The valuation system: a coordinate-based meta-analysis of BOLD fMRI experiments examining neural correlates of subjective value

Abstract

Numerous experiments have recently sought to identify neural signals associated with the subjective value (SV) of choice alternatives. Theoretically, SV assessment is an intermediate computational step during decision making, in which alternatives are placed on a common scale to facilitate value-maximizing choice. Here we present a quantitative, coordinate-based meta-analysis of 206 published fMRI studies investigating neural correlates of SV. Our results identify two general patterns of SV-correlated brain responses. In one set of regions, both positive and negative effects of SV on BOLD are reported at above-chance rates across the literature. Areas exhibiting this pattern include anterior insula, dorsomedial prefrontal cortex, dorsal and posterior striatum, and thalamus. The mixture of positive and negative effects potentially reflects an underlying U-shaped function, indicative of signal related to arousal or salience. In a second set of areas, including ventromedial prefrontal cortex and anterior ventral striatum, positive effects predominate. Positive effects in the latter regions are seen both when a decision is confronted and when an outcome is delivered, as well as for both monetary and primary rewards. These regions appear to constitute a "valuation system," carrying a domain-general SV signal and potentially contributing to value-based decision making.

Figure 1

Two hypothetical profiles for regional BOLD as a function of SV. Pattern A (linear) represents a monotonically increasing response for more valuable outcomes, while Pattern B (quadratic) represents a greater response to more extreme outcomes, either rewards or penalties.

Figure 2

Effect of gray-matter probability (pGM) on the density of reported activation foci, across all results in our corpus. A: Voxels with nonzero pGM were divided into five equal-sized bins. Foci density increased as a function of pGM. We therefore account for pGM in our null hypothesis. B: Illustration of the pGM bins (lighter color denotes higher pGM).

Figure 3

Whole-brain meta-analysis of positive and negative responses. A: Significant clustering of positive responses. B: Significant clustering of negative responses. C: Conjunction maps, showing regions with significant clustering for both positive and negative responses. D: Results of a between-category comparison, showing regions with significantly greater clustering for positive than negative effects. E: Detail of the striatum, illustrating overlap between the conjunction map (Panel C) and the difference map (Panel D). In the overlapping region (orange), positive effects cluster more densely than negative effects, which in turn still cluster more densely than expected by chance.

Figure 4

Region-of-interest (ROI) analysis comparing positive and negative effects of SV on BOLD. A: Illustration of the two ROI masks. B: Scatterplot of foci for positive and negative effects in striatum. All foci are projected onto a single sagittal slice for visualization. Note that the five posterior/inferior foci fall in the putamen, lateral to the anatomical slice shown (|x|>=28). C: Permutation-based null distribution (gray histogram bars) and actual results (red line) for spatial logistic regression based on the data in Panel B.

Figure 5

Whole-brain meta-analysis of positive and negative effects, accounting for valence. A: Significant clustering of positive effects in the domain of rewards. B: Significant clustering of negative effects in the domain of penalties. C: Overlap of the results in Panels A and B. This conjunction of effects would be expected in a region in which the BOLD response is a U-shaped function of SV (see Figure 1, Pattern B).

Figure 6

Whole-brain meta-analysis of SV effects at either the time a decision is evaluated, or the time an outcome is received. These results consider positive effects only. A: SV effects at the decision stage. B: SV effects at the outcome receipt stage. C: Conjunction map, showing voxels with significant positive effects during both decision making and outcome receipt. An SV signal occurring in both these time periods is potentially positioned to guide value-based decision making. D: Regions showing significantly denser clustering of positive effects during the decision stage than during the outcome receipt stage.

Figure 7

Negative effects of SV on BOLD, broken down according to the time period at which effects are examined. A: Negative effects overall (identical to Figure 3B), reproduced here for reference. B: Negative effects limiting to the outcome receipt stage (55 studies). Results resemble those in Panel A except for the absence of effects in striatum. C: Significant clustering of negative effects in studies examining the anticipation stage of the MID task (11 studies); here, effects in striatum are strongly present.

Figure 8

Whole-brain meta-analysis results for monetary vs. primary rewards and penalties. These analyses are limited to positive effects at the time of outcome receipt. A: SV effects for monetary outcomes. B: SV effects for primary outcomes (see text for details). C: Conjunction of SV effects for both categories of outcomes. D: Regions with more densely clustered SV effects for monetary than primary outcomes.

Figure 9

Results of a five-way conjunction analysis, designed to detect regions carrying a monotonic, modality-independent SV signal. Binary map indicates voxels that showed significantly greater density of foci for positive than negative effects (Fig. 3D), and showed above-chance clustering for positive effects at both the decision and receipt stages (Fig. 6C), as well as for both monetary and primary outcomes (Fig. 8C).

Figure 10

Meta-analytic results obtained from the Neurosynth database at http://neurosynth.org/ (Yarkoni et al., 2011). Each map is based on foci reported in papers associated with a specific key term. A: Results for the term “reward.” B: Results for the term “punishment.” Despite using different methods and a largely non-overlapping sample of publications, these results resemble the analogous maps obtained in the present investigation (Figs. 3A and 3B, respectively).