Given two rewarding stimuli, animals tend to choose the more rewarding (or less effortful) option. However, they also move faster toward that stimulus [1-5]. This suggests that reward and effort not only affect decision-making, they also influence motor control [6, 7]. How does the brain compute the effort requirements of a task? Here, we considered data acquired during walking, reaching, flying, or isometric force production. In analyzing the decision-making and motor-control behaviors of various animals, we considered the possibility that the brain may estimate effort objectively, via the metabolic energy consumed to produce the action. We measured the energetic cost of reaching and found that, like walking, it was convex in time, with a global minimum, implying that there existed a movement speed that minimized effort. However, reward made it worthwhile to be energetically inefficient. Using a framework in which utility of an action depended on reward and energetic cost, both discounted in time, we found that it was possible to account for a body of data in which animals were free to choose how to move (reach slow or fast), as well as what to do (walk or fly, produce force F1 or F2). We suggest that some forms of decision-making and motor control may share a common utility in which the brain represents the effort associated with performing an action objectively via its metabolic energy cost and then, like reward, temporally discounts it as a function of movement duration.

Making a movement may be thought of as an economic decision in which one spends effort to acquire reward. Time discounts reward, which predicts that the magnitude of reward should affect movement vigor: we should move faster, spending greater effort, when there is greater reward at stake. Indeed, saccade peak velocities are greater and reaction-times shorter when a target is paired with reward. In this study, we focused on human reaching and asked whether movement kinematics were affected by expectation of reward. Participants made out-and-back reaching movements to one of four quadrants of a 14-cm circle. During various periods of the experiment only one of the four quadrants was paired with reward, and the transition from reward to nonreward status of a quadrant occurred randomly. Our experiment design minimized dependence of reward on accuracy, granting the subjects wide latitude in self-selecting their movement speed, amplitude, and variability. When a quadrant was paired with reward, reaching movements had a shorter reaction time, higher peak velocity, and greater amplitude. Despite this greater vigor, movements toward the rewarded quadrant suffered from less variability: both reaction times and reach kinematics were less variable when there was expectation of reward. Importantly, the effect of reward on vigor was specific to the movement component that preceded the time of reward (outward reach), not the movement component that followed it (return reach). Our results suggest that expectation of reward not only increases vigor of human reaching but also decreases its variability. NEW & NOTEWORTHY Movements may be thought of as an economic transaction where the vigor of the movement represents the effort that the brain is willing to expend to acquire a rewarding state. We show that in reaching, reward discounts the cost of effort, producing movements with shorter reaction time, higher velocity, greater amplitude, and reduced reaction-time variability. These results complement earlier observations in saccades, suggesting a common principle of economics across modalities of motor control.

Recent findings have demonstrated that reward feedback alone can drive motor learning. However, it is not yet clear whether reward feedback alone can lead to learning when a perturbation is introduced abruptly, or how a reward gradient can modulate learning. In this study, we provide reward feedback that decays continuously with increasing error. We asked whether it is possible to learn an abrupt visuomotor rotation by reward alone, and if the learning process could be modulated by combining reward and sensory feedback and/or by using different reward landscapes. We designed a novel visuomotor learning protocol during which subjects experienced an abruptly introduced rotational perturbation. Subjects received either visual feedback or reward feedback, or a combination of the two. Two different reward landscapes, where the reward decayed either linearly or cubically with distance from the target, were tested. Results demonstrate that it is possible to learn from reward feedback alone and that the combination of reward and sensory feedback accelerates learning. An analysis of the underlying mechanisms reveals that although reward feedback alone does not allow for sensorimotor remapping, it can nonetheless lead to broad generalization, highlighting a dissociation between remapping and generalization. Also, the combination of reward and sensory feedback accelerates learning without compromising sensorimotor remapping. These findings suggest that the use of reward feedback is a promising approach to either supplement or substitute sensory feedback in the development of improved neurorehabilitation techniques. More generally, they point to an important role played by reward in the motor learning process.

To understand subjective evaluation of an option, various disciplines have quantified the interaction between reward and effort during decision-making, producing an estimate of economic utility, i.e., the subjective 'goodness' of an option. However, variables that affect utility of an option also influence vigor of movements toward that option, i.e., reaction-time plus movementtime. For example, expectation of reward increases speed of saccadic eye movements, whereas expectation of effort decreases this speed. These results imply that vigor may serve as a new, realtime metric with which to quantify subjective utility, and that control of movements may be an implicit reflection of the brain's economic evaluation of the expected outcome.

Increased expression of the regulatory subunit of HIFs (HIF-1α or HIF-2α) is associated with metabolic adaptation, angiogenesis, and tumor progression. Understanding how HIFs are regulated is of intense interest. Intriguingly, the molecular mechanisms that link mitochondrial function with the HIF-regulated response to hypoxia remain to be unraveled. Here we describe what we believe to be novel functions of the human gene CHCHD4 in this context. We found that CHCHD4 encodes 2 alternatively spliced, differentially expressed isoforms (CHCHD4.1 and CHCHD4.2). CHCHD4.1 is identical to MIA40, the homolog of yeast Mia40, a key component of the mitochondrial disulfide relay system that regulates electron transfer to cytochrome c. Further analysis revealed that CHCHD4 proteins contain an evolutionarily conserved coiled-coil-helix-coiled-coilhelix (CHCH) domain important for mitochondrial localization. Modulation of CHCHD4 protein expression in tumor cells regulated cellular oxygen consumption rate and metabolism. Targeting CHCHD4 expression blocked HIF-1α induction and function in hypoxia and resulted in inhibition of tumor growth and angiogenesis in vivo. Overexpression of CHCHD4 proteins in tumor cells enhanced HIF-1α protein stabilization in hypoxic conditions, an effect insensitive to antioxidant treatment. In human cancers, increased CHCHD4 expression was found to correlate with the hypoxia gene expression signature, increasing tumor grade, and reduced patient survival. Thus, our study identifies a mitochondrial mechanism that is critical for regulating the hypoxic response in tumors.

It is often assumed that the CNS controls movements in a manner that minimizes energetic cost. While empirical evidence for actual metabolic minimization exists in locomotion, actual metabolic cost has yet to be measured during motor learning and/or arm reaching. Here, we measured metabolic power consumption using expired gas analysis, as humans learned novel arm reaching dynamics. We hypothesized that (1) metabolic power would decrease with motor learning and (2) muscle activity and coactivation would parallel changes in metabolic power. Seated subjects made horizontal planar reaching movements toward a target using a robotic arm. The novel dynamics involved compensating for a viscous curl force field that perturbed reaching movements. Metabolic power was measured continuously throughout the protocol. Subjects decreased movement error and learned the novel dynamics. By the end of learning, net metabolic power decreased by ϳ20% (ϳ0.1 W/kg) from initial learning. Muscle activity and coactivation also decreased with motor learning. Interestingly, distinct and significant reductions in metabolic power occurred even after muscle activity and coactivation had stabilized and movement changes were small. These results provide the first evidence of actual metabolic reduction during motor learning and for a reaching task. Further, they suggest that muscle activity may not explain changes in metabolic cost as completely as previously thought. Additional mechanisms such as more subtle features of arm muscle activity, changes in activity of other muscles, and/or more efficient neural processes may also underlie the reduction in metabolic cost during motor learning.

During foraging, animals decide how long to stay at a patch and harvest reward, and then, they move with certain vigor to another location. How does the brain decide when to leave, and how does it determine the speed of the ensuing movement? Here, we considered the possibility that both the decision-making and the motor control problems aimed to maximize a single normative utility: the sum of all rewards acquired minus all efforts expended divided by total time. This optimization could be achieved if the brain compared a local measure of utility with its history. To test the theory, we examined behavior of people as they gazed at images: they chose how long to look at the image (harvesting information) and then moved their eyes to another image, controlling saccade speed. We varied reward via image content and effort via image eccentricity, and then, we measured how these changes affected decision making (gaze duration) and motor control (saccade speed). After a history of low rewards, people increased gaze duration and decreased saccade speed. In anticipation of future effort, they lowered saccade speed and increased gaze duration. After a history of high effort, they elevated their saccade speed and increased gaze duration. Therefore, the theory presented a principled way with which the brain may control two aspects of behavior: movement speed and harvest duration. Our experiments confirmed many (but not all) of the predictions, suggesting that harvest duration and movement speed, fundamental aspects of behavior during foraging, may be governed by a shared principle of control.

The ability to learn new movements and dynamics is important for maintaining independence with advancing age. Age-related sensorimotor changes and increased muscle coactivation likely alter the trial-and-error-based process of adapting to new movement demands (motor adaptation). Here, we asked, to what extent is motor adaptation to novel dynamics maintained in older adults (≥65 yr)? We hypothesized that older adults would adapt to the novel dynamics less well than young adults. Because older adults often use muscle coactivation, we expected older adults to use greater muscle coactivation during motor adaptation than young adults. Nevertheless, we predicted that older adults would reduce muscle activity and metabolic cost with motor adaptation, similar to young adults. Seated older (n = 11, 73.8 ± 5.6 yr) and young (n = 15, 23.8 ± 4.7 yr) adults made targeted reaching movements while grasping a robotic arm. We measured their metabolic rate continuously via expired gas analysis. A force field was used to add novel dynamics. Older adults had greater movement deviations and compensated for just 65% of the novel dynamics compared with 84% in young adults. As expected, older adults used greater muscle coactivation than young adults. Last, older adults reduced muscle activity with motor adaptation and had consistent reductions in metabolic cost later during motor adaptation, similar to young adults. These results suggest that despite increased muscle coactivation, older adults can adapt to the novel dynamics, albeit less accurately. These results also suggest that reductions in metabolic cost may be a fundamental feature of motor adaptation.