Parabolic discounting of monetary rewards by physical effort

Hartmann, Matthias; Hager, Oliver M.; Tobler, Philippe N.; Kaiser, Stefan

doi:10.1016/j.beproc.2013.09.014

Cited by 134 publications

(208 citation statements)

References 19 publications

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“…Previous studies indicate that this rewardeffort arbitration follows a quadratic or sigmoidal, rather than a hyperbolic, discount function (39)(40)(41). As in these prior studies, we also found that a sigmoidal discounting function best described this arbitration (SI Appendix, Fig.…”

Section: Significancesupporting

confidence: 85%

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Separate mesocortical and mesolimbic pathways encode effort and reward learning signals

Hauser

Eldar

Dolan

2017

Proc. Natl. Acad. Sci. U.S.A.

122

127

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Optimal decision making mandates organisms learn the relevant features of choice options. Likewise, knowing how much effort we should expend can assume paramount importance. A mesolimbic network supports reward learning, but it is unclear whether other choice features, such as effort learning, rely on this same network. Using computational fMRI, we show parallel encoding of effort and reward prediction errors (PEs) within distinct brain regions, with effort PEs expressed in dorsomedial prefrontal cortex and reward PEs in ventral striatum. We show a common mesencephalic origin for these signals evident in overlapping, but spatially dissociable, dopaminergic midbrain regions expressing both types of PE. During action anticipation, reward and effort expectations were integrated in ventral striatum, consistent with a computation of an overall net benefit of a stimulus. Thus, we show that motivationally relevant stimulus features are learned in parallel dopaminergic pathways, with formation of an integrated utility signal at choice. effort prediction errors | reward prediction errors | apathy | substantia nigra/ventral tegmental area | dorsomedial prefrontal cortex O rganisms need to make energy-efficient decisions to maximize benefits and minimize costs, a tradeoff exemplified in effort expenditure (1-3). A key example occurs during foraging, where an overestimation of effort can lead to inaction and starvation (4), whereas underestimation of effort can result in persistent failure, as exemplified in the myth of Sisyphus (5).In a naturalistic environment, we often simultaneously learn about success in expending sufficient effort into an action as well as the reward we obtain from this same action. The reward outcomes that signal success and failure of an action are usually clear, although the effort necessary to attain success is often less transparent. Only by repeatedly experiencing success and failure is it possible to acquire an estimate of an optimal level of effort needed to succeed, without unnecessary waste of energy. This type of learning is important in contexts as diverse as foraging, hunting, and harvesting (6-8). Hull in his "law of less work" proposed that organisms "gradually learn" how to minimize effort expenditure (9). Surprisingly, we know little regarding the neurocognitive mechanisms that guide this form of simultaneous learning about reward and effort.A mesolimbic dopamine system encodes a teaching signal tethered to prediction of reward outcomes (10, 11). These reward prediction errors (PEs) arise from dopaminergic neurons in substantia nigra and ventral tegmental area (SN/VTA) and are broadcast to ventral striatum (VS) to mediate reward-related adaptation and learning (12, 13). Dopamine is also thought to provide a motivational signal (14-18), while dopaminergic deficits in rodents impair how effort and reward are arbitrated (1, 4, 19). The dorsomedial prefrontal cortex (dmPFC; spanning presupplementary motor area [pre-SMA] and dorsal anterior cingulate cortex [dACC]) is a candidate substrate...

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Section: Significancesupporting

confidence: 85%

“…24,70) with effort discounting (39)(40)(41). The effort PE encoded in dmPFC can be seen as representing an adjustment in belief about the height of a required effort threshold.…”

Section: Discussionmentioning

confidence: 99%

Separate mesocortical and mesolimbic pathways encode effort and reward learning signals

Hauser

Eldar

Dolan

2017

Proc. Natl. Acad. Sci. U.S.A.

122

127

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“…Most studies on physical effort in human decision making operationalize effort by asking subjects to squeeze a handle that measures hand grip force [7,8,20,21], a device that is easy to use and fMRI-friendly. The effort is then an isometric contraction of varying magnitude, expressed as a percentage of the maximum voluntary contraction (%MVC) that each subject can produce.…”

Section: Discussionmentioning

confidence: 99%

“…Subjects are typically required to choose between 2 squeezes with different grip forces, each associated with different rewards or additional decision factors (delay or risk). Hartmann and colleagues associated monetary rewards with grip forces and reported that among linear, hyperbolic, and quadratic effort cost functions, the quadratic cost function explained the subjects’ behavior best [8]. Klein-Flügge and colleagues used a similar task to compare effort discounting and delay discounting and reported that effort cost seemed best described as a sigmoidal function, i.e., showed a convex dependency for lower forces, as Hartmann and colleagues did, but Klein-Flügge and colleagues found a concave relationship for forces closer to MVC because of saturation of effort cost [20].…”

Section: Discussionmentioning

confidence: 99%

“…Using a similar task, Hartmann and colleagues [8] showed a quadratic discounting of monetary rewards by squeezing force, suggesting that effort for isometric force production grows proportionally to the square of the force amount. In contrast, by pitting isometric force production with different parameters directly against each other, Körding and colleagues defined effort as a function of both the duration and magnitude of force production, without the need to use an external monetary scale [9].…”

Section: Introductionmentioning

confidence: 99%

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What makes a reach movement effortful? Physical effort discounting supports common minimization principles in decision making and motor control

2017

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When deciding between alternative options, a rational agent chooses on the basis of the desirability of each outcome, including associated costs. As different options typically result in different actions, the effort associated with each action is an essential cost parameter. How do humans discount physical effort when deciding between movements? We used an action-selection task to characterize how subjective effort depends on the parameters of arm transport movements and controlled for potential confounding factors such as delay discounting and performance. First, by repeatedly asking subjects to choose between 2 arm movements of different amplitudes or durations, performed against different levels of force, we identified parameter combinations that subjects experienced as identical in effort (isoeffort curves). Movements with a long duration were judged more effortful than short-duration movements against the same force, while movement amplitudes did not influence effort. Biomechanics of the movements also affected effort, as movements towards the body midline were preferred to movements away from it. Second, by introducing movement repetitions, we further determined that the cost function for choosing between effortful movements had a quadratic relationship with force, while choices were made on the basis of the logarithm of these costs. Our results show that effort-based action selection during reaching cannot easily be explained by metabolic costs. Instead, force-loaded reaches, a widely occurring natural behavior, imposed an effort cost for decision making similar to cost functions in motor control. Our results thereby support the idea that motor control and economic choice are governed by partly overlapping optimization principles.

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Causal role of lateral prefrontal cortex in mental effort and fatigue

Soutschek

Tobler

2020

Human Brain Mapping

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Dorsolateral prefrontal cortex (DLPFC) is well-known for its role in exerting mental work, however the contribution of DLPFC for deciding whether or not to engage in effort remains unknown. Here, we assessed the causal role of DLPFC in effort-based decision making. We disrupted functioning of DLPFC with noninvasive brain stimulation before participants repeatedly decided whether to exert mental effort in a working memory task. We found the same DLPFC subregion involved in mental effort exertion to influence also effort-based decisions: First, it enhanced effort discounting, suggesting that DLPFC may signal the capacity to successfully deal with effort demands. Second, a novel computational model integrating the costs of enduring effort into the effort-based decision process revealed that DLPFC disruption reduced fatigue after accumulated effort exertion, linking DLPFC activation with fatigue. Together, our findings indicate that in effort-based decisions DLPFC represents the capacity to exert mental effort and the updating of this information with enduring time-on-task, informing theoretical accounts on the role of DLPFC in the motivation to exert mental effort and the fatigue arising from it.

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Parabolic discounting of monetary rewards by physical effort

Cited by 134 publications

References 19 publications

Separate mesocortical and mesolimbic pathways encode effort and reward learning signals

Separate mesocortical and mesolimbic pathways encode effort and reward learning signals

What makes a reach movement effortful? Physical effort discounting supports common minimization principles in decision making and motor control

Causal role of lateral prefrontal cortex in mental effort and fatigue

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