Dopaminergic Modulation of Human Intertemporal Choice: A Diffusion Model Analysis Using the D2-Receptor Antagonist Haloperidol

Abstract

The neurotransmitter dopamine is implicated in diverse functions, including reward processing, reinforcement learning, and cognitive control. The tendency to discount future rewards over time has long been discussed in the context of potential dopaminergic modulation. Here we examined the effect of a single dose of the D2 receptor antagonist haloperidol (2 mg) on temporal discounting in healthy female and male human participants. Our approach extends previous pharmacological studies in two ways. First, we applied combined temporal discounting drift diffusion models to examine choice dynamics. Second, we examined dopaminergic modulation of reward magnitude effects on temporal discounting. Hierarchical Bayesian parameter estimation revealed that the data were best accounted for by a temporal discounting drift diffusion model with nonlinear trialwise drift rate scaling. This model showed good parameter recovery, and posterior predictive checks revealed that it accurately reproduced the relationship between decision conflict and response times in individual participants. We observed reduced temporal discounting and substantially faster nondecision times under haloperidol compared with placebo. Discounting was steeper for low versus high reward magnitudes, but this effect was largely unaffected by haloperidol. Results were corroborated by model-free analyses and modeling via more standard approaches. We previously reported elevated caudate activation under haloperidol in this sample of participants, supporting the idea that haloperidol elevated dopamine neurotransmission (e.g., by blocking inhibitory feedback via presynaptic D2 auto-receptors). The present results reveal that this is associated with an augmentation of both lower-level (nondecision time) and higher-level (temporal discounting) components of the decision process.SIGNIFICANCE STATEMENT Dopamine is implicated in reward processing, reinforcement learning, and cognitive control. Here we examined the effects of a single dose of the D2 receptor antagonist haloperidol on temporal discounting and choice dynamics during the decision process. We extend previous studies by applying computational modeling using the drift diffusion model, which revealed that haloperidol reduced the nondecision time and reduced impulsive choice compared with placebo. These findings are compatible with a haloperidol-induced increase in striatal dopamine (e.g., because of a presynaptic mechanism). Our data provide novel insights into the contributions of dopamine to value-based decision-making and highlight how comprehensive model-based analyses using sequential sampling models can inform the effects of pharmacological modulation on choice processes.

Keywords: computational modeling; decision making; dopamine; haloperidol; intertemporal choice; pharmacology.

Figure 1.

a, Overall RT distributions for the placebo group (n = 26) and the haloperidol group (n = 23). Negative RTs reflect choices of the SS option, whereas positive RTs reflect choices of the LL option. It can be seen that participants in the placebo group made numerically more SS selections than participants in the haloperidol group. For individual subject RT distributions, see Extended Data Figure 1-1. For minimum RTs following trial filtering, see Extended Data Figure 1-2. b, Proportion of LL choices per group and magnitude condition.

Figure 2.

Modeling results (blue: placebo, orange: haloperidol) from a hierarchical Bayesian Model with softmax choice rule. a, Log(k) is the log(discount rate) from the high magnitude condition (smaller-sooner reward = 100€). b, Log(k)_shift is the change in log(k) from the high magnitude condition to the low magnitude condition (smaller-sooner reward = 20€). c, is the inverse temperature parameter. d,_shift the corresponding shift in inverse temperature from the high to low magnitude condition. The thin (thick) horizontal lines denote 95% (85%) highest posterior density intervals.

Figure 3.

Posterior distributions (blue: placebo, orange: haloperidol) per parameter (top row: a, Log(k); b, Drift rate coefficient; c, Nondecision time; d, Boundary separation; e, Starting point bias; f, Drift rate maximum) and group differences (bottom row, placebo–haloperidol) for the baseline condition (smaller-sooner reward = 100€). Thin (thick) horizontal lines denote 95% (85%) highest posterior density intervals.

Figure 4.

Posterior distributions (blue: placebo, orange: haloperidol) of the change in each parameter from the high magnitude (baseline) to the low magnitude condition (top row: a, Log(k)_shift; b, Drift rate coefficient_shift; c, Nondecision time_shift; d, Boundary separation_shift; e, Starting point bias_shift; f, Drift rate maximum_shift) and corresponding group differences (bottom row, placebo–haloperidol). Thin (thick) horizontal line denote 95% (85%) highest posterior density intervals.

Figure 5.

Correlations between all single-subject median posterior parameter estimates across participants from the haloperidol (a) and placebo group (b).

Figure 6.

Modeling results (blue: placebo, orange: haloperidol) from a hierarchical linear regression with decision conflict as a predictor and 1/RT as dependent variable. Top row: The slope in a, represents the influence of increasing decision conflict (decreasing value differences) on 1/RT. The intercept in c, here corresponds to 1/RT for the lowest decision conflict (highest subjective value difference) from the high magnitude condition (smaller-sooner reward = 100€). Shift-parameters again reflect the change in slope and intercept (b, d) from the high to the low magnitude condition. e, Illustrates 1/RT predicted by this regression model as a function of group, condition and decision conflict. Bottom row: Corresponding group differences (placebo–haloperidol). The thin (thick) horizontal lines denote 95% (85%) highest posterior density intervals.

Figure 7.

Placebo condition posterior predictive checks. For each participant and condition (high (left facet) represents the high magnitude condition; low (right facet) represents the low magnitude condition), trials were binned into five equal sized bins according to the absolute difference in between subjective LL and SS options (decision conflict bin). Plotted are mean observed RTs per bin (data) as well model-generated RTs (blue represents DDM₀; red represents DDM_lin; orange represents DDM_S) averaged >10,000 datasets simulated from the posterior distribution of each hierarchical model (blue represents DDM₀; red represents DDM_lin; orange represents DDM_s).

Figure 8.

Haloperidol condition posterior predictive checks. For each participant and condition (high (left facet) represents the high magnitude condition; low (right facet) represents the low magnitude condition), trials were binned into five equal sized bins according to the absolute difference in between subjective LL and SS options (decision conflict bin). Plotted are mean observed RTs per bin (data) as well model-generated RTs (blue represents DDM₀; red represents DDM_lin; orange represents DDM_S) averaged >10,000 datasets simulated from the posterior distribution of each hierarchical model (blue represents DDM₀; red represents DDM_lin; orange represents DDM_s).

Figure 9.

Parameter recovery analysis for all Baseline parameters using the DDM_s (a, Log(k); b, Drift rate coefficient; c, Nondecision time; d, Boundary separation; e, Starting point bias; f, Drift rate maximum). Top row: Generating parameters vs. fitted parameters for each subject across ten simulations for haloperidol group (yellow) and placebo group (blue). Second row: True generating group level hyperparameter means (points) and Bottom row: standard deviations (points) and estimated 95% highest density intervals (lines) per fitted simulation. For correlations between generating and estimated single-subject parameters, see Extended Data Figure 9-1.

Figure 10.

Parameter recovery analysis for all shift parameters using the DDM_s (a, Log(k)_shift; b, Drift rate coefficient_shift; c, Nondecision time_shift; d, Boundary separation_shift; e, Starting point bias_shift; f, Drift rate maximum_shift). Top row: Generating parameters vs. fitted parameters for each subject across ten simulations for haloperidol group (yellow) and placebo group (blue). Second row: True generating group level hyperparameter means (points) and Bottom row: standard deviations (points) and estimated 95% highest density intervals (lines) per fitted simulation.