Actual Reward Research Articles

Contribution of substantia nigra glutamate to prediction error signals in schizophrenia: a combined magnetic resonance spectroscopy/functional imaging study.

Background:Because dopamine neurons signal a mismatch between expected and actual reward called prediction error (PE), aberrant PE signals in schizophrenia have been attributed to known dopaminergic abnormalities. However, dysfunction of N-methyl-D-aspartate receptors on cortical γ-aminobutyric acid neurons, as hypothesized in schizophrenia, could lead to excess glutamate release in the substantia nigra (SN) and affect reward processing.Aims:The aim of this study was to investigate the contribution of SN glutamate to PE signals in healthy controls (HC) and patients with schizophrenia (SZ).Methods:We recruited 22 medicated SZ and 19 HC. We obtained (1) functional magnetic resonance imaging during a probabilistic monetary reward task to assess PE-related blood oxygen level-dependent (BOLD) signal and (2) magnetic resonance spectroscopy to measure Glx (glutamate+glutamine) in the SN. To identify group differences in regions where the BOLD signal varies as a function of PE, we analyzed PEs generated during the task as parametric modulators of reward delivery. Finally, we examined the correlation of PE-related BOLD signal and SN Glx in each group.Results:Relative to HC, PE-related BOLD signals in SZ were significantly different in the midbrain/SN and ventral striatum. In SZ, SN Glx was significantly elevated. In HC, but not in SZ, PE-related BOLD signal in SN was positively correlated with SN Glx.Conclusions:These results suggest a role of glutamate in the neural coding of PE in controls. They indicate that glutamatergic dysfunction might contribute to abnormal PE coding in schizophrenia, suggesting the use of glutamate-targeted approaches to improve these deficits.

Further evidence of close correspondence for alcohol demand decision making for hypothetical and incentivized rewards

Alcohol purchase tasks (APTs) are increasingly being used to assess behavioral economic demand for alcohol. Prior studies utilizing APTs have typically assessed demand for hypothetical outcomes, making the extent to which these hypothetical measures reflect preferences when actual rewards are at stake an important empirical question. This study examined alcohol demand across hypothetical and incentivized APTs. Nineteen male heavy drinkers completed two APTs – one for hypothetical alcohol and another in which one randomly-selected outcome was provided. Participants were given an opportunity to consume the alcohol associated with their choice on the incentivized APT during a self-administration period in a simulated bar environment. Results indicated generally close correspondence between APT versions, though participants were more sensitive to increases in price and tended to consume more at low prices on the incentivized version. Estimated consumption on the incentivized APT was highly correlated with the amount of alcohol consumed in the laboratory (r=.87, p<.001), suggesting that APT responses are valid indicators of actual drinking behavior. These results provide further evidence of congruence of demand-based decision-making when rewards are hypothetical vs. actually available. Implications for behavioral economic approaches to addictive behavior and directions for future research are discussed.

Effects of fictive reward on rat's choice behavior

Choices of humans and non-human primates are influenced by both actually experienced and fictive outcomes. To test whether this is also the case in rodents, we examined rat's choice behavior in a binary choice task in which variable magnitudes of actual and fictive rewards were delivered. We found that the animal's choice was significantly influenced by the magnitudes of both actual and fictive rewards in the previous trial. A model-based analysis revealed, however, that the effect of fictive reward was more transient and influenced mostly the choice in the next trial, whereas the effect of actual reward was more sustained, consistent with incremental learning of action values. Our results suggest that the capacity to modify future choices based on fictive outcomes might be shared by many different animal species, but fictive outcomes are less effective than actual outcomes in the incremental value learning system.

Learning to minimize efforts versus maximizing rewards: computational principles and neural correlates.

The mechanisms of reward maximization have been extensively studied at both the computational and neural levels. By contrast, little is known about how the brain learns to choose the options that minimize action cost. In principle, the brain could have evolved a general mechanism that applies the same learning rule to the different dimensions of choice options. To test this hypothesis, we scanned healthy human volunteers while they performed a probabilistic instrumental learning task that varied in both the physical effort and the monetary outcome associated with choice options. Behavioral data showed that the same computational rule, using prediction errors to update expectations, could account for both reward maximization and effort minimization. However, these learning-related variables were encoded in partially dissociable brain areas. In line with previous findings, the ventromedial prefrontal cortex was found to positively represent expected and actual rewards, regardless of effort. A separate network, encompassing the anterior insula, the dorsal anterior cingulate, and the posterior parietal cortex, correlated positively with expected and actual efforts. These findings suggest that the same computational rule is applied by distinct brain systems, depending on the choice dimension-cost or benefit-that has to be learned.

Inferring reward prediction errors in patients with schizophrenia: a dynamic reward task for reinforcement learning.

Abnormalities in the dopamine system have long been implicated in explanations of reinforcement learning and psychosis. The updated reward prediction error (RPE)—a discrepancy between the predicted and actual rewards—is thought to be encoded by dopaminergic neurons. Dysregulation of dopamine systems could alter the appraisal of stimuli and eventually lead to schizophrenia. Accordingly, the measurement of RPE provides a potential behavioral index for the evaluation of brain dopamine activity and psychotic symptoms. Here, we assess two features potentially crucial to the RPE process, namely belief formation and belief perseveration, via a probability learning task and reinforcement-learning modeling. Forty-five patients with schizophrenia [26 high-psychosis and 19 low-psychosis, based on their p1 and p3 scores in the positive-symptom subscales of the Positive and Negative Syndrome Scale (PANSS)] and 24 controls were tested in a feedback-based dynamic reward task for their RPE-related decision making. While task scores across the three groups were similar, matching law analysis revealed that the reward sensitivities of both psychosis groups were lower than that of controls. Trial-by-trial data were further fit with a reinforcement learning model using the Bayesian estimation approach. Model fitting results indicated that both psychosis groups tend to update their reward values more rapidly than controls. Moreover, among the three groups, high-psychosis patients had the lowest degree of choice perseveration. Lumping patients' data together, we also found that patients' perseveration appears to be negatively correlated (p = 0.09, trending toward significance) with their PANSS p1 + p3 scores. Our method provides an alternative for investigating reward-related learning and decision making in basic and clinical settings.

Reward contingencies and the recalibration of task monitoring and reward systems: A high-density electrical mapping study

Task execution almost always occurs in the context of reward-seeking or punishment-avoiding behavior. As such, ongoing task-monitoring systems are influenced by reward anticipation systems. In turn, when a task has been executed either successfully or unsuccessfully, future iterations of that task will be re-titrated on the basis of the task outcome. Here, we examined the neural underpinnings of the task-monitoring and reward-evaluation systems to better understand how they govern reward-seeking behavior. Twenty-three healthy adult participants performed a task where they accrued points that equated to real world value (gift cards) by responding as rapidly as possible within an allotted timeframe, while success rate was titrated online by changing the duration of the timeframe dependent on participant performance. Informative cues initiated each trial, indicating the probability of potential reward or loss (four levels from very low to very high). We manipulated feedback by first informing participants of task success/failure, after which a second feedback signal indicated actual magnitude of reward/loss. High-density electroencephalography (EEG) recordings allowed for examination of event-related potentials (ERPs) to the informative cues and in turn, to both feedback signals. Distinct ERP components associated with reward cues, task-preparatory and task-monitoring processes, and reward feedback processes were identified. Unsurprisingly, participants displayed increased ERP amplitudes associated with task-preparatory processes following cues that predicted higher chances of reward. They also rapidly updated reward and loss prediction information dependent on task performance after the first feedback signal. Finally, upon reward receipt, initial reward probability was no longer taken into account. Rather, ERP measures suggested that only the magnitude of actual reward or loss was now processed. Reward and task-monitoring processes are clearly dissociable, but interact across very fast timescales to update reward predictions as information about task success or failure is accrued. Careful delineation of these processes will be useful in future investigations in clinical groups where such processes are suspected of having gone awry.

How We Learn to Make Decisions: Rapid Propagation of Reinforcement Learning Prediction Errors in Humans

Our ability to make decisions is predicated upon our knowledge of the outcomes of the actions available to us. Reinforcement learning theory posits that actions followed by a reward or punishment acquire value through the computation of prediction errors-discrepancies between the predicted and the actual reward. A multitude of neuroimaging studies have demonstrated that rewards and punishments evoke neural responses that appear to reflect reinforcement learning prediction errors [e.g., Krigolson, O. E., Pierce, L. J., Holroyd, C. B., & Tanaka, J. W. Learning to become an expert: Reinforcement learning and the acquisition of perceptual expertise. Journal of Cognitive Neuroscience, 21, 1833-1840, 2009; Bayer, H. M., & Glimcher, P. W. Midbrain dopamine neurons encode a quantitative reward prediction error signal. Neuron, 47, 129-141, 2005; O'Doherty, J. P. Reward representations and reward-related learning in the human brain: Insights from neuroimaging. Current Opinion in Neurobiology, 14, 769-776, 2004; Holroyd, C. B., & Coles, M. G. H. The neural basis of human error processing: Reinforcement learning, dopamine, and the error-related negativity. Psychological Review, 109, 679-709, 2002]. Here, we used the brain ERP technique to demonstrate that not only do rewards elicit a neural response akin to a prediction error but also that this signal rapidly diminished and propagated to the time of choice presentation with learning. Specifically, in a simple, learnable gambling task, we show that novel rewards elicited a feedback error-related negativity that rapidly decreased in amplitude with learning. Furthermore, we demonstrate the existence of a reward positivity at choice presentation, a previously unreported ERP component that has a similar timing and topography as the feedback error-related negativity that increased in amplitude with learning. The pattern of results we observed mirrored the output of a computational model that we implemented to compute reward prediction errors and the changes in amplitude of these prediction errors at the time of choice presentation and reward delivery. Our results provide further support that the computations that underlie human learning and decision-making follow reinforcement learning principles.

Effects of Prefrontal Cortical Inactivation on Neural Activity in the Ventral Tegmental Area

Dopamine (DA) cells have been suggested to signal discrepancies between expected and actual rewards in reinforcement learning. DA cells in the ventral tegmental area (VTA) receive direct projections from the medial prefrontal cortex (mPFC), a structure known to be one of the brain areas that represents expected future rewards. To investigate whether the mPFC contributes to generating reward prediction error signals of DA cells, we recorded VTA cells from rats foraging for different amounts of reward in a spatial working memory task. Our results showed that DA cells initially responded after the acquisition of rewards, but over training, they exhibited phasic responses when rats detected sensory cues originating from the rewards before obtaining them. We also observed two separate groups of non-DA cells activated in expectation of upcoming rewards or during reward consumption. Bilateral injections of muscimol, a GABAA agonist, into the mPFC significantly decreased the non-DA activity that encoded reward expectation. By contrast, the same manipulation of the mPFC elevated DA responses to reward-predicting cues. However, neither DA nor non-DA responses elicited after reward acquisition were affected by mPFC inactivation. These results suggest that the mPFC provides information about expected rewards to the VTA, and its functional loss elevates DA responses to reward-predicting cues by altering expectations about forthcoming rewards.

Reinforcement Learning Using a Continuous Time Actor-Critic Framework with Spiking Neurons

Animals repeat rewarded behaviors, but the physiological basis of reward-based learning has only been partially elucidated. On one hand, experimental evidence shows that the neuromodulator dopamine carries information about rewards and affects synaptic plasticity. On the other hand, the theory of reinforcement learning provides a framework for reward-based learning. Recent models of reward-modulated spike-timing-dependent plasticity have made first steps towards bridging the gap between the two approaches, but faced two problems. First, reinforcement learning is typically formulated in a discrete framework, ill-adapted to the description of natural situations. Second, biologically plausible models of reward-modulated spike-timing-dependent plasticity require precise calculation of the reward prediction error, yet it remains to be shown how this can be computed by neurons. Here we propose a solution to these problems by extending the continuous temporal difference (TD) learning of Doya (2000) to the case of spiking neurons in an actor-critic network operating in continuous time, and with continuous state and action representations. In our model, the critic learns to predict expected future rewards in real time. Its activity, together with actual rewards, conditions the delivery of a neuromodulatory TD signal to itself and to the actor, which is responsible for action choice. In simulations, we show that such an architecture can solve a Morris water-maze-like navigation task, in a number of trials consistent with reported animal performance. We also use our model to solve the acrobot and the cartpole problems, two complex motor control tasks. Our model provides a plausible way of computing reward prediction error in the brain. Moreover, the analytically derived learning rule is consistent with experimental evidence for dopamine-modulated spike-timing-dependent plasticity.

How bad do you want it? Reward modulation in the avian nidopallium caudolaterale.

The prefrontal cortex plays an important role in reward processing in humans and nonhuman primates. In the current study we examined reward modulation in the single cell activity of the avian analog of the prefrontal cortex, the nidopallium caudolaterale (NCL). Pigeons had to peck at stimuli that represented either no reward, a small reward, or a large reward in a no-choice condition (only one stimulus presented) or a choice condition (two stimuli presented simultaneously). Of the 92 recorded cells, 34 cells showed some form of reward modulation, either during the stimulus presentation or the delay activity preceding the delivery of reward in the no-choice or choice condition. Latencies to peck at the stimulus revealed that the differences in reward amount were behaviorally significant to the pigeons. Moreover, a majority of cells (approximately 70%) showed activity during the actual reward phase. Our results show that cells in the NCL modulate their response as a function of the amount of reward.

Auction-based crowdsourcing supporting skill management

Crowdsourcing is a promising approach for enterprises to maintain a flexible workforce that is able to solve parts of business processes formerly processed in-house. Companies perceive crowdsourcing as a concept that allows receiving solutions quickly and at little cost. Similar to cloud computing where computing power is provided on demand, the crowd promises a flexible on-demand workforce. However, businesses realize that these benefits entail a lack of quality control. The main difference compared to traditional approaches in business process execution is that tasks or activities cannot be directly assigned to employees but are posted to the crowdsourcing platform. Its members can choose deliberately which tasks to book and work on. In fact, crowdsourcing is heavily affected by the loose-coupling of workers to crowdsourcers and the dynamics of the environment. Hence, it remains a major challenge to guarantee high-quality processing of tasks within the prescribed time limit. A further obstacle for adoption of crowdsourcing in enterprises is the fact that it is hard to specify a fair monetary reward in advance. The concepts introduced in this work allow to smoothly integrate new workers, to keep them motivated, and to help them develop and improve skills needed in the system. We present a crowdsourcing marketplace that matches complex tasks, requiring multiple skills, to suitable workers. The key to ensuring high quality lies in skilled members whose capabilities can be estimated correctly. To that end, we present auction mechanisms that help to correctly estimate workers and to evolve skills that are needed in the system. Crowdsourcers do not need to predefine exact prices but only maximum prices they are willing to pay since the actual rewards for tasks are formed by supply and demand. Extensive experiments show that our approach leads to improved crowdsourcing, in most cases.

Computer-based measures of expected satiety predict portion-size selection and intake of real food

Previously, expected satiety (ES) has been measured using software and two-dimensional pictures presented on a computer screen. In this context, ES is an excellent predictor of self-selected portions, when quantified using images and similar software. In the present study we sought to establish the veracity of ES as a predictor of behaviours associated with real foods. Participants (N = 30) used computer software to assess their ES and ideal portion of three familiar foods. A real (non virtual) bowl of one food (pasta) was then presented and participants self-selected an ideal portion size. They then consumed the portion ad libitum. Additional measures of appetite, expected and actual liking, novelty, and reward, were also taken. Consistent with previous studies, we observed a strong correlation between screen-based measures of expected satiety and ideal portion size (r (28) = 0.68, p = 0.001). Importantly, expected satiety was also a significant predictor of physical self-selected ideal portions (r(28) = 0.527, p = 0.004) and actual intake (r(28) = 0.46, p = 0.017). These findings further validate the use of screen-based measures to explore determinants of portion-size selection and energy intake in humans. This research was supported by a BBSRC-DRINC grant (ref: BB/G005443/1).

Beyond Performance Evaluations...Organizational Citizenship Behavior and Outcomes

Although there is a great deal of work examining outcomes of organizational citizenship behavior (OCB), the vast majority of work at the individual level has focused on the outcome of performance evaluations. Although an important outcome, it is fairly clear that OCB contributes positively to performance evaluations. In this symposium, we hope to move beyond performance evaluations and investigate new outcomes of OCB. In this proposed symposium, the set of four papers examines a diverse set of outcomes, including hiring decisions and entry salary recommendations, psychological well-being and positive affect, supervisory reward recommendations, and specific career outcomes (e.g., promotion decisions, actual reward decisions). The set of papers includes both conceptual and empirical work, as well as both field and experimental studies. In limiting the number of papers to four, we hope to stimulate new thinking on outcomes of OCB and generate discussion about additional research directions. Our discussant, Elizabeth Morrison, will lend her expertise in commenting on the set of papers.Selecting Good Soldiers Versus Avoiding Bad ApplesPresenter: Timothy D. Maynes; Indiana U.Presenter: Steven Whiting; Indiana U.Presenter: Nathan Philip Podsakoff; U. of ArizonaFrom Helping to Happy: Why Being Neurotic Isn’t So BadPresenter: Matthias Spitzmueller; National U. of SingaporePresenter: David T. Wagner; Singapore Management U.Presenter: You Jin Kim; Michigan State U.Presenter: Linn Van Dyne; Michigan State U.Presenter: Remus Ilies; National U. of SingaporeA Social Information Processing Model of Task and Contextual PerformancePresenter: Riki Takeuchi; Hong Kong U. of Science and TechnologyPresenter: Cass Shum; Hong Kong U. of Science and TechnologyPresenter: Dan S. Chiaburu; Texas A&M U.The Moderating Effect of Reward System on the Relationship Between Citizenship Behavior and OutcomesPresenter: Diane Bergeron; Case Western Reserve U.Presenter: Jaron Harvey; U. of AlabamaPresenter: Mark C. Bolino; U. of OklahomaPresenter: Anthony Klotz; U. of Oklahoma - Norman

Supplementary Eye Field Encodes Reward Prediction Error

The outcomes of many decisions are uncertain and therefore need to be evaluated. We studied this evaluation process by recording neuronal activity in the supplementary eye field (SEF) during an oculomotor gambling task. While the monkeys awaited the outcome, SEF neurons represented attributes of the chosen option, namely, its expected value and the uncertainty of this value signal. After the gamble result was revealed, a number of neurons reflected the actual reward outcome. Other neurons evaluated the outcome by encoding the difference between the reward expectation represented during the delay period and the actual reward amount (i.e., the reward prediction error). Thus, SEF encodes not only reward prediction error but also all the components necessary for its computation: the expected and the actual outcome. This suggests that SEF might actively evaluate value-based decisions in the oculomotor domain, independent of other brain regions.

Safeguarding Justice Research

What do people think is just? A basic element in justice research is the just reward–the observer’s idea about what is just for a particular rewardee. But discerning the just reward is not easy; much intervenes between the idea and its expression. Justice theory suggests four classes of measures, one direct and three indirect, each class encompassing several designs for data collection and data analysis. This paper examines direct designs and one of the three classes of indirect designs–that based on the justice evaluation–and revisits the longstanding challenge of justice-evaluation-based indirect designs, namely, to reduce or eliminate the correlation between the actual reward and the unobserved just reward. The paper presents statistical, algebraic, and empirical evidence to show how the factorial survey, with its layers of randomization, protects against bias, mitigating the correlation in the one-reward design and destroying it in the multiple-reward design. Of course, the search for better measures continues. As with length and weight, new theory and new technology will bring new and better measures of the just reward.

Akt1 deficiency modulates reward learning and reward prediction error in mice

In contemporary reinforcement learning models, reward prediction error (RPE), the difference between the expected and actual reward, is thought to guide action value learning through the firing activity of dopaminergic neurons. Given the importance of dopamine in reward learning and the involvement of Akt1 in dopamine-dependent behaviors, the aim of this study was to investigate whether Akt1 deficiency modulates reward learning and the magnitude of RPE using Akt1 mutant mice as a model. In comparison to wild-type littermate controls, the expression of Akt1 proteins in mouse brains occurred in a gene-dosage-dependent manner and Akt1 heterozygous (HET) mice exhibited impaired striatal Akt1 activity under methamphetamine challenge. No genotypic difference was found in the basal levels of dopamine and its metabolites. In a series of reward-related learning tasks, HET mice displayed a relatively efficient method of updating reward information from the environment during the acquisition phase of the two natural reward tasks and in the reverse section of the dynamic foraging T-maze but not in methamphetamine-induced or aversive-related reward learning. The implementation of a standard reinforcement learning model and the Bayesian hierarchical parameter estimation show that HET mice have higher RPE magnitudes and that their action values are updated more rapidly among all three test sections in T-maze. These results indicate that Akt1 deficiency modulates natural reward learning and RPE. This study showed a promising avenue for investigating RPE in mutant mice and provided evidence for the potential link from genetic deficiency, to neurobiological abnormalities, to impairment in higher-order cognitive functioning.

Neuron-type-specific signals for reward and punishment in the ventral tegmental area

Dopamine plays a key role in motivation and reward. Dopaminergic neurons in the ventral tegmental area (VTA) signal the discrepancy between expected and actual rewards (i.e., reward prediction error, RPE)1-3, but how they compute such signals is unknown. We recorded the activity of VTA neurons while mice associated different odour cues with appetitive and aversive outcomes. We found three types of neurons based on responses to odours and outcomes: approximately half of the neurons (Type I, 52%) showed phasic excitation after reward-predicting odours and rewards in a manner consistent with RPE coding. The other half of neurons showed persistent activity during the delay between odour and outcome, that was modulated positively (Type II, 31%) or negatively (Type III, 17%) by the value of outcomes. While the activity of Type I neurons was sensitive to actual outcomes (i.e., when the reward was delivered as expected vs. unexpectedly omitted), the activity of Types II and III neurons was determined predominantly by reward-predicting odours. We “tagged” dopaminergic and GABAergic neurons with the light-sensitive protein channelrhodopsin-2 (ChR2) and identified them based on their responses to optical stimulation while recording. All identified dopaminergic neurons were of Type I and all GABAergic neurons were of Type II. These results show that VTA GABAergic neurons signal expected reward, a key variable for dopaminergic neurons to calculate RPE.

Action selection performance of a reconfigurable basal ganglia inspired model with Hebbian–Bayesian Go-NoGo connectivity

Several studies have shown a strong involvement of the basal ganglia (BG) in action selection and dopamine dependent learning. The dopaminergic signal to striatum, the input stage of the BG, has been commonly described as coding a reward prediction error (RPE), i.e., the difference between the predicted and actual reward. The RPE has been hypothesized to be critical in the modulation of the synaptic plasticity in cortico-striatal synapses in the direct and indirect pathway. We developed an abstract computational model of the BG, with a dual pathway structure functionally corresponding to the direct and indirect pathways, and compared its behavior to biological data as well as other reinforcement learning models. The computations in our model are inspired by Bayesian inference, and the synaptic plasticity changes depend on a three factor Hebbian–Bayesian learning rule based on co-activation of pre- and post-synaptic units and on the value of the RPE. The model builds on a modified Actor-Critic architecture and implements the direct (Go) and the indirect (NoGo) pathway, as well as the reward prediction (RP) system, acting in a complementary fashion. We investigated the performance of the model system when different configurations of the Go, NoGo, and RP system were utilized, e.g., using only the Go, NoGo, or RP system, or combinations of those. Learning performance was investigated in several types of learning paradigms, such as learning-relearning, successive learning, stochastic learning, reversal learning and a two-choice task. The RPE and the activity of the model during learning were similar to monkey electrophysiological and behavioral data. Our results, however, show that there is not a unique best way to configure this BG model to handle well all the learning paradigms tested. We thus suggest that an agent might dynamically configure its action selection mode, possibly depending on task characteristics and also on how much time is available.

Does the orbitofrontal cortex signal value?

The orbitofrontal cortex (OFC) has long been implicated in associative learning. Early work by Mishkin and Rolls showed that the OFC was critical for rapid changes in learned behavior, a role that was reflected in the encoding of associative information by orbitofrontal neurons. Over the years, new data-particularly neurophysiological data-have increasingly emphasized the OFC in signaling actual value. These signals have been reported to vary according to internal preferences and judgments and to even be completely independent of the sensory qualities of predictive cues, the actual rewards, and the responses required to obtain them. At the same time, increasingly sophisticated behavioral studies have shown that the OFC is often unnecessary for simple value-based behavior and instead seems critical when information about specific outcomes must be used to guide behavior and learning. Here, we review these data and suggest a theory that potentially reconciles these two ideas, value versus specific outcomes, and bodies of work on the OFC.

Is Talk “Cheap”? An Initial Investigation of the Equivalence of Alcohol Purchase Task Performance for Hypothetical and Actual Rewards

Behavioral economic alcohol purchase tasks (APTs) are self-report measures of alcohol demand that assess estimated consumption at escalating levels of price. However, the relationship between estimated performance for hypothetical outcomes and choices for actual outcomes has not been determined. The present study examined both the correspondence between choices for hypothetical and actual outcomes, and the correspondence between estimated alcohol consumption and actual drinking behavior. A collateral goal of the study was to examine the effects of alcohol cues on APT performance. Forty-one heavy-drinking adults (56% men) participated in a human laboratory protocol comprising APTs for hypothetical and actual alcohol and money, an alcohol cue reactivity paradigm, an alcohol self-administration period, and a recovery period. Pearson correlations revealed very high correspondence between APT performance for hypothetical and actual alcohol (ps < 0.001). Estimated consumption on the APT was similarly strongly associated with actual consumption during the self-administration period (r = 0.87, p < 0.001). Exposure to alcohol cues significantly increased subjective craving and arousal and had a trend-level effect on intensity of demand, in spite of notable ceiling effects. Associations among motivational indices were highly variable, suggesting multidimensionality. These results suggest there may be close correspondence both between value preferences for hypothetical alcohol and actual alcohol, and between estimated consumption and actual consumption. Methodological considerations and priorities for future studies are discussed.