Dopamine Receptor-Specific Contributions to the Computation of Value

Abstract

Dopamine is thought to play a crucial role in value-based decision making. However, the specific contributions of different dopamine receptor subtypes to the computation of subjective value remain unknown. Here we demonstrate how the balance between D1 and D2 dopamine receptor subtypes shapes subjective value computation during risky decision making. We administered the D2 receptor antagonist amisulpride or placebo before participants made choices between risky options. Compared with placebo, D2 receptor blockade resulted in more frequent choice of higher risk and higher expected value options. Using a novel model fitting procedure, we concurrently estimated the three parameters that define individual risk attitude according to an influential theoretical account of risky decision making (prospect theory). This analysis revealed that the observed reduction in risk aversion under amisulpride was driven by increased sensitivity to reward magnitude and decreased distortion of outcome probability, resulting in more linear value coding. Our data suggest that different components that govern individual risk attitude are under dopaminergic control, such that D2 receptor blockade facilitates risk taking and expected value processing.

Figure 1

Potential effects of amisulpride on utility function, illustration of example trial, and observed effects of amisulpride on choice behavior. (a, b) Prospect theory proposes an asymmetrical, nonlinear, and concave and convex mapping of subjective value on increasing monetary gains and losses that results in different risk attitudes of individuals. (a) Potential effect of D2/D3 blockade on reducing the curvature of the value function (red dashed line; prospect theory utility curvature parameter σ=0.7) compared with placebo (blue; σ=0.5). (b) Potential effect of D2/D3 blockade on reducing loss aversion (red dashed line; prospect theory loss aversion parameter λ=1.5) compared with placebo (blue line; λ=2). Note that solely manipulating the loss aversion parameter does not affect value sensitivity in the gain domain. (c) Example trial. After a fixed intertrial interval of 2 s, participants made a self-paced choice (20 in total) between risky options that varied in gain and loss magnitudes and probabilities and that were presented on the left and right sides of the screen. (d) Participants in the amisulpride group chose the riskier (higher variance) option significantly more often than participants in the placebo group, indicating decreased risk aversion. (e) Amisulpride resulted in more frequent choices of the high expected value option, consistent with increased value sensitivity.

Figure 2

Observed effects of amisulpride on subjective value function from prospect theory. (a) Fitting a prospect theory model to participants’ choices revealed a significantly more linear value function for the amisulpride (red) relative to the placebo group (blue). (b) These findings were supported by utility curvature (σ) parameters being significantly closer to 1, ie, linearity (gray dashed line) in the amisulpride group. Boxplots show that the mean (+) and median (−) coincided; *** indicates p<0.001; boxes represent 25th and 75th percentiles and whiskers the 9th and the 91st percentile. (c) Recovered value functions for different reward magnitudes revealed a steeper and more linear function for the amisulpride group (red), indicating lower risk aversion when compared with the placebo group (blue) (d). As both utility curvature and probability weighting (Figure 3) functions were more linear under amisulpride, sensitivity to increases in the expected value of the option should also be increased. This was confirmed by a logistic regression of choices of the higher expected value option against the expected value of the option that allowed us to estimate regression weights as a proxy for the expected value sensitivity in the two groups. Participants rarely encountered extremely high option magnitudes as they were associated with high risk. Accordingly, we could not plot an error bar for the amisulpride group at CHF 1000. Error bars represent the SEM.

Figure 3

Effects of amisulpride on probability distortion and loss aversion. (a) Average probability weighting functions were less distorted in the amisulpride group (red) than in the placebo group (blue). (b) Accordingly, probability distortion (α) parameters were significantly closer to 1 for the amisulpride compared with the placebo group. (c) Groups did not differ significantly in mean loss aversion (λ) parameters. In (b) and (c), boxplots show the mean (+) and median (−); *** indicates p<0.001; n.s. indicates no significant difference; boxes represent 25th and 75th percentiles and whiskers the 9th and the 91st percentile, with the gray dashed line representing 1 (ie, linearity). (d) To better visualize individual probability weighting functions we randomly selected 15 participants in the placebo group (whole group shown in inset). Similar to the full group, probability distortions were more pronounced and varied more widely in these participants. (e) In contrast, the amisulpride group showed more homogeneous and reduced probability distortions, both in randomly selected 15 participants and in the full group (inset).

Figure 4

Estimated model frequencies from Bayesian model comparison. (a) For the placebo group, prospect theory (PT) best explained choices in 38/48 participants, and was the best fitting model overall (highest exceedance probability) in comparison with expected utility (EU) or expected value (EV) models. (b) In contrast, the expected utility model best explained choices in the amisulpride group, with 28/45 participants being classified as EU types. Dashed lines in both panels denote the probability that all models perform equally well at best fitting participants.