Reward-dependent selection of feedback gains impacts rapid motor decisions

Expected reward is known to affect planning strategies through modulation of movement vigor. Strikingly, although current theories suggest that movement planning consists in selecting a goal-directed control policy, the influence of reward on feedback control strategies remains unknown. Here we investigated this question in three human reaching experiments. First, we varied the explicit reward associated with the goal target and found an overall increase in movement vigor for higher reward targets, highlighted by larger velocities, feedback responses to external loads, and background muscle activity. Then, assuming that larger feedback gains were used to reject perturbations, we sought to investigate whether this effect hindered online decisions to switch to a new target in the presence of multiple successful goals. We indeed observed idiosyncratic switching strategies dependent on both target rewards and movement vigor, such that the more vigorous movements were less likely to switch to a new goal following perturbations. To gain further insight into a causal influence of movement vigor on rapid motor decisions, we demonstrated that biasing the baseline activity and reflex gains by means of a background load evoked a larger proportion of target switches in the direction opposite to the background load associated with lower muscle activity. Our results highlight the competition between movement vigor and flexibility to switch target during movement.

How Is Attention in Soldiers Affected by Carrying Combat Equipment?

Background: Load carriage is a common task in military contexts. This study analysed the influence of carrying different equipment during an obstacle course on perception and attention performance in soldiers belonging to the Spanish infantry. Methods: Forty-six soldiers were evaluated before and after having completed a 1-km obstacle course carrying the combat equipment and carrying no additional load (control). The determination test was used to measure the stress tolerance and reaction abilities, while the divided attention test measured the alertness, vigilance and divided attention. Results: A significant decrease was observed in the reaction time after the course compared to the pre-course in the control and combat conditions. In contrast, the correct and incorrect responses and number of reactions increased from the pre to post-obstacle test in the control and combat conditions. Conclusion: Soldiers improved their arousal after having completed a moderate intensity obstacle test, answering more quickly than on a pre-obstacle test.

Background Load Denoising across Complex Load Based on Generative Adversarial Network to Enhance Load Identification

Non-Intrusive Load Monitoring (NILM) allows load identification of appliances through a single sensor. By using NILM, users can monitor their electricity consumption, which is beneficial for energy efficiency or energy saving. In advance NILM systems, identification of appliances on/off events should be processed instantly. Thus, it is necessary to use an extremely short period signal of appliances to shorten the time delay for users to acquire event information. However, acquiring event information from a short period signal raises another problem. The problem is target load feature to be easily mixed with background load. The more complex the background load has, the noisier the target load occurs. This issue certainly reduces the appliance identification performance. Therefore, we provide a novel methodology that leverages Generative Adversarial Network (GAN) to generate noise distribution of background load then use it to generate a clear target load. We also built a Convolutional Neural Network (CNN) model to identify load based on single load data. Then we use that CNN model to evaluate the target load generated by GAN. The result shows that GAN is powerful to denoise background load across the complex load. It yields a high accuracy of load identification which could reach 92.04%.

Fibre slippage in cyclically perturbed fibrous assemblies

The dynamics of fibre slippage within general non-bonded fibrous assemblies is studied in the situation where the assembly is subjected to general small cyclic loads. Two models are proposed. The first is applicable when the general cyclic loading is complemented by an occasional tugging force on one end of a fibre, which causes it to gradually withdraw from the assembly, such as might occur during the pilling of a textile. The second considers the situation in which the cyclic perturbations act around a constant background load applied to the assembly. The dynamics is reminiscent of self-organized critical behaviour. This model is applied to predict the progressive elongation of a single yarn during weaving.

Effect of background loading on perception of inspiratory loads

The effect of background loading on magnitude estimation of added elastic and resistive inspiratory loads was determined. An analogous study involving estimation of the heaviness of weights in the hand was also performed. Perceptual performance was assessed using Stevens' power law psi = k phi n, where psi is the subjective magnitude, phi is the peak mouth pressure generated with an inspiratory load or the weight of the load in grams for the heaviness estimation, and the exponent n characterizes perceptual performance. The value of n was determined for the control and background conditions for each study. The results for both inspiratory loading studies and the heaviness estimation experiment indicate that background loading is associated with a significant increase in the exponent for magnitude estimation (P less than 0.05). Adjustment of the stimulus scale by subtracting the difference in peak mouth pressures generated during resting breathing between control and background-loaded conditions for the inspiratory loading studies, or the weight of the background load in the heaviness estimation experiment, converted the exponents obtained under background-loaded conditions to values that were not significantly different from those for control (P greater than 0.05). These results are consistent with the theory suggesting that an increase in detection threshold, produced by the background load, is responsible for the increase in exponent for magnitude estimation.