1. Ventromedial Prefrontal Cortex Encodes a Latent Estimate of Cumulative Reward 
Keno Juechems, Jan Balaguer, Maria Ruz, Christopher Summerfield 
Neuron 
Volume 93, Issue 3, Pages e4 (February 2017)
DOI: /j.neuron
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2. Figure 1 Task and Behavioral Results
(A) The task is shown with an example sequence of responses, outcomes, and their combination into cumulative reward.
(B) Logistic regression of choice in RM 1, where ∗∗∗p < 0.001, ∗∗p < 0.01, and ∗p < 0.05 for a two-tailed t test against zero with 19 degrees of freedom. Bar height is mean and error bars are SEM.
(C) Effect of most recent choice and outcome on accept probability on current trial. Circles are across-subject mean with SEM overlaid.
(D) Example subject data (left panel) and group average of accept probability (right panel). Crosses correspond to mean accept probabilities for a given EV for below-median cumulative (light crosses) and above-median cumulative (dark crosses). Dashed lines correspond to best-fitting sigmoid. Note that some subjects have sigmoids that are more shifted to the left (risk-seeking on average) or vice versa.
(E) Differences in estimated sigmoid shifts and slopes for high versus low cumulative reward. Shift and slope differences are expressed as the estimates for high (above-median within-participant) cumulative reward minus the estimates for low cumulative reward. Each circle represents one participant.
(F) Accept probability as a function of cumulative reward (over the ten deciles). Error bars are SEM.
Neuron  , e4DOI: ( /j.neuron )
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3. Figure 2 BOLD Effects of Outcome and Cumulative Reward
(A) Sagittal and coronal slices are shown for the outcome contrast depicting the clusters in the vmPFC and the ventral striatum (global peak).
(B) Sagittal and coronal slices depicting the clusters in the vmPFC and angular gyrus (global peak). All images are on a common color scale with threshold at p < 0.001, unc., uncorrected.
Neuron  , e4DOI: ( /j.neuron )
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4. Figure 3 Computational Models of Behavior
(A) Schematic depicts the rationale of prospect theory, where wins, losses, and probabilities are passed through nonlinearities and then combined into a utility value, upon which the decision policy is based. The arrows show a subset of models (labels from main text) and where cumulative reward changes their computation.
(B) Adaptive gain model. The indifference point of the sigmoid is shifted from its initial value (black) toward the right for incurred gains (green), toward the left for incurred losses (red), and for the certain loss incurred from reject choices (gray).
(C) Policy simulation of a dynamic agent for an example sequence of 21 trials. The upper panel depicts the average payoff at the end of the sequence, the middle panel shows the variance of the payoffs across simulations, and the lower panel shows the probability of incurring payoffs above zero. All panels are as a function of update parameter α.
(D) Logistic regression of the choices predicted by the adaptive model in RM1. Bar height is mean and error bars are SEM (∗∗∗p < and ∗∗p < 0.01 for a two-tailed t test against zero with 19 degrees of freedom).
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5. Figure 4 BOLD Effects of DV by Choice Interaction
(A) Sagittal and axial slices depicting the dmPFC (global peak) and insula clusters on a common color scale. Images are thresholded at p < 0.001, unc. from GLM2.
(B) Encoding of DV (BOLD beta) in the dmPFC, insula, and vmPFC depending on choice from GLM3. Data from these three regions were extracted from the ROI described in the STAR Methods. Bar height is across-participant mean, and error bars are SEM (∗∗∗p < for a two-tailed t test against zero with 19 degrees of freedom).
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6. Figure 5 BOLD Effect of Proximity
Sagittal and coronal slices depicting clusters (circles) found for the DV by choice interaction in Figure 4. Images are on a common color scale and thresholded at p < 0.001, unc.
Neuron  , e4DOI: ( /j.neuron )
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