Scientific Insight that Will Guide Future Study of Visual Regulation of Human Locomotion - A Testament to the Contribution of Dr. Aftab Patla
Bibliographic record
Abstract
The article by Daniel S. Marigold, Ph.D., (4) (see page 145 in this issue) is a stimulating summary of our current understanding of how visual information is used in the feedback and feed-forward control of human locomotion. The Marigold review was written in memory of Aftab Patla, Ph.D., (1954-2007), former supervisor of Dr. Marigold. Dr. Patla succumbed to an aggressive brain tumor on January 29, 2007, 8 months after the initial diagnosis. He was a professor in the Department of Kinesiology at the University of Waterloo. The main theme and focus of Dr. Patla's more than 200 scientific contributions across his career was the regulation and control of human locomotion. Throughout his distinguished academic career, Dr. Patla addressed issues related to determining the following: what aspect of the locomotor patterns are preplanned and stored within the central nervous system, the regulatory role of sensory information in shaping the basic locomotor patterns in "cluttered" environments, and the effects of development and aging on the expression of locomotor behaviors. Dr. Patla incorporated experiment and modeling to provide deep insight into important problems related to gait and posture. He also made use of methodologies from biomechanics, neurophysiology, motor behavior, exercise physiology, mathematics, and computer modeling. Dr. Patla was an ardent supporter and key contributor to the field of kinesiology and human movement science. Many of Dr. Patla's most significant contributions were in the area of the generation and regulation of the human locomotor pattern by feedback. As early as 1985, he used data from a reduced cat preparation combined with mathematical modeling to address the complexity of a multilevel locomotor central pattern generator (CPG) (9). In the article, he argued that the locomotor CPG should be considered to have three subcomponents: an oscillator for basic rhythm generation, interneuronal layers for shaping the pattern to be produced, and weighting functions for each muscle such that the overall walking pattern is appropriately generated across a range of speeds. The article presaged what now constitutes a hot and burgeoning area in motor control neuroscience (see (5)). The use of afferent feedback to shape and regulate the locomotor pattern was also a key early contribution of Dr. Patla. In three important publications, he addressed the issues of phase- and task-dependent modulation of reflex amplitude (1,2,8) that remains to this day a much investigated area. It was also more than 20 yr ago that the seeds of the visual regulation of human gait were planted (3). A central point of his later work was that visual input performs a crucial role as both feedback and feed-forward (i.e., "one step ahead") regulation of walking. One of his most influential and highly cited articles was published in 1991 on the issue of the use of vision in sculpting obstacle avoidance during walking (11). The influence of vision on the regulation of walking was later expounded upon in many influential articles (6,7,10,12). In his article here, Dr. Marigold highlights this main theme of Dr. Patla's research focus in recent years - that is, the issue of visual guidance of locomotion and navigation in a cluttered environment. This review describes how visual information is used on-line to plan and sculpt the locomotor pattern. Key to this article and the work of Dr. Marigold is the issue of visual cues picked up from so-called peripheral vision and automatically integrated into the overall walking pattern. This information automatically updates and refines on a continuous basis our locomotor progression throughout the environment. Dr. Marigold clearly describes how vision has an integrative role to play in meshing with other sensory modalities to ensure safe placement of the foot and leg during walking. In this way, visual information can be seen as crucial to appropriate sculpting of the locomotor pattern and, as with other modalities such as somatosensory feedback, is especially crucial in uneven terrain or cluttered environments. The article of Dr. Marigold (4) therefore summarizes and extends many of the approaches Dr. Patla took many years ago about the automatic regulation of locomotion. It serves as an excellent contribution to honor his memory while simultaneously pointing the way to the bright future his efforts have helped to secure. In closing, above all, Dr. Patla was a preeminent scientist who should be remembered as much for his significant contributions to research as for his humanity. He represents that rare combination of scientific rigor, grace, and honor to which we should all aspire. Dr. Patla mentored 22 M.Sc., 25 Ph.D., and 4 postdoctoral fellows, ensuring that the impact of this gentleman-scholar will continue to reverberate for many scientific generations. E. PAUL ZEHR Rehabilitation Neuroscience Laboratory University of Victoria Victoria, British Columbia Canada
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".