Movement planning is thought to be primarily determined by motor costs such as inaccuracy and effort. Solving for the optimal plan that minimizes these costs typically leads to specifying a time-varying feedback controller which both generates the movement and can optimally correct for errors that arise within a movement. However, the quality of the sensory feedback during a movement can depend substantially on the generated movement. We show that by incorporating such state-dependent sensory feedback, the optimal solution incorporates active sensing and is no longer a pure feedback process but includes a significant feedforward component. To examine whether people take into account such state-dependency in sensory feedback we asked people to make movements in which we controlled the reliability of sensory feedback. We made the visibility of the hand state-dependent, such that the visibility was proportional to the component of hand velocity in a particular direction. Subjects gradually adapted to such a sensory perturbation by making curved hand movements. In particular, they appeared to control the late visibility of the movement matching predictions of the optimal controller with state-dependent sensory noise. Our results show that trajectory planning is not only sensitive to motor costs but takes sensory costs into account and argues for optimal control of movement in which feedforward commands can play a significant role. The dominant theory of how movements are planned suggests that a task specifies a motor cost in terms of effort and accuracy and the motor system chooses a movement to minimize this cost. The pre-eminent theory is that this results an optimal feedback controller that both generates the movement and can correct online for disturbances that arise due to factors such as noise. However, current formulations of the optimal feedback control problem have assumed that sensory information has noise independent of the action, or noise that simply scales with movement speed. In reality, visual and proprioceptive noise of limb position vary substantially with the state of the body. We show that including such variation in sensory feedback qualitatively changes the optimal solution so that feedback control alone is no longer optimal for the task and an additional feedforward controller must be used. To test this idea, we develop a novel motor learning paradigm in which we can vary the sensory noise on visual feedback of the limb and show that human participants adapt to such a perturbation to produce complex reach paths which are well accounted for by our model.

With continued advancements in portable eye-tracker technology liberating experimenters from the restraints of artificial laboratory designs, research can now collect gaze data from real-world, natural navigation. However, the field lacks a robust method for achieving this, as past approaches relied upon the time-consuming manual annotation of eye-tracking data, while previous attempts at automation lack the necessary versatility for in-the-wild navigation trials consisting of complex and dynamic scenes. Here, we propose a system capable of informing researchers of where and what a user’s gaze is focused upon at any one time. The system achieves this by first running footage recorded on a head-mounted camera through a deep-learning-based object detection algorithm called Masked Region-based Convolutional Neural Network (Mask R-CNN). The algorithm’s output is combined with frame-by-frame gaze coordinates measured by an eye-tracking device synchronized with the head-mounted camera to detect and annotate, without any manual intervention, what a user looked at for each frame of the provided footage. The effectiveness of the presented methodology was legitimized by a comparison between the system output and that of manual coders. High levels of agreement between the two validated the system as a preferable data collection technique as it was capable of processing data at a significantly faster rate than its human counterpart. Support for the system’s practicality was then further demonstrated via a case study exploring the mediatory effects of gaze behaviors on an environment-driven attentional bias.

Hill-type muscle models are highly preferred as phenomenological models for musculoskeletal simulation studies despite their introduction almost a century ago. The use of simple Hill-type models in simulations, instead of more recent cross-bridge models, is well justified since computationally ‘light-weight’—although less accurate—Hill-type models have great value for large-scale simulations. However, this article aims to invite discussion on numerical instability issues of Hill-type muscle models in simulation studies, which can lead to computational failures and, therefore, cannot be simply dismissed as an inevitable but acceptable consequence of simplification. We will first revisit the basic premises and assumptions on the force–length and force–velocity relationships that Hill-type models are based upon, and their often overlooked but major theoretical limitations. We will then use several simple conceptual simulation studies to discuss how these numerical instability issues can manifest as practical computational problems. Lastly, we will review how such numerical instability issues are dealt with, mostly in an ad hoc fashion, in two main areas of application: musculoskeletal biomechanics and computer animation.

As a novel non-invasive human brain stimulation method, transcranial focused ultrasound (tFUS) is receiving growing attention due to its superior spatial specificity and depth penetrability. Since the focal point of tFUS needs to be fixated precisely to the target brain region during stimulation, a critical issue is to identify and maintain the accurate position and orientation of the tFUS transducer relative to the subject's head. This study aims to propose the entire framework of tFUS stimulation integrating the methods previously proposed by the authors for tFUS transducer configuration optimization and a subject-specific 3D-printed helmet, and to validate this complete setup in a human behavioral neuromodulation study. To find the optimal configuration of the tFUS transducer, a numerical method based on subject-specific tFUS beamlines simulation was used. Then, the subject-specific 3D-printed helmet has been applied to effectively secure the transducer at the estimated optimal configuration. To validate this tFUS framework, a common behavioral neuromodulation paradigm was chosen; the effect of the dorsolateral prefrontal cortex (DLPFC) stimulation on anti-saccade (AS) behavior. While human participants (n=2) were performing AS tasks, tFUS stimulations were randomly applied to the left DLPFC right after the fixation target disappeared. The neuromodulation result strongly suggests that the cortical stimulation using the proposed tFUS setup is effective in significantly reducing the error rates of anti-saccades (about -10 %p for S1 and -16 %p for S2), whereas no significant effect was observed on their latencies. These observed behavioral effects are consistent with the previous results based on conventional brain stimulation or lesion studies. The proposed subject-specific tFUS framework has been effectively used in human neuromodulation study. The result suggests that the tFUS stimulation targeted to the DLPFC can generate a neuromodulatory effect on AS behavior.

When opposing force fields are presented alternately or randomly across trials for identical reaching movements, subjects learn neither force field, a behavior termed ‘interference’. Studies have shown that a small difference in the endpoint posture of the limb reduces this interference. However, any difference in the limb’s endpoint location typically changes the hand position, joint angles and the hand orientation making it ambiguous as to which of these changes underlies the ability to learn dynamics that normally interfere. Here we examine the extent to which each of these three possible coordinate systems—Cartesian hand position, shoulder and elbow joint angles, or hand orientation—underlies the reduction in interference. Subjects performed goal-directed reaching movements in five different limb configurations designed so that different pairs of these configurations involved a change in only one coordinate system. By specifically assigning clockwise and counter-clockwise force fields to the configurations we could create three different conditions in which the direction of the force field could only be uniquely distinguished in one of the three coordinate systems. We examined the ability to learn the two fields based on each of the coordinate systems. The largest reduction of interference was observed when the field direction was linked to the hand orientation with smaller reductions in the other two conditions. This result demonstrates that the strongest reduction in interference occurred with changes in the hand orientation, suggesting that hand orientation may have a privileged role in reducing motor interference for changes in the endpoint posture of the limb.