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Dissociable neural representations of future reward magnitude and delay during temporal discounting.

ABSTRACT: In temporal discounting, individuals often prefer smaller immediate rewards to larger delayed rewards, implying a trade off between the magnitude and delay of future rewards. While recent functional magnetic resonance imaging (fMRI) investigations of temporal discounting have generated conflicting findings, no studies have focused on whether distinct neural substrates respond to the magnitude and delay of future rewards. Combining a novel, temporally distributed discounting task with event-related fMRI, we found that while nucleus accumbens (NAcc), mesial prefrontal cortical (MPFC), and posterior cingulate cortical (PCC) activation positively correlated with future reward magnitude, dorsolateral prefrontal cortical (DLPFC) and posterior parietal cortical (PPC) activation negatively correlated with future reward delay. Further, more impulsive individuals showed diminished NAcc activation to the magnitude of future rewards and greater deactivations to delays of future rewards in the MPFC, DLPFC, and PPC. These findings suggest that while mesolimbic dopamine projection regions show greater sensitivity to the magnitude of future rewards, lateral cortical regions show greater (negative) sensitivity to the delay of future rewards, potentially reconciling different neural accounts of temporal discounting.

SUBMITTER: Ballard K

PROVIDER: S-EPMC2685201 | biostudies-literature | 2009 Mar

REPOSITORIES: biostudies-literature

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Dissociable neural representations of future reward magnitude and delay during temporal discounting.

Ballard Kacey K Knutson Brian B

NeuroImage 20081124 1

In temporal discounting, individuals often prefer smaller immediate rewards to larger delayed rewards, implying a trade off between the magnitude and delay of future rewards. While recent functional magnetic resonance imaging (fMRI) investigations of temporal discounting have generated conflicting findings, no studies have focused on whether distinct neural substrates respond to the magnitude and delay of future rewards. Combining a novel, temporally distributed discounting task with event-relat ...[more]

PMID: 19071223

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Project description:There has been considerable interest from the fields of biology, economics, psychology, and ecology about how decision costs decrease the value of rewarding outcomes. For example, formal descriptions of how reward value changes with increasing temporal delays allow for quantifying individual decision preferences, as in animal species populating different habitats, or normal and clinical human populations. Strikingly, it remains largely unclear how humans evaluate rewards when these are tied to energetic costs, despite the surge of interest in the neural basis of effort-guided decision-making and the prevalence of disorders showing a diminished willingness to exert effort (e.g., depression). One common assumption is that effort discounts reward in a similar way to delay. Here we challenge this assumption by formally comparing competing hypotheses about effort and delay discounting. We used a design specifically optimized to compare discounting behavior for both effort and delay over a wide range of decision costs (Experiment 1). We then additionally characterized the profile of effort discounting free of model assumptions (Experiment 2). Contrary to previous reports, in both experiments effort costs devalued reward in a manner opposite to delay, with small devaluations for lower efforts, and progressively larger devaluations for higher effort-levels (concave shape). Bayesian model comparison confirmed that delay-choices were best predicted by a hyperbolic model, with the largest reward devaluations occurring at shorter delays. In contrast, an altogether different relationship was observed for effort-choices, which were best described by a model of inverse sigmoidal shape that is initially concave. Our results provide a novel characterization of human effort discounting behavior and its first dissociation from delay discounting. This enables accurate modelling of cost-benefit decisions, a prerequisite for the investigation of the neural underpinnings of effort-guided choice and for understanding the deficits in clinical disorders characterized by behavioral inactivity.

Project description:BackgroundThe tendency to devaluate future options as a function of time, known as delay discounting, is associated with various factors such as psychiatric illness and personality. Under identical experimental conditions, individuals may therefore strongly differ in the degree to which they discount future options. In delay discounting tasks, this inter-individual variability inevitably results in an unequal number of discounted trials per subject, generating difficulties in linking delay discounting to psychophysiological and neural correlates. Many studies have therefore focused on assessing delay discounting adaptively. Here, we extend these approaches by developing an adaptive paradigm which aims at inducing more comparable and homogeneous discounting frequencies across participants on a dimensional scale.MethodThe proposed approach probabilistically links a (common) discounting function to behavior to obtain a probabilistic model, and then exploits the model to obtain a formal condition which defines how to construe experimental trials so as to induce any desired discounting probability. We first infer subject-level models on behavior on a non-adaptive delay discounting task and then use these models to generate adaptive trials designed to evoke graded relative discounting frequencies of 0.3, 0.5, and 0.7 in each participant. We further compare and evaluate common models in the field through out-of-sample prediction error estimates, to iteratively improve the trial-generating model and paradigm.ResultsThe developed paradigm successfully increases discounting behavior during both reward and loss discounting. Moreover, it evokes graded relative choice frequencies in line with model-based expectations (i.e., 0.3, 0.5, and 0.7) suggesting that we can successfully homogenize behavior. Our model comparison analyses indicate that hyperboloid models are superior in predicting unseen discounting behavior to more conventional hyperbolic and exponential models. We report out-of-sample error estimates as well as commonalities and differences between reward and loss discounting, demonstrating for instance lower discounting rates, as well as differences in delay perception in loss discounting.ConclusionThe present work proposes a model-based framework to evoke graded responses linked to cognitive function at a single subject level. Such a framework may be used in the future to measure cognitive functions on a dimensional rather than dichotomous scale.

Dataset Information

Dissociable neural representations of future reward magnitude and delay during temporal discounting.

Publications

Dissociable neural representations of future reward magnitude and delay during temporal discounting.

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