Reward Rate Research Articles

34 Neurocomputational Mechanisms of Social Reward Processing in Combat-Exposed Veterans

Objective:Combat exposure is associated with higher rates of depressive symptoms, including anhedonia (i.e., a reduced ability to seek and experience rewards) and feelings of social disconnectedness. While these symptoms are commonly documented in combat-exposed Veterans following deployment, the cognitive mechanisms underlying this pathology is less well understood. Computational modeling can provides detailed mechanistic insights into complex cognition, which may be particularly useful to understand how social reward processing is altered following combat exposure. Here, we use a Bayesian learning model framework to address this question.Participants and Methods:Thirty-three Operation Enduring Freedom (OEF)/ Operation Iraqi Freedom (OIF)/Operation New Dawn (OND) Veterans (25 Male, 8 Female) between the ages of 18-65 years old (M = 41.61, SD = 10.49) participated in this study. In both classic/monetary and social reward conditions, participants completed a 2-arm bandit task, in which they must choose on each trial between two options (i.e., slot machine vs social partner) with unknown reward rates. While they received monetary outcomes in the classic condition, participants received compliments from different fictitious partners in the social condition. We first compared a learning-independent Win-stay/Lose-shift (WSLS) heuristic and either a Rescorla-Wagner Q-learning or a Bayesian learning model (Dynamic Belief Model/DBM) paired with a Softmax reward maximization policy. DBM+Softmax provided the best fit of the data for most participants (31/33). Individual DBM parameters of prior reward expectation, reward learning (i.e., perceived stability of reward rates), and Softmax reward maximization were estimated and compared across conditions.Results:Participants did not differ in their reward learning parameters across monetary and social conditions (t(30)= -0.70, p = 0.490), suggesting similar perception of reward stability in both modalities. However, higher Bayesian prior mean (i.e., initial belief of reward rate; t(30)= -2.31, p = 0.028, d=0.42) and greater reward maximization (i.e., Softmax parameter; t(30)= -2.26, p = 0.031, d=0.41) were observed in response to social vs monetary rewards. In the social reward condition, higher self-reported social connectedness was associated with greater model fit of our DBM model (i.e., smaller Bayesian Information Criterion/BIC; r = -0.38, p = 0.041). In this condition, those expecting higher reward rates when initiating reward exploration (those with higher DBM prior mean) endorsed lower self-esteem (Spearman's ρ = -0.43, p = 0.078) and lower positive affect (ρ = -0.32, p = 0.078).Conclusions:A Bayesian learning modeling framework can characterize mechanistic differences in the processing of social vs non-social reward among combat-exposed Veterans. Individuals with higher social connectedness were more model-based in their performance, consistent with the notion that they are more likely to estimate and anticipate how much social peers have to offer. Combat-exposed individuals with lower self-esteem and positive affect appear to have higher initial expectations of reward from unknown partners, which could reflect greater need for mood and/or self-esteem repair in those individuals. Overall, Bayesian modeling of social reward behavior provides a useful quantitative framework to predict clinically relevant construct of functional outcomes in military populations.

Real, potentially real, and hypothetical monetary rewards in probability discounting.

Although hypothetical rewards have been used almost exclusively in human discounting studies, investigations of their validity are limited. The present experiment compared the discounting of monetary reward value by probability across conditions in which the rewards were real, potentially real, and hypothetical. Twenty-four undergraduates choose between an uncertain large reward and a certain small reward 60 times (trials). In the real and hypothetical reward conditions, the participants made choices with real and hypothetical money, respectively, in every trial. In the potentially real condition, they did so with real money in randomly selected three of the 60 trials and with hypothetical money in the remainder. The log10 -transformed h values of a hyperbolic probability-discount function and the values of the area under the curve with an ordinal transformation of odds against were higher and lower, respectively, in the potentially real and in the hypothetical reward conditions than in the real reward condition, demonstrating that the probability discounting of hypothetical monetary rewards was larger than that of real rewards. These results suggest that future studies are required to identify why the hypothetical reward procedure overestimates the discounting rates of real rewards.

Reduced Sensitivity to Background Reward Underlies Apathy After Traumatic Brain Injury: Insights From an Ecological Foraging Framework.

Altered reward processing is increasingly recognised as a crucial mechanism underpinning apathy in many brain disorders. However despite its clinical relevance, little is known about the mechanisms of apathy following moderate-to-severe traumatic brain injury (TBI). In real-life situations, reward representations encompass both foreground (gains from current activity) and background (potential gains from the broader environment) elements. This latter variable provides a crucial set-point for switching behaviour in many naturalistic settings. We hypothesised apathy post-TBI would be associated with disrupted background reward sensitivity. We administered a computer-based foraging task to 45 people with moderate-to-severe TBI (20 with apathy, 39 males) and 37 matched controls. Participants decided when to leave locations (patches) where foreground reward rates depleted at differing rates, to pursue greater rewards from other patches in the environment, which had either a high or low background reward rate. Primary analysis was performed using linear mixed effects models, with patch leaving time the dependent variable. Findings showed a significant interaction between apathy and background reward sensitivity, driven by apathetic TBI participants not altering patch-leaving decisions as environmental reward rate changed. In contrast, although TBI was associated with reduced sensitivity to changing foreground rewards, this did not vary as a function of apathy. These results provide the first evidence directly linking disrupted background reward processing to apathy in any brain disorder. They identify a novel mechanism for apathy following moderate-to-severe TBI, and point towards novel interventions to improve this debilitating complication of head injury.

Managing Customer Churn via Service Mode Control

We introduce a novel stochastic control model for the problem of a service firm interacting over time with one of its customers who probabilistically churns depending on the customer’s satisfaction. The firm has two service modes available, and they determine the drift and volatility of the Brownian reward process. The firm’s objective is to maximize the rewards generated over the customer’s lifetime. Meanwhile, the customer might churn probabilistically if the customer’s satisfaction, modeled as an Orstein–Uhlenbeck process controlled by the firm’s service mode, is below a certain threshold. We build upon Markov processes with spatial delay to solve this problem, and we explicitly characterize the firm’s optimal policy, which is either myopic or a sandwich policy. A sandwich policy is one in which the firm deploys the service mode with inferior reward rate when the customer satisfaction level is in a specific interval near the satisfaction threshold and uses the myopically optimal service mode for all other satisfaction levels. Specifically, we find that the firm should use the safe service mode when the customer is marginally satisfied and the risky service mode when the customer is marginally unsatisfied. We find numerically that the customer lifetime value under the optimal policy is large relative to that under the myopic policy. Our results are robust to a variety of alternative model specifications. Funding: J. Lu gratefully acknowledges financial support from Natural Science Foundation of China (NSFC) [Project 72192805]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/moor.2021.0179 .

Comparison of three participation modes on ride-hailing platforms

With the rapid development of ride-hailing services, various participation modes have emerged on ride-hailing platforms that are recruiting drivers to serve the growing number of passengers. This paper investigates three popular participation modes for a person joining the ride-hailing platform as a driver: car buying mode (BM), short-term leasing mode (SLM), and financial leasing mode (FLM). We compare these three modes and study which is the best choice from the perspective of the driver and platform, considering different types of drivers (myopic or strategic). We develop an analytical model to explore this problem. Interestingly, even when the driver bears a high cost of maintenance, BM always dominates the other two modes for the driver. We find that the platform’s optimal strategy depends on the driver type. FLM will never be an optimal choice for the platform if the driver is strategic. However, if the driver is myopic, FLM will be the best mode under certain conditions. We also find that when the maintenance cost is moderate, regardless of the driver type, the driver and platform can realise a win-win situation with SLM, but otherwise, they are better off adopting BM. Moreover, with moderate maintenance cost, FLM can be a win-win strategy if the driver is myopic. Lastly, our study discloses that increasing the new-driver rewards benefits both the driver and platform only if the driver is myopic and the reward rate exceeds a certain threshold.

When Smaller Sooner Depletes a Pool of Resources Faster.

Behavior has short-term (proximal) and long-term (distal) consequences, and these consequences often involve different commodities. In particular, a commonly encountered distal consequence involves running out of resources - energy to respond, available food, ammunition, or money in the bank - that must be replenished before continuing a rewarding task. The current project examines proximal behavioral consequences in a video game (the amount of damage done to a clicked-on target as a function of waiting) and distal behavioral consequences (running out of the resources that allow the player to click on a target). When depleted, the resource replenished after a fixed amount of time. Thus, participants sometimes faced a tradeoff between behaviors that maximized their short-term reward rate and those that maximized their long-term reward rate. When the proximal contingency did not affect the short-term reward rate, the mere presence of limitations resulted in the slower use of resources, but the slowdown did not evidence strong sensitivity to the size of the resource pool nor the delay to its replenishment (Experiment 1). However, when the proximal contingency rewarded faster use of resources, participants did show sensitivity to the duration of the replenishment delay and the size of the resource pool (Experiment 2).

Experience-dependent evolution of odor mixture representations in piriform cortex.

Rodents can learn from exposure to rewarding odors to make better and quicker decisions. The piriform cortex is thought to be important for learning complex odor associations; however, it is not understood exactly how it learns to remember discriminations between many, sometimes overlapping, odor mixtures. We investigated how odor mixtures are represented in the posterior piriform cortex (pPC) of mice while they learn to discriminate a unique target odor mixture against hundreds of nontarget mixtures. We find that a significant proportion of pPC neurons discriminate between the target and all other nontarget odor mixtures. Neurons that prefer the target odor mixture tend to respond with brief increases in firing rate at odor onset compared to other neurons, which exhibit sustained and/or decreased firing. We allowed mice to continue training after they had reached high levels of performance and find that pPC neurons become more selective for target odor mixtures as well as for randomly chosen repeated nontarget odor mixtures that mice did not have to discriminate from other nontargets. These single unit changes during overtraining are accompanied by better categorization decoding at the population level, even though behavioral metrics of mice such as reward rate and latency to respond do not change. However, when difficult ambiguous trial types are introduced, the robustness of the target selectivity is correlated with better performance on the difficult trials. Taken together, these data reveal pPC as a dynamic and robust system that can optimize for both current and possible future task demands at once.

Capacity investment of shore power berths for a container port: Environmental incentive and infrastructure subsidy policies

China has vowed to peak its carbon dioxide emissions by 2030 and achieve carbon neutrality by 2060. The emissions generated by the shipping industry cannot be ignored. Shore power or cold ironing is one of the commonly-used technologies to reduce emissions from berthing vessels for port cities. The port is the executor of power capacity investment. The over-investment of the power capacity causes lower utilization and capital waste, while the under-investment results in service congestion and reduces the enthusiasm of vessels to use shore power berths (SPBs). This paper investigates the optimal capacity of SPBs by considering the choice behaviors of vessels with power receiving facilities (VWs) on SPBs and traditional berths (TBs). Furthermore, we consider that the government adopts two mechanisms of the environmental incentive (EI) and infrastructure subsidy (IS) to develop a bi-level mathematical programming problem to realize that government subsidies promote the capacity investment of port authorities on SPBs. The government rewards the port authority according to the environmental improvement under the EI policy and subsidizes the port authority based on the infrastructure capital cost under IS policy. The subsidy and reward rates can affect the port authority to change the capacity decision of SPBs and then affect the total emission reduction. Both IS and EI mechanisms can promote the installation of shore power in ports, and IS can achieve the best emission reduction effect.

Impact of decision and action outcomes on subsequent decision and action behaviours in humans.

Speed-accuracy trade-off adjustments in decision-making have been mainly studied separately from those in motor control. In the wild, however, animals coordinate their decision and action, often deciding while acting. Recent behavioural studies support this view, indicating that animals, including humans, trade decision time for movement time to maximize their global rate of reward during experimental sessions. Besides, it is well established that choice outcomes impact subsequent decisions. Crucially though, whether and how a decision outcome also influences the subsequent motor performance, and whether and how the outcome of a movement influences the next decision, is unclear. Here, we address these questions by analysing trial-to-trial changes of choice and motor behaviours in healthy human participants instructed to perform successive perceptual decisions expressed with reaching movements whose duration was either weakly or strongly constrained in separate tasks. Results indicate that after a wrong decision, subjects who were weakly constrained in their action duration decided more slowly and more accurately. Interestingly, they also shortened their subsequent movement duration by moving faster. Conversely, we found that errors of constrained movements influenced not only the speed and the amplitude of the following movement but those of the decision too. If the movement had to be slowed down, the decision that precedes that movement was accelerated and vice versa. Together, these results indicate that from one trial to the next, humans seek to determine a behavioural duration as a whole instead of optimizing each of the decision and action speed-accuracy trade-offs independently of each other.

A stochastic time scale based framework for system reliability under a Markovian dynamic environment

AbstractMost engineering systems operate under stochastic dynamic environments. The variability and stochasticity of environmental conditions have a non‐negligible impact on the failure behavior of engineering systems. This article develops a reliability modeling and assessment framework for systems operating under a Markovian dynamic environment. The stochastic dynamic environment is characterized by a continuous‐time Markov chain. Using the cumulative exposure principle, the stochastic time scale, resulting from the cumulative effect of the Markovian dynamic environment, is computed via a Markov reward model. Based on the above settings, the system reliability model under the Markovian dynamic environment is developed. The maximum likelihood estimates and confidence intervals for the model parameters, including the transition rate matrix of the Markov chain, the reward rates of the Markov reward model, and the parameters of the baseline lifetime distribution, are obtained by utilizing the collected environment and lifetime data. The system reliability is then assessed with the estimated parameters. The effectiveness of the proposed framework are validated using simulation and through an application to a long‐term storage system. The results show that the unknown reliability model parameters can be accurately estimated, and the proposed model with the consideration of the cumulative effect of the Markovian dynamic environment can provide a more accurate reliability estimate than that without such a consideration.

Genetic variation in the dopamine system is associated with mixed-strategy decision-making in patients with Parkinson's disease.

Decision-making during mixed-strategy games requires flexibly adapting choice strategies in response to others' actions and dynamically tracking outcomes. Such decisions involve diverse cognitive processes, including reinforcement learning, which are affected by disruptions to the striatal dopamine system. We therefore investigated how genetic variation in dopamine function affected mixed-strategy decision-making in Parkinson's disease (PD), which involves striatal dopamine pathology. Sixty-six PD patients (ages 49-85, Hoehn and Yahr Stages 1-3) and 22 healthy controls (ages 54-75) competed in a mixed-strategy game where successful performance depended on minimizing choice biases (i.e., flexibly adapting choices trial by trial). Participants also completed a fixed-strategy task that was matched for sensory input, motor outputs and overall reward rate. Factor analyses were used to disentangle cognitive from motor aspects within both tasks. Using a within-subject, multi-centre design, patients were examined on and off dopaminergic therapy, and genetic variation was examined via a multilocus genetic profile score representing the additive effects of three single nucleotide polymorphisms (SNPs) that influence dopamine transmission: rs4680 (COMT Val158 Met), rs6277 (C957T) and rs907094 (encoding DARPP-32). PD and control participants displayed comparable mixed-strategy choice behaviour (overall); however, PD patients with genetic profile scores indicating higher dopamine transmission showed improved performance relative to those with low scores. Exploratory follow-up tests across individual SNPs revealed better performance in individuals with the C957T polymorphism, reflecting higher striatal D2/D3 receptor density. Importantly, genetic variation modulated cognitive aspects of performance, above and beyond motor function, suggesting that genetic variation in dopamine signalling may underlie individual differences in cognitive function in PD.

Overconsumption as a function of how individuals make choices: A paper in honor of Howard Rachlin's contributions to psychology.

Howard Rachlin's widely influential behavioral economic approach to self-control and related issues provides the model for this submission. The topic is overconsumption. Current human consumption levels are unsustainable. Explanations typically focus on societal factors, such as the seductive power of advertising and/or misguided tax policies. However, the effectiveness of these factors depends on the degree to which individuals are susceptible to the message: "consume more." Humans are not blank slates. This paper argues that how individuals frame their choices establishes the susceptibility to overconsume. According to economic theory, consumers frame their options as bundles, composed of different combinations of the available items and activities. This leads to maximizing. In experiments, participants tend to frame their options as "either-or" choices. This leads to the matching law. Mathematical models of concurrent schedule choice procedures show that (1) the matching law implies overconsumption of the most preferred option and (2) that individuals will persist in preferring their favorite option even when doing so reduces overall reward rates. Given that the matching law better describes how individuals choose than does maximizing, the mathematical models of widely used choice procedures help explain why efforts to increase consumption have been more influential than efforts to control consumption.

Average reward rates enable motivational transfer across independent reinforcement learning tasks

Outcomes and feedbacks on performance may influence behavior beyond the context in which it was received, yet it remains unclear what neurobehavioral mechanisms may account for such lingering influences on behavior. The average reward rate (ARR) has been suggested to regulate motivated behavior, and was found to interact with dopamine-sensitive cognitive processes, such as vigilance and associative memory encoding. The ARR could therefore provide a bridge between independent tasks when these are performed in temporal proximity, such that the reward rate obtained in one task could influence performance in a second subsequent task. Reinforcement learning depends on the coding of prediction error signals by dopamine neurons and their downstream targets, in particular the nucleus accumbens. Because these brain regions also respond to changes in ARR, reinforcement learning may be vulnerable to changes in ARR. To test this hypothesis, we designed a novel paradigm in which participants (n = 245) performed two probabilistic reinforcement learning tasks presented in interleaved trials. The ARR was controlled by an "induction" task which provided feedback with a low (p = 0.58), a medium (p = 0.75), or a high probability of reward (p = 0.92), while the impact of ARR on reinforcement learning was tested by a second "reference" task with a constant reward probability (p = 0.75). We find that performance was significantly lower in the reference task when the induction task provided low reward probabilities (i.e., during low levels of ARR), as compared to the medium and high ARR conditions. Behavioral modeling further revealed that the influence of ARR is best described by models which accumulates average rewards (rather than average prediction errors), and where the ARR directly modulates the prediction error signal (rather than affecting learning rates or exploration). Our results demonstrate how affective information in one domain may transfer and affect motivated behavior in other domains. These findings are particularly relevant for understanding mood disorders, but may also inform abnormal behaviors attributed to dopamine dysfunction.

Zero-sum semi-Markov games with state-action-dependent discount factors

Semi-Markov model is one of the most general models for stochastic dynamic systems. This paper deals with a two-person zero-sum game for semi-Markov processes. We focus on the expected discounted criterion with state-action-dependent discount factors. The state and action spaces are both Polish spaces, and the reward rate function is ω-bounded. We first construct a fairly general model of semi-Markov games under a given semi-Markov kernel and a pair of strategies. Next, based on the standard regularity condition and the continuity-compactness condition for semi-Markov games, we derive a “drift condition” on the semi-Markov kernel and suppose that the discount factors have a positive lower bound, under which the existence of the value function and an optimal pair of stationary strategies of our semi-Markov game are proved by using a general Shapley equation. Moreover, in the scenario of finite state and action spaces, a value iteration-type algorithm for approximating the value function and an optimal pair of stationary strategies is developed. The convergence and the error bound of the algorithm are also proved. Finally, we conduct numerical examples to demonstrate the main results.

Normative decision rules in changing environments.

Models based on normative principles have played a major role in our understanding of how the brain forms decisions. However, these models have typically been derived for simple, stable conditions, and their relevance to decisions formed under more naturalistic, dynamic conditions is unclear. We previously derived a normative decision model in which evidence accumulation is adapted to fluctuations in the evidence-generating process that occur during a single decision (Glaze et al., 2015), but the evolution of commitment rules (e.g. thresholds on the accumulated evidence) under dynamic conditions is not fully understood. Here, we derive a normative model for decisions based on changing contexts, which we define as changes in evidence quality or reward, over the course of a single decision. In these cases, performance (reward rate) is maximized using decision thresholds that respond to and even anticipate these changes, in contrast to the static thresholds used in many decision models. We show that these adaptive thresholds exhibit several distinct temporal motifs that depend on the specific predicted and experienced context changes and that adaptive models perform robustly even when implemented imperfectly (noisily). We further show that decision models with adaptive thresholds outperform those with constant or urgency-gated thresholds in accounting for human response times on a task with time-varying evidence quality and average reward. These results further link normative and neural decision-making while expanding our view of both as dynamic, adaptive processes that update and use expectations to govern both deliberation and commitment.

The Average Reward Rate Modulates Behavioral and Neural Indices of Effortful Control Allocation.

People tend to avoid exerting cognitive effort, and findings from recent behavioral studies suggest that effort allocation is in part determined by the opportunity cost of slothful responding-operationalized as the average reward rate per unit time. When the average rate of reward is high, individuals make more errors in cognitive control tasks, presumably owing to a withdrawal of costly cognitive processing. An open question remains whether the presumed modulations of cognitively effortful control processes are observable at the neural level. Here, we measured EEG while participants completed the Simon task, a well-known response conflict task, while the experienced average reward rate fluctuated across trials. We examined neural activity associated with the opportunity cost of time by applying generalized eigendecomposition, a hypothesis-driven source separation technique, to identify a midfrontal component associated with the average reward rate. Fluctuations in average reward rate modulated not only component amplitude but also, most importantly, component theta power (4-8 Hz). Higher average reward rate was associated with reduced theta power, suggesting that the opportunity of time modulates effort allocation. These neural results provide evidence for the idea that people strategically modulate the amount of cognitive effort they exert based on the opportunity cost of time.

Incentive Framework for Cross-Device Federated Learning and Analytics With Multiple Tasks Based on a Multi-Leader-Follower Game

Federated learning and analytics are proposed to collaboratively learn models or statistics from decentralized data. However, the accuracy of global models relies heavily on the participation of data owners. Furthermore, for a multi-task system, data owners need to allocate limited computation resources efficiently among different local tasks. In this paper, based on the federated optimization, we use a multi-leader-follower game to build an incentive framework for the federated learning and analytics system with multiple tasks. In the proposed framework, each task owner at the upper layer decides its reward rate to incentivize data owners to participate in the federated learning or analytics. Then, based on the reward rates of tasks, each data owner (e.g., mobile device or organization) at the lower layer determines its accuracy level of local tasks' solutions. We analyze the optimal computation strategies of data owners. Because the closed-form expression of the lower-layer solution cannot be obtained, we leverage the variational inequality (VI) theory to analyze the existence of the proposed game's solution. Moreover, we provide a block coordinate descent (BCD) algorithm to solve the proposed game. Simulation results show that our proposed framework can achieve the performance improvement of the whole system.

Mice are Near Optimal Timers

Abstract Many conventional interval timing tasks do not contain asymmetric cost (loss) functions and thereby favor high temporal accuracy. On the other hand, asymmetric cost functions that differentially penalize/reinforce the early or late responses result in adaptive biases (shift) in timed responses due to timing uncertainty. Consequently, optimal performance in these tasks entails the normative parametrization of adaptive timing biases by the level of timing uncertainty. Differential reinforcement of response duration (DRRD) is one of these tasks that require mice to actively respond (e.g., continuously depressing a lever) for a minimum amount of time to be reinforced. The active production of a time interval by mice in DRRD differentiates this task from the differential reinforcement of low rates of responding (DRL) task as a passive waiting task that was used in earlier studies to investigate the optimality of adaptive biases in timing behavior. We tested 21 Th-Cre male mice (9 weeks old) in a DRRD task with a minimum requirement of 2 s. Mean response durations were positively biased (longer than the minimum requirement), and the extent of bias was predicted by the level of endogenous timing uncertainty. Mice nearly maximized the reward rate in this task. These results contribute to the accumulating evidence supporting optimal temporal risk assessment in non-human animals.

Critical periods when dopamine controls behavioral responding during Pavlovian learning.

Learning the association between rewards and predictive cues is critical for appetitive behavioral responding. The mesolimbic dopamine system is thought to play an integral role in establishing these cue-reward associations. The dopamine response to cues can signal differences in reward value, though this emerges only after significant training. This suggests that the dopamine system may differentially regulate behavioral responding depending on the phase of training. The purpose of this study was to determine whether antagonizing dopamine receptors elicited different effects on behavior depending on the phase of training or the type of Pavlovian task. Separate groups of male rats were trained on Pavlovian tasks in which distinct audio cues signaled either differences in reward size or differences in reward rate. The dopamine receptor antagonist flupenthixol was systemically administered prior to either the first ten sessions of training (acquisition phase) or the second ten sessions of training (expression phase), and we monitored the effect of these manipulations for an additional ten training sessions. We identified acute effects of dopamine receptor antagonism on conditioned responding, the latency to respond, and post-reward head entries in both Pavlovian tasks. Interestingly, dopamine receptor antagonism during the expression phase produced persistent deficits in behavioral responding only in rats trained on the reward size Pavlovian task. Together, our results illustrate that dopamine's control over behavior in Pavlovian tasks depends upon one's prior training experience and the information signaled by the cues.

Dopamine and reward-related vigor in younger and older adults

Vigor reflects how motivated people are to respond to stimuli. We previously showed that, on average, humans are more vigorous when a higher rate of reward is available, and that this relationship is modulated by the dopamine precursor levodopa. Dopamine signaling and probabilistic reward learning deteriorate across the adult life span, and thus, the relationship between vigor and reward may also change in aging. We tested this assertion and assessed whether it correlates with D1 dopamine receptor availability, measured using Positron Emission Tomography. We registered response times of 30 older and 30 younger participants during an oddball discrimination task where rewards varied systematically between trials. The average reward rate had a similar impact on vigor in both age groups. There was a weak positive association between ventral striatal dopamine receptor availability and the effect of average reward rate on response time. Overall, the effect of reward on response vigor was similar in younger and older adults, and weakly correlated with dopamine D1 receptor availability.