Movement vigor as a traitlike attribute of individuality

Abstract

A common aspect of individuality is our subjective preferences in evaluation of reward and effort. The neural circuits that evaluate these commodities influence circuits that control our movements, raising the possibility that vigor differences between individuals may also be a trait of individuality, reflecting a willingness to expend effort. In contrast, classic theories in motor control suggest that vigor differences reflect a speed-accuracy trade-off, predicting that those who move fast are sacrificing accuracy for speed. Here we tested these contrasting hypotheses. We measured motion of the eyes, head, and arm in healthy humans during various elementary movements (saccades, head-free gaze shifts, and reaching). For each person we characterized their vigor, i.e., the speed with which they moved a body part (peak velocity) with respect to the population mean. Some moved with low vigor, while others moved with high vigor. Those with high vigor tended to react sooner to a visual stimulus, moving both their eyes and arm with a shorter reaction time. Arm and head vigor were tightly linked: individuals who moved their head with high vigor also moved their arm with high vigor. However, eye vigor did not correspond strongly with arm or head vigor. In all modalities, vigor had no impact on end-point accuracy, demonstrating that differences in vigor were not due to a speed-accuracy trade-off. Our results suggest that movement vigor may be a trait of individuality, not reflecting a willingness to accept inaccuracy but demonstrating a propensity to expend effort. NEW & NOTEWORTHY A common aspect of individuality is how we evaluate economic variables like reward and effort. This valuation affects not only decision making but also motor control, raising the possibility that vigor may be distinct between individuals but conserved across movements within an individual. Here we report conservation of vigor across elementary skeletal movements, but not eye movements, raising the possibility that the individuality of our movements may be driven by a common neural mechanism of effort evaluation across modalities of skeletal motor control.

Individual movement features during prism adaptation correlate with after-effects and interlimb transfer

“…We thus investigated the influence of peak velocity, peak acceleration and variable trajectory errors throughout adaptation or specifically during the early and late phases of Prism exposure (first and last 10 exposure trials). We found that interlimb transfer was correlated with variables typically associated to movement vigor, such as peak acceleration and peak velocity (Mazzoni et al 2007;Reppert et al 2018). Figure 8A shows a positive linear correlation between the transfer value and the mean peak acceleration averaged across the Prism exposure phase (PA = 0.1 × transfer value -6; r=0.52; p=0.02).…”

“…We found that peak acceleration and peak velocity during prism exposure, as well as variability of initial direction at the end of the exposure phase, were related to interlimb transfer. Mazzoni et al (2007) as well as Reppert et al (2018) highlighted how variables related to movement vigor, peak velocity or peak acceleration, for instance, vary across individuals, possibly because of differences in perceived motor cost. Kitazawa et al (1997) previously highlighted the importance of peak velocity in prism adaptation when they showed that the magnitude of the after-effect depends on the velocity difference between movements during and after the exposure phase (see also Mattar & Ostry 2010).…”

Reward-based improvements in motor control are driven by multiple error-reducing mechanisms

“…For the 10p and 50p trials participants could earn money based on their combined reaction time and movement time. The scoring function which translated performance to monetary gain was adaptive (figure 1B), factoring in the recent history of movement times and reaction times to ensure participants experienced comparable amounts of reward despite idiosyncrasies in individual’s reaction times and movement speed (Berret et al, 2018; Reppert et al, 2018; Manohar et al, 2015). To assess selection and execution performance concomitantly, we interleaved normal trials and distractor trials.…”

“…The reward function was a close-loop design that incorporated the recent history of performance, to ensure that participants received similar amounts of reward, and that the task remained consistently challenging over the experiment (Manohar et al, 2015; Reppert et al, 2018). To that end, the reward function was defined as: where r max was the maximum reward value for a given trial, MTRT the sum of reaction time and movement time, and τ 1 and τ 2 adaptable parameters varying as a function of performance (figure 1B).…”

Reward-driven enhancements in motor control are robust to TMS manipulation

“…Execution of reaching movements also showed a pronounced increase in peak velocity (vigour) with reward, while radial accuracy was maintained. While these reward-driven improvements are now behaviourally wellcharacterised and confirmed in a number of previous reports (Griffiths and Beierholm 2017;Reppert et al 2018;Summerside et al 2018), the neural substrates of these effects remain unknown. Here, we aimed to investigate which cortical regions are involved in the reward-based enhancement of motor performance using transcranial magnetic stimulation (TMS).…”