Explanation of Fitts' law in Reaching Movement based on Human Arm Dynamics

Temporal properties of the speed-accuracy trade-off for arm-pointing movements in various directions around the body

Human body movements are based on the intrinsic trade-off between speed and accuracy. Fitts's law (1954) shows that the time required for movement is represented by a simple logarithmic equation and is applicable to a variety of movements. However, few studies have determined the role of the direction in modulating the performance of upper limb movements and the effects of the interactions between direction and distance and between direction and target size. This study examined the variations in temporal properties of the speed-accuracy trade-off in arm-pointing movements that directly manipulate objects according to the direction, distance, and target size. Participants performed pointing movements to the targets with 3 different sizes presented at 15 locations (5 directions and 3 distances) on a horizontal plane. Movement time (MT) for each trial in each condition was obtained. Subsequently, Mackenzie's model (1992), MT = a + b log2(D/W +1), where D and W represent the distance and width of the target, respectively, was fitted. The slope factor b, a fitted parameter in the equation, was calculated and evaluated according to the changes in the direction, distance, and target size. The results showed that MTs exhibited anisotropy in the hemifield, being the smallest in the right-forward direction. Additionally, the slope factor b, as a function of distance, was smaller in the rightward direction than in the forward and left-forward directions. These results suggest that the degree of difficulty of upper limb movements expands heterogeneously in various directions around the body.

Motor invariants in action execution and perception

The nervous system is sensitive to statistical regularities of the external world and forms internal models of these regularities to predict environmental dynamics. Given the inherently social nature of human behavior, being capable of building reliable predictive models of others' actions may be essential for successful interaction. While social prediction might seem to be a daunting task, the study of human motor control has accumulated ample evidence that our movements follow a series of kinematic invariants, which can be used by observers to reduce their uncertainty during social exchanges. Here, we provide an overview of the most salient regularities that shape biological motion, examine the role of these invariants in recognizing others' actions, and speculate that anchoring socially-relevant perceptual decisions to such kinematic invariants provides a key computational advantage for inferring conspecifics' goals and intentions.

The placebo effect shortens movement time in goal-directed movements

The placebo effect is a powerful psychobiological phenomenon whereby a positive outcome follows the administration of an inert treatment thought to be effective. Growing evidence shows that the placebo effect extends beyond the healing context, affecting also motor performance. Here we explored the placebo effect on the control of goal-directed movement, a fundamental function in many daily activities. Twenty-four healthy volunteers performed upper-limb movements toward a target at different indexes of difficulty in two conditions: in the placebo condition, an electrical device (inert) was applied to the right forearm together with verbal information about its positive effects in improving movement precision; in the control condition, the same device was applied along with verbal information about its neutral effects on performance. Interestingly, we found shorter movement time in the placebo compared to the control condition. Moreover, subjective perception of fatigability was reduced in the placebo compared to the control condition. These findings indicate that the placebo effect can improve the execution of goal-directed movements, thus adding new evidence to the placebo effect in the motor domain. This study could inspire future applications to improve upper-limb movements or in clinical settings for patients with motor deficits.

Hierarchical Motion Learning for Goal-Oriented Movements With Speed-Accuracy Tradeoff of a Musculoskeletal System

Generating various goal-oriented movements via the flexible muscle model of the musculoskeletal system as fast and accurately as possible is a pressing problem, which is also the basis of most human adaptive behaviors, such as reaching, catching, interception, and pointing. This article focuses on the adaptive motion generation of fast goal-oriented motion on the musculoskeletal system by implementing the speed-accuracy tradeoff (SAT) in a hierarchical motion learning framework. First, we introduce Fitts' Law into the modified basal ganglia circuit-inspired iterative decision-making model for achieving dynamic and adaptive decision making. Then, as a time constraint, the decision is decomposed into a series of supervised terms by the proposed striatal FSI-SPN interneuron circuit-inspired velocity modulator to implement the tradeoff smoothly on the musculoskeletal system. Finally, an improved policy gradient algorithm is suggested to generate the muscle excitations of the modulated motion via the proposed muscle co-contraction policy, which promotes general cooperation between flexor and extensor muscles. In experiments, a redundant musculoskeletal arm model is trained to perform the adaptive quick pointing movements. By combining the muscle co-contraction policy with SAT, our algorithm shows the most efficient training and the best performance in the adaptive motion generation among the other three popular reinforcement learning algorithms on the musculoskeletal model.

Reinforcement learning control of a biomechanical model of the upper extremity

Among the infinite number of possible movements that can be produced, humans are commonly assumed to choose those that optimize criteria such as minimizing movement time, subject to certain movement constraints like signal-dependent and constant motor noise. While so far these assumptions have only been evaluated for simplified point-mass or planar models, we address the question of whether they can predict reaching movements in a full skeletal model of the human upper extremity. We learn a control policy using a motor babbling approach as implemented in reinforcement learning, using aimed movements of the tip of the right index finger towards randomly placed 3D targets of varying size. We use a state-of-the-art biomechanical model, which includes seven actuated degrees of freedom. To deal with the curse of dimensionality, we use a simplified second-order muscle model, acting at each degree of freedom instead of individual muscles. The results confirm that the assumptions of signal-dependent and constant motor noise, together with the objective of movement time minimization, are sufficient for a state-of-the-art skeletal model of the human upper extremity to reproduce complex phenomena of human movement, in particular Fitts' Law and the [Formula: see text] Power Law. This result supports the notion that control of the complex human biomechanical system can plausibly be determined by a set of simple assumptions and can easily be learned.

Forward Inverse Relaxation Model Incorporating Movement Duration Optimization

A computational trajectory formation model based on the optimization principle, which introduces the forward inverse relaxation model (FIRM) as the hardware and algorithm, represents the features of human arm movements well. However, in this model, the movement duration was defined as a given value and not as a planned value. According to considerable empirical facts, movement duration changes depending on task factors, such as required accuracy and movement distance thus, it is considered that there are some criteria that optimize the cost function. Therefore, we propose a FIRM that incorporates a movement duration optimization module. The movement duration optimization module minimizes the weighted sum of the commanded torque change term as the trajectory cost, and the tolerance term as the cost of time. We conducted a behavioral experiment to examine how well the movement duration obtained by the model reproduces the true movement. The results suggested that the model movement duration was close to the true movement. In addition, the trajectory generated by inputting the obtained movement duration to the FIRM reproduced the features of the actual trajectory well. These findings verify the use of this computational model in measuring human arm movements.