Winona Snapp-Childs

Perceptual Learning Immediately Yields New Stable Motor Coordination

Coordinated rhythmic movement is specifically structured in humans. Movement at 0° mean relative phase is maximally stable, 180° is less stable, and other coordinations can, but must, be learned. Variations in perceptual ability play a key role in determining the observed stabilities so we investigated whether stable movements can be acquired by improving perceptual ability. We assessed movement stability in Baseline, Post Training, and Retention sessions by having participants use a joystick to coordinate the movement of two dots on a screen at three relative phases. Perceptual ability was also assessed using a two-alternative forced choice task in which participants identified a target phase of 90° in a pair of displays. Participants then trained with progressively harder perceptual discriminations around 90° with feedback. Improved perceptual discrimination of 90° led to improved performance in the movement task at 90° with no training in the movement task. The improvement persisted until Retention without further exposure to either task. A control groups movement stability did not improve. Movement stability is a function of perceptual ability, and information is an integral part of the organization of this dynamical system.

Learning a coordinated rhythmic movement with task-appropriate coordination feedback

A common perception–action learning task is to teach participants to produce a novel coordinated rhythmic movement, e.g. 90° mean relative phase. As a general rule, people cannot produce these novel movements stably without training. This is because they are extremely poor at discriminating the perceptual information required to coordinate and control the movement, which means people require additional (augmented) feedback to learn the novel task. Extant methods (e.g. visual metronomes, Lissajous figures) work, but all involve transforming the perceptual information about the task and thus altering the perception–action task dynamic being studied. We describe and test a new method for providing online augmented coordination feedback using a neutral colour cue. This does not alter the perceptual information or the overall task dynamic, and an experiment confirms that (a) feedback is required for learning a novel coordination and (b) the new feedback method provides the necessary assistance. This task-appropriate augmented feedback therefore allows us to study the process of learning while preserving the perceptual information that constitutes a key part of the task dynamic being studied. This method is inspired by and supports a fully perception–action approach to coordinated rhythmic movement.

A Sensorimotor Approach to the Training of Manual Actions in Children With Developmental Coordination Disorder

Developmental coordination disorder affects a relatively large proportion (5%-6%) of the childhood population. Severity of the disorder varies but there is a great need for therapeutic intervention. We propose a method for the training of manual actions in children with developmental coordination disorder. Our solution is achieved by applying haptic virtual reality technology to attack the difficulties that children with developmental coordination disorder evidence. Our results show that children with developmental coordination disorder are able to learn complex motor skills if proper training methods are employed. These findings conflict with reports of impaired motor learning in developmental coordination disorder because of underactivation of cerebellar and parietal networks.

Active Prospective Control Is Required for Effective Sensorimotor Learning

Passive modeling of movements is often used in movement therapy to overcome disabilities caused by stroke or other disorders (e.g. Developmental Coordination Disorder or Cerebral Palsy). Either a therapist or, recently, a specially designed robot moves or guides the limb passively through the movement to be trained. In contrast, action theory has long suggested that effective skill acquisition requires movements to be actively generated. Is this true? In view of the former, we explicitly tested the latter. Previously, a method was developed that allows children with Developmental Coordination Disorder to produce effective movements actively, so as to improve manual performance to match that of typically developing children. In the current study, we tested practice using such active movements as compared to practice using passive movement. The passive movement employed, namely haptic tracking, provided a strong test of the comparison, one that showed that the mere inaction of the muscles is not the problem. Instead, lack of prospective control was. The result was no effective learning with passive movement while active practice with prospective control yielded significant improvements in performance.

Transfer of learning between unimanual and bimanual rhythmic movement coordination: transfer is a function of the task dynamic

Under certain conditions, learning can transfer from a trained task to an untrained version of that same task. However, it is as yet unclear what those certain conditions are or why learning transfers when it does. Coordinated rhythmic movement is a valuable model system for investigating transfer because we have a model of the underlying task dynamic that includes perceptual coupling between the limbs being coordinated. The model predicts that (1) coordinated rhythmic movements, both bimanual and unimanual, are organised with respect to relative motion information for relative phase in the coupling function, (2) unimanual is less stable than bimanual coordination because the coupling is unidirectional rather than bidirectional, and (3) learning a new coordination is primarily about learning to perceive and use the relevant information which, with equal perceptual improvement due to training, yields equal transfer of learning from bimanual to unimanual coordination and vice versa [but, given prediction (2), the resulting performance is also conditioned by the intrinsic stability of each task]. In the present study, two groups were trained to produce 90° either unimanually or bimanually, respectively, and tested in respect to learning (namely improved performance in the trained 90° coordination task and improved visual discrimination of 90°) and transfer of learning (to the other, untrained 90° coordination task). Both groups improved in the task condition in which they were trained and in their ability to visually discriminate 90°, and this learning transferred to the untrained condition. When scaled by the relative intrinsic stability of each task, transfer levels were found to be equal. The results are discussed in the context of the perception–action approach to learning and performance.

Perceptuo-motor learning rate declines by half from 20s to 70/80s

This study examined perception–action learning in younger adults in their 20s compared to older adults in their 70s and 80s. The goal was to provide, for the first time, quantitative estimates of perceptuo-motor learning rates for each age group and to reveal how these learning rates change between these age groups. We used a visual coordination task in which participants are asked to learn to produce a novel-coordinated rhythmic movement. The task has been studied extensively in young adults, and the characteristics of the task are well understood. All groups showed improvement, although learning rates for those in their 70s and 80s were half the rate for those in their 20s. We consider the potential causes of these differences in learning rates by examining performance across the different coordination patterns examined as well as recent results that reveal age-related deficits in motion perception.

The 50s Cliff: A Decline in Perceptuo-Motor Learning, Not a Deficit in Visual Motion Perception

Previously, we measured perceptuo-motor learning rates across the lifespan and found a sudden drop in learning rates between ages 50 and 60, called the “50s cliff.” The task was a unimanual visual rhythmic coordination task in which participants used a joystick to oscillate one dot in a display in coordination with another dot oscillated by a computer. Participants learned to produce a coordination with a 90° relative phase relation between the dots. Learning rates for participants over 60 were half those of younger participants. Given existing evidence for visual motion perception deficits in people over 60 and the role of visual motion perception in the coordination task, it remained unclear whether the 50s cliff reflected onset of this deficit or a genuine decline in perceptuo-motor learning. The current work addressed this question. Two groups of 12 participants in each of four age ranges (20s, 50s, 60s, 70s) learned to perform a bimanual coordination of 90° relative phase. One group trained with only haptic information and the other group with both haptic and visual information about relative phase. Both groups were tested in both information conditions at baseline and post-test. If the 50s cliff was caused by an age dependent deficit in visual motion perception, then older participants in the visual group should have exhibited less learning than those in the haptic group, which should not exhibit the 50s cliff, and older participants in both groups should have performed less well when tested with visual information. Neither of these expectations was confirmed by the results, so we concluded that the 50s cliff reflects a genuine decline in perceptuo-motor learning with aging, not the onset of a deficit in visual motion perception.

Training Compliance Control Yields Improvements in Drawing as a Function of Beery Scores

Many children have difficulty producing movements well enough to improve in sensori-motor learning. Previously, we developed a training method that supports active movement generation to allow improvement at a 3D tracing task requiring good compliance control. Here, we tested 7–8 year old children from several 2nd grade classrooms to determine whether 3D tracing performance could be predicted using the Beery VMI. We also examined whether 3D tracing training lead to improvements in drawing. Baseline testing included Beery, a drawing task on a tablet computer, and 3D tracing. We found that baseline performance in 3D tracing and drawing co-varied with the visual perception (VP) component of the Beery. Differences in 3D tracing between children scoring low versus high on the Beery VP replicated differences previously found between children with and without motor impairments, as did post-training performance that eliminated these differences. Drawing improved as a result of training in the 3D tracing task. The training method improved drawing and reduced differences predicted by Beery scores.

Jetstream: A Cloud System Enabling Learning in Higher Education Communities

Jetstream is the first production cloud funded by the NSF for conducting general-purpose science and engineering research as well as an easy-to-use platform for education activities. Unlike many high-performance computing systems, Jetstream uses the interactive Atmosphere graphical user interface developed as part of the iPlant (now CyVerse) project and focuses on interactive use on uniprocessor or multiprocessor. This interface provides for a lower barrier of entry for use by educators, students, practicing scientists, and engineers. A key part of Jetstreams mission is to extend the reach of the NSFs eXtreme Digital (XD) program to a community of users who have not previously utilized NSF XD program resources, including those communities and institutions that traditionally lack significant cyberinfrastructure resources. One manner in which Jetstream eases this access is via virtual desktops facilitating use in education and research at small colleges and universities, including Historically Black Colleges and Universities (HBCUs), Minority Serving Institutions (MSIs), Tribal colleges, and higher education institutions in states designated by the NSF as eligible for funding via the Experimental Program to Stimulate Competitive Research (EPSCoR). Jetstream entered into full production in September 2016 and during the first six months it has supported more than a dozen educational efforts across the United States. Here, we discuss how educators at institutions of higher education have been using Jetstream in the classroom and at student-focused workshops. Specifically, we explore success stories, difficulties encountered, and everything in between. We also discuss plans for increasing the use of cloud-based systems in higher education. A primary goal in this paper is to spark discussions between educators and information technologists on how to improve using cloud resources in education.

Training to improve manual control in 7–8 and 10–12 year old children: Training eliminates performance differences between ages

Many children have difficulty producing movements well enough to improve in perceptuo-motor learning. We have developed a training method that supports active movement generation to allow improvement in a 3D tracing task requiring good compliance control. We previously tested 7-8 year old children who exhibited poor performance and performance differences before training. After training, performance was significantly improved and performance differences were eliminated. According to the Dynamic Systems Theory of development, appropriate support can enable younger children to acquire the ability to perform like older children. In the present study, we compared 7-8 and 10-12 year old school children and predicted that younger children would show reduced performance that was nonetheless amenable to training. Indeed, the pre-training performance of the 7-8 year olds was worse than that of the 10-12 year olds, but post-training performance was equally good for both groups. This was similar to previous results found using this training method for children with DCD and age-matched typically developing children. We also found in a previous study of 7-8 year old school children that training in the 3D tracing task transferred to a 2D drawing task. We now found similar transfer for the 10-12 year olds.