Exploring the role of task constraints on motor variability and assessing consistency in individual responses during repetitive lifting using linear variability of kinematics

2022 ◽  
Vol 100 ◽  
pp. 103668
Author(s):  
Nathalie M.C.W. Oomen ◽  
Ryan B. Graham ◽  
Steven L. Fischer
Keyword(s):  
Motor Variability ◽  
Task Constraints ◽  
Repetitive Lifting

scholarly journals High variability impairs motor learning regardless of whether it affects task performance

2018 ◽  
Vol 119 (1) ◽  
pp. 39-48 ◽  
Author(s):  
Marco Cardis ◽  
Maura Casadio ◽  
Rajiv Ranganathan
Keyword(s):  
Motor Learning ◽  
Task Performance ◽  
Null Space ◽  
Motor Task ◽  
Task Space ◽  
Movement Variability ◽  
Motor Variability ◽  
Different Dimensions ◽  
Left And Right

Motor variability plays an important role in motor learning, although the exact mechanisms of how variability affects learning are not well understood. Recent evidence suggests that motor variability may have different effects on learning in redundant tasks, depending on whether it is present in the task space (where it affects task performance) or in the null space (where it has no effect on task performance). We examined the effect of directly introducing null and task space variability using a manipulandum during the learning of a motor task. Participants learned a bimanual shuffleboard task for 2 days, where their goal was to slide a virtual puck as close as possible toward a target. Critically, the distance traveled by the puck was determined by the sum of the left- and right-hand velocities, which meant that there was redundancy in the task. Participants were divided into five groups, based on both the dimension in which the variability was introduced and the amount of variability that was introduced during training. Results showed that although all groups were able to reduce error with practice, learning was affected more by the amount of variability introduced rather than the dimension in which variability was introduced. Specifically, groups with higher movement variability during practice showed larger errors at the end of practice compared with groups that had low variability during learning. These results suggest that although introducing variability can increase exploration of new solutions, this may adversely affect the ability to retain the learned solution.NEW & NOTEWORTHY We examined the role of introducing variability during motor learning in a redundant task. The presence of redundancy allows variability to be introduced in different dimensions: the task space (where it affects task performance) or the null space (where it does not affect task performance). We found that introducing variability affected learning adversely, but the amount of variability was more critical than the dimension in which variability was introduced.

scholarly journals The role of task constraints in relating laboratory and clinical measures of balance

2015 ◽  
Vol 42 (3) ◽  
pp. 275-279 ◽  
Author(s):  
Nikita A. Kuznetsov ◽  
Michael A. Riley
Keyword(s):  
Task Constraints ◽  
Clinical Measures

scholarly journals Motor Synergies Measurement Reveals the Relevant Role of Variability in Reward-Based Learning

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6448
Author(s):  
Carla Caballero ◽  
Francisco J. Moreno ◽  
David Barbado
Keyword(s):  
Force Sensor ◽  
Fluctuation Analysis ◽  
Motor Variability ◽  
Performance Improvements ◽  
Task Goal ◽  
Motor Synergies ◽  
Relevant Role ◽  
Computer Environment ◽  
Motor Synergy

Currently, it is not fully understood how motor variability is regulated to ease of motor learning processes during reward-based tasks. This study aimed to assess the potential relationship between different dimensions of motor variability (i.e., the motor variability structure and the motor synergies variability) and the learning rate in a reward-based task developed using a two-axis force sensor in a computer environment. Forty-four participants performed a pretest, a training period, a posttest, and three retests. They had to release a virtual ball to hit a target using a vertical handle attached to a dynamometer in a computer-simulated reward-based task. The participants’ throwing performance, learning ratio, force applied, variability structure (detrended fluctuation analysis, DFA), and motor synergy variability (good and bad variability ratio, GV/BV) were calculated. Participants with higher initial GV/BV displayed greater performance improvements than those with lower GV/BV. DFA did not show any relationship with the learning ratio. These results suggest that exploring a broader range of successful motor synergy combinations to achieve the task goal can facilitate further learning during reward-based tasks. The evolution of the motor variability synergies as an index of the individuals’ learning stages seems to be supported by our study.

scholarly journals Concurrent Changes of Brain Functional Connectivity and Motor Variability When Adapting to Task Constraints

2018 ◽  
Vol 9 ◽  
Author(s):  
Grégoire Vergotte ◽  
Stéphane Perrey ◽  
Muthuraman Muthuraman ◽  
Stefan Janaqi ◽  
Kjerstin Torre
Keyword(s):  
Functional Connectivity ◽  
Motor Variability ◽  
Task Constraints ◽  
Brain Functional Connectivity

Perceptual-Cognitive Skills and Their Interaction as a Function of Task Constraints in Soccer

2013 ◽  
Vol 35 (2) ◽  
pp. 144-155 ◽  
Author(s):  
André Roca ◽  
Paul R. Ford ◽  
Allistair P. McRobert ◽  
A. Mark Williams
Keyword(s):  
Decision Making ◽  
Cognitive Skills ◽  
Superior Performance ◽  
Thought Processes ◽  
Verbal Reports ◽  
Task Constraints ◽  
Skilled Performance ◽  
Task Conditions ◽  
Specific Search

The ability to anticipate and to make decisions is crucial to skilled performance in many sports. We examined the role of and interaction between the different perceptual-cognitive skills underlying anticipation and decision making. Skilled and less skilled players interacted as defenders with life-size film sequences of 11 versus 11 soccer situations. Participants were presented with task conditions in which the ball was located in the offensive or defensive half of the pitch (far vs. near conditions). Participants’ eye movements and verbal reports of thinking were recorded across two experiments. Skilled players reported more accurate anticipation and decision making than less skilled players, with their superior performance being underpinned by differences in task-specific search behaviors and thought processes. The perceptual-cognitive skills underpinning superior anticipation and decision making were shown to differ in importance across the two task constraints. Findings have significant implications for those interested in capturing and enhancing perceptual-cognitive skill in sport and other domains.

Shared invariance control for constraint satisfaction in multi-robot systems

2019 ◽  
Vol 38 (10-11) ◽  
pp. 1268-1285
Author(s):  
Melanie Kimmel ◽  
Jannick Pfort ◽  
Jan Wöhlke ◽  
Sandra Hirche
Keyword(s):  
Constraint Satisfaction ◽  
Intelligent Agents ◽  
Task Constraints ◽  
Control Approach ◽  
Control Scheme ◽  
Robot Systems ◽  
Invariance Control ◽  
The Individual ◽  
Multi Robot

In systems involving multiple intelligent agents, e.g. multi-robot systems, the satisfaction of environmental, inter-agent, and task constraints is essential to ensure safe and successful task execution. This requires a constraint enforcing control scheme, which is able to allocate and distribute the required evasive control actions adequately among the agents, ideally according to the role of the agents or the importance of the executed tasks. In this work, we propose a shared invariance control scheme in combination with a suitable agent prioritization to control multiple agents safely and reliably. Based on the projection of the constraints into the input spaces of the individual agents using input–output linearization, shared invariance control determines constraint enforcing control inputs and facilitates implementation in a distributed manner. In order to allow for shared evasive actions, the control approach introduces weighting factors derived from a two-stage prioritization scheme, which allots the weights according to a variety of factors such as a fixed task priority, the number of constraints affecting each agent or a manipulability measure. The proposed control scheme is proven to guarantee constraint satisfaction. The approach is illustrated in simulations and an experimental evaluation on a dual-arm robotic platform.

Natural Break Points

2012 ◽  
Vol 6 (1) ◽  
pp. 5-29 ◽  
Author(s):  
Christian P. Janssen ◽  
Duncan P. Brumby ◽  
Rae Garnett
Keyword(s):  
Task Performance ◽  
Task Constraints ◽  
Optimal Balance ◽  
Modeling Analysis ◽  
Natural Break ◽  
Dual Task Performance ◽  
Break Points ◽  
Complete Set ◽  
And Task

What factors determine when people interleave tasks when multitasking? Here the authors look at the role of priorities and cognitive and motor cues. A study was conducted in which participants steered a simulated vehicle while also dialing two phone numbers that contained sets of repeating digits. Participants tended to interleave tasks after typing in a complete set of repeating digits and sometimes also at the cognitive chunk boundary. The exact pattern of how participants interleaved these tasks depended on their priority objective. A modeling analysis that explored performance for a series of alternative strategies for task interleaving, given the cognitive and task constraints, suggested why participants avoided interleaving at other points: Such strategies tend to move performance away from a trade-off curve that strikes an optimal balance between dialing and driving performance. The study highlights the role that cognitive and motor cues can play in dual-task performance and the importance of being aware, and acting on, priorities. Further implications and limitations are discussed.

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