Individual variations in motor adaptation rate were recently shown to correlate with movement variability or “motor noise” in a forcefield adaptation task. However, this finding could not be replicated in a meta-analysis of adaptation experiments. Possibly, this inconsistency stems from noise being composed of distinct components that relate to adaptation rate in different ways. Indeed, previous modeling and electrophysiological studies have suggested that motor noise can be factored into planning noise, originating from the brain, and execution noise, stemming from the periphery. Were the motor system optimally tuned to these noise sources, planning noise would correlate positively with adaptation rate, and execution noise would correlate negatively with adaptation rate, a phenomenon familiar in Kalman filters. To test this prediction, we performed a visuomotor adaptation experiment in 69 subjects. Using a novel Bayesian fitting procedure, we succeeded in applying the well-established state-space model of adaptation to individual data. We found that adaptation rate correlates positively with planning noise (β = 0.44; 95% HDI = [0.27 0.59]) and negatively with execution noise (β = –0.39; 95% HDI = [–0.50 –0.30]). In addition, the steady-state Kalman gain calculated from planning and execution noise correlated positively with adaptation rate (r = 0.54; 95% HDI = [0.38 0.66]). These results suggest that motor adaptation is tuned to approximate optimal learning, consistent with the “optimal control” framework that has been used to explain motor control. Since motor adaptation is thought to be a largely cerebellar process, the results further suggest the sensitivity of the cerebellum to both planning noise and execution noise.

This work provides an internationally comparable consumer food waste dataset based on food availability, energy gap and consumer affluence. Such data can be used for constructing meaningful and internationally comparable metrics on food waste, such as those for Sustainable Development Goal 12. The data suggests that consumer food waste follows a linear-log relationship with consumer affluence and starts to emerge when consumers reach a threshold of approximately $6.70/day/capita level of expenditure. These findings also imply that most empirical models overestimate consumption by not accounting for the possibility of food waste in their analysis. The results also show that the most widely cited global estimate of food waste is underestimated by a factor greater than 2 (214 Kcal/day/capita versus 527 Kcal/day/capita). Comparison with estimates of US consumer food waste based on national survey data shows this approach can reasonably reproduce the results without needing extensive data from national surveys.

The specific role for BDNF Val66Met in eyeblink conditioning, but not vestibulo-ocular reflex adaptation, saccade adaptation or visuomotor adaptation could be related to dominance of the role of simple spike suppression of cerebellar Purkinje cells with a high baseline firing frequency in eyeblink conditioning. Susceptibility of non-carriers to anodal tDCS in eyeblink conditioning might be explained by a relatively larger effect of tDCS-induced subthreshold depolarization in this group, which might increase the spontaneous firing frequency up to the level of that of the carriers.

Individual variations in motor adaptation rate were recently shown to correlate with movement variability or “motor noise” in a forcefield adaptation task. However, this finding could not be replicated in a meta-analysis of visuomotor adaptation experiments. Possibly, this inconsistency stems from noise being composed of distinct components which relate to adaptation rate in different ways. Indeed, previous modeling and electrophysiological studies have suggested that motor noise can be factored into planning noise, originating from the brain, and execution noise, stemming from the periphery. Were the motor system optimally tuned to these noise sources, planning noise would correlate positively with adaptation rate and execution noise would correlate negatively with adaptation rate, a phenomenon familiar in Kalman filters. To test this prediction, we performed a visuomotor adaptation experiment in 69 subjects. Using a novel Bayesian fitting procedure, we succeeded in applying the well-established state-space model of adaptation to individual data. We found that adaptation rate correlates positively with planning noise (r=0.27; 95%HDI=[0.05 0.50]) and negatively with execution noise (r=−0.41; 95%HDI=[−0.63 −0.16]). In addition, the steady-state Kalman gain calculated from state and execution noise correlated positively with adaptation rate (r = 0.31; 95%HDI = [0.09 0.54]). These results suggest that motor adaptation is tuned to approximate optimal learning, consistent with the “optimal control” framework that has been used to explain motor control. Since motor adaptation is thought to be a largely cerebellar process, the results further suggest the sensitivity of the cerebellum to both planning noise and execution noise.SIGNIFICANCE STATEMENTOur study shows that the adaptation rate is optimally tuned to planning noise and execution noise across individuals. This suggests that motor adaptation is tuned to approximate optimal learning, consistent with “optimal control” approaches to understanding the motor system. In addition, our results imply sensitivity of the cerebellum to both planning noise and execution noise, an idea not previously considered. Finally, our Bayesian statistical approach represents a powerful, novel method for fitting the well-established state-space models that could have an influence on the methodology of the field.