98%
921
2 minutes
20
In this study we asked whether Fitts' Law, a well-established relationship that predicts movement times (MTs) for direct movements between two positions, could be extended to predict MTs for curved, obstacle avoiding, movements. We had participants make movements in the presence of an obstacle. Using these data, we tested an extensions of Fitts' Law that predicted MTs based on the movement's index of difficulty and the distance that the obstacle intruded into the direct movement path. Including both factors led to more accurate predictions of MTs for obstacle-avoiding movements than was possible with the index of difficulty alone. In addition, the simple extension of Fitts' Law did as well as a model which relied on the obtained movement paths between targets. This is an encouraging outcome because it suggests that the physical layout of the workspace can be used to predict MTs for obstacle avoiding movements, an accomplishment that fits with the spirit of Fitts' Law.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1007/s00221-007-0996-y | DOI Listing |
Comput Biol Med
September 2025
Department of Electrical and Computer Engineering and the Institute of Biomedical Engineering, University of New Brunswick, Fredericton, E3B 5A3, NB, Canada.
Pattern recognition-based myoelectric control is traditionally trained with static or ramp contractions, but this fails to capture the dynamic nature of real-world movements. This study investigated the benefits of training classifiers with continuous dynamic data, encompassing transitions between various movement classes. We employed both conventional (LDA) and deep learning (LSTM) classifiers, comparing their performance when trained with ramp data, continuous dynamic data, and an LSTM pre-trained with a self-supervised learning technique (VICReg).
View Article and Find Full Text PDFNeural Comput
August 2025
Computing and Mathematical Sciences, California Institute of Technology, Pasadena, CA 91125, USA
Human sensorimotor control is remarkably fast and accurate at the system level despite severe speed-accuracy trade-offs at the component level. The discrepancy between the contrasting speed-accuracy trade-offs at these two levels is a paradox. Meanwhile, speed accuracy trade-offs, heterogeneity, and layered architectures are ubiquitous in nerves, skeletons, and muscles, but they have only been studied in isolation using domain-specific models.
View Article and Find Full Text PDFIEEE Trans Vis Comput Graph
August 2025
Virtual Reality (VR) technologies in fields such as telehealth, teleconferencing, and virtual education are significantly affected by end-to-end latency, which notably impacts users' interactive experience and performance. Previous research suggests that a perceptual threshold may exist-once latency is reduced below a certain level, users no longer perceive it, and their interactive performance remains largely unaffected. However, there is no consensus on the exact value of this absolute latency perception threshold.
View Article and Find Full Text PDFHum Mov Sci
August 2025
Department of Biomechanics, University of Nebraska at Omaha, Omaha, NE, USA. Electronic address:
The Optimal Movement Variability Hypothesis posits that healthy movements exhibit an optimal structure of variability, characterized by fractal patterns, which confer both stability and flexibility in motor control. This optimal, fractally-structured, variability has been associated with reduced metabolic cost during walking and enhanced resilience to perturbations. However, the full extent of the potential benefits of this variability remains largely unexplored.
View Article and Find Full Text PDFJ Mot Behav
July 2025
School of Health and Sport Sciences, Liverpool Hope University, Liverpool, UK.
Prolonged movement time as a function of task difficulty (as defined by the Index of Difficulty [ID]) can be equally prevalent within executed and imagined movements ─ something referred to as the . This effect has been leveraged as support for , where an internal representation can be shared for execution and imagery. However, times tend to rise exponentially more for imagined, compared to executed, movements, which could be attributed to the time spent within a task.
View Article and Find Full Text PDF