Dopamine Manipulation Affects Response Vigor Independently of Opportunity Cost

Abstract

Dopamine is known to be involved in regulating effort investment in relation to reward, and the disruption of this mechanism is thought to be central in some pathological situations such as Parkinson's disease, addiction, and depression. According to an influential model, dopamine plays this role by encoding the opportunity cost, i.e., the average value of forfeited actions, which is an important parameter to take into account when making decisions about which action to undertake and how fast to execute it. We tested this hypothesis by asking healthy human participants to perform two effort-based decision-making tasks, following either placebo or levodopa intake in a double blind within-subject protocol. In the effort-constrained task, there was a trade-off between the amount of force exerted and the time spent in executing the task, such that investing more effort decreased the opportunity cost. In the time-constrained task, the effort duration was constant, but exerting more force allowed the subject to earn more substantial reward instead of saving time. Contrary to the model predictions, we found that levodopa caused an increase in the force exerted only in the time-constrained task, in which there was no trade-off between effort and opportunity cost. In addition, a computational model showed that dopamine manipulation left the opportunity cost factor unaffected but altered the ratio between the effort cost and reinforcement value. These findings suggest that dopamine does not represent the opportunity cost but rather modulates how much effort a given reward is worth.

Significance statement: Dopamine has been proposed in a prevalent theory to signal the average reward rate, used to estimate the cost of investing time in an action, also referred to as opportunity cost. We contrasted the effect of dopamine manipulation in healthy participants in two tasks, in which increasing response vigor (i.e., the amount of effort invested in an action) allowed either to save time or to earn more reward. We found that levodopa-a synthetic precursor of dopamine-increases response vigor only in the latter situation, demonstrating that, rather than the opportunity cost, dopamine is involved in computing the expected value of effort.

Keywords: computational model; decision making; dopamine; effort-based decision making; psychopharmacology.

Figure 1.

Schematic depiction of the tasks. A, Effort-constrained task layout. The subject was presented with a coin of 50, 20, or 5 euro cents. The screen then showed the amount of effort required to reach the reward with a red line in a rectangular cylinder. If the subject accepted, he or she then had to squeeze the dynamometer to obtain the reward, with no time constraint. If he or she refused, the next offer was proposed. B, Time-constrained task layout. The subject was presented a proposed reward amount (ranging from 5 to 80 cents). If the subject accepted the offer, he or she had to squeeze a dynamometer during 5 s. The reward earned depended on the level reached on the gauge at the end of the 5 s time limit. If the subject refused, the next offer was proposed.

Figure 2.

Main predictions and behavioral results. A, B, Computational model predictions of force execution during the effort-constrained (left) and time-constrained tasks (right), for different values of the opportunity cost factor O_c (A) or utility ratio factor R_u (B). In the right panel of A and left panel of B, the force intensity does not vary with the changes in the model factors, and all curves are therefore superimposed. Changes in O_c predict variations in the force intensity in the effort-constrained task only, while changes in R_u predict force variations only in the time-constrained task. C, The relation between the force intensity and the effort required in the effort-constrained task (left) or reward magnitude on offer in the time-constrained task (right) is shown for both the placebo (dashed) and levodopa (solid) treatment conditions. In the left panel, the three different reward conditions are color coded, and the inset illustrates the effect of the reward condition on force intensity, z-scored within each effort level condition. Regarding the data illustrated in the right panel, the force intensity was computed from the accepted trials only, and only the data points including more that 10% average acceptance rate were included. The inset in the right panel shows the effect of the treatment condition on the force intensity, z-scored within each reward condition. Error bars illustrate the SEM.

Figure 3.

Acceptance rate and reaction time results. A, C, Effort-constrained task. The relations between the effort required (x-axis) and the acceptance rate (A) and the RT (C) are depicted. The conventions are the same as in Figure 2. B, D, Time-constrained task. The relations between reward magnitude on offer (cents) and the acceptance rate (B) and the RT (D) are illustrated for both the placebo (dashed) and levodopa (solid) treatment conditions.

Figure 4.

Average force execution time course. A, Averaged time course of force execution as a function of effort (color coded) and treatment condition (solid and dashed for the levodopa and placebo conditions, respectively) in the effort-constrained task. The rate of acceptance and duration of the effort varied between conditions and subjects. Therefore, we excluded from the figure the bins that included data for <30% of the participants. B, Effect of the treatment condition (solid and dashed for the levodopa and placebo conditions, respectively) on the time course of force execution in the time-constrained task. C, Time course of force execution as a function of reward (color coded) in the time-constrained task.

Figure 5.

Control analysis. A, Treatment effect on force intensity (z scores) as a function of the total effort executed [force in percentage times time (seconds)] in both types of tasks (effort and time constrained). B, Treatment effect on force intensity (z scores) as a function of the total amount of reward received (cents) in both types of tasks (effort and time constrained).

Figure 6.

Computational results. A–D, Data points correspond to the actual data averaged across participants, while solid and dashed lines correspond to the average model fitting in the levodopa and placebo conditions, respectively. A, Acceptance rate in the effort-constrained task. Reward conditions are color coded. B, Acceptance rate in the time-constrained task. C, Average force during the effort-constrained task. D, Average force during the time-constrained task. It is noteworthy that while the fit was quite accurate overall, the model failed to account for the force exertion in the low reward condition when levodopa was administered. E, Changes in model parameters between the placebo and levodopa sessions. Error bars represent 95% confidence intervals. Q_r corresponds to the decision threshold, R_u to the utility ratio, O_c to the opportunity cost factor, T_a to the acceptance temperature parameter, T_e to the effort temperature parameter, and E_r, E_f, and E_d to the reward, force, and duration exponent parameters, respectively. MVC, Maximal Voluntary Contraction. The asterisk indicates that only R_u changed significantly between treatment conditions (α = 0.05).