aligned to the RT the first and second target viewing times (black and blue boxplots, respectively) are widely spread in time and have substantial overlap. This overlap creates an artifact in which the area under the ROC curve for the ‘1st value’ decoder briefly goes below 0.5 (whereas in Fig. 3a,b it is always above 0.5). This is because within this brief time window, the encoding subspaces for ‘1st value’ and ‘2nd value’ are aligned such that neural activity related to ‘2nd value’ projects onto the ‘1st value’ subspace. In other words, the ‘1st value’ decoder becomes contaminated by spiking activity related to the second offer value, resulting in predictions driven by the value of the second offer (that is that are below 0.5). Note that the subspace alignment and resulting contamination arises only because of the large spread between the first and second target viewing times when the data are RT locked. When the data are locked onto target viewing, the decoder weights for ‘1st value’ and ‘2nd value’ are uncorrelated (Fig. 4a), indicating orthogonal decoding subspaces. (j) Choice decoding and neuron dropping analyses when using non-condition-normalized data to calculate the area under the ROC curve37. In this method, trials were grouped according to 66 unique conditions, defined as the combination of the first and second target identities. The non-condition-normalized data (\({\mathbf{\hat{Y}}}\) from equation (3)) were then used to calculate a separate AUC within each condition. Finally, within each session the AUCs were averaged across conditions to compute the session-wise AUC. AUCs could not be calculated for conditions without at least one trial of each choice outcome (first or second offer chosen); because there were 66 unique conditions, there were often too few trials per condition, and as a result ~65% of test trials were discarded. For this reason, the main analysis uses normalized decodes, so that data from all eligible test trials can contribute to the AUC calculation. All conventions are the same as in Fig. 3a,b. (k): Choice decoding as in Fig. 3a,b was performed using only those test trials in which the offers were equal in value (mean 43.9 trials in 16 sessions for monkey K and 51.7 trials in 16 sessions for monkey C). Filled significance indicators: corrected p < 0.05 compared to 0.5; open indicators: uncorrected P < 0.05 compared to 0.5. Otherwise, conventions are the same as in Fig. 3a,b.

Extended Data Fig. 6 Neural–behavioral logistic model.

Logistic regression was used to explain the choice of first vs. second offers with neural and behavioral variables described in equation (4). (a) Boxplots of regression estimates for n = 32 sessions. Thick lines and boxes show the median and IQR; whisker minima/maxima are up to three times the IQR. Estimates are in arbitrary units. Significance indicators indicate difference from zero by two-sided t-test. (b) Estimates from neural signal variables in panel (a) (full model) were no different from estimates from a model containing only the first four terms in equation (4) (minimal model). (c) Estimates from models fit using only trials with value difference of 1 drop compared to estimates fit using only trials with equal-value offers (dark vs. light bars, n = 32 sessions each). Significance indicators give difference between dark and light bar in each pair by paired, two-sided t-test. No significant differences were found after p-value correction for multiple comparisons. Note that an estimate for ‘Chosen value’ was not obtained for ‘value difference 0’, because this variable is constant within this group of trials.

Extended Data Fig. 7 Decoding performance vs. number of cells used.

(a) Choice decoding accuracy as a function of the number of cells recorded in each session, using the data from the ‘late’ epoch in Fig. 3b. Solid lines give linear fit. For ‘1st value’ the Pearson’s correlation was 0.015 (p = 0.94), and for ‘2nd value’ was 0.087 (p = 0.64) P-values reflect two-sided test where null hypothesis is r = 0, by transformation to Student’s t-values. (b-c) Choice decoding effects referenced to second target viewing, using cells meeting ‘strict’ sort quality criteria ( < 1% of ISIs shorter than 2 ms, and <10% of ISIs estimated to come from rogue spikes) or ‘liberal’ sorting criteria ( < 50% of ISIs from rogue spikes, no restriction on ISIs shorter than 2 ms). Mean cells per session for strict criteria were 23.9, and for liberal criteria were 77.6. Conventions are the same as in Fig. 3b.

Extended Data Fig. 8 Chosen value relationship with choice decoding.

(a) Data are taken from the late epoch in Fig. 3b. Solid lines indicate ordinary least squares fit; dashed lines indicate robust fit from MATLAB function ‘robustfit’ with default arguments. Statistics r and ρ indicate Pearson’s and Spearman’s coefficients, respectively. P-values reflect two-sided test against the null hypothesis of r = 0 or ρ = 0. The x-axis gives the per-cell weights that define the choice subspace (see Methods), and the y-axis gives the per-cell weights obtained by fitting equation (2) to the variable ‘chosen value’ – that is the chosen value weights. (b) The choice decoding analysis in Fig. 6c was repeated after first projecting the spiking data into a subspace that is orthogonal to the subspace encoding chosen values. This was done with the same procedure used to project data into the subspace orthogonal to offer values, except that the matrix B has only one column, given by weights obtained by fitting equation (2) to the variable ‘chosen value’ (the same weights as in panel a). This effectively removed information about chosen values from the spiking data, after which the other steps necessary to reproduce Fig. 6c were performed. Conventions are as in Fig. 6c.

Extended Data Fig. 9 Pairwise noise correlations.

Pairwise noise correlations measured from spike counts over a 200 ms window centered 200 ms after fixation onto the first target. This window was chosen because it contains the bulk of the response to the first-viewed target, but entirely excludes encoding of the second viewed target (see Fig. 2d). Before calculation of the noise correlations, spike counts were first z-scored according to the identity of the first target and whether the first fixation location was left or right. Cells weights for first offer value were obtained by equation (2) with LASSO regularization, and designated as zero, positive, or negative according to the sign of the weights. (a) Histogram of noise correlations for all simultaneously recorded cells pairs (n = 39,049). (b) Box plots of noise correlations between pairs of cells defined by the sign of the encoding weight for ‘1st value’. N = 26,734 cell pairs for zero-zero; n = 718 for pos-pos; n = 589 for neg-neg; n = 537 for pos-neg. Thick lines and boxes show the median and IQR; whisker minima/maxima are up to 1.5 times the IQR. The horizontal line at 0.25 indicates the vertical range of the bar plot in panel C. (c) Same data as in (b) plotted as means and standard errors. A one-way ANOVA indicated a significant effect of group (F3,28536 = 4.2, p = 0.006).

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McGinty, V.B., Lupkin, S.M. Behavioral read-out from population value signals in primate orbitofrontal cortex. Nat Neurosci 26, 2203–2212 (2023). https://doi.org/10.1038/s41593-023-01473-7

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