Moving slowly is hard for humans: limitations of dynamic primitives

Park, Se-Woong; Marino, Hamal; Charles, Steven K.; Sternad, Dagmar; Hogan, Neville

doi:10.1152/jn.00643.2016

Cited by 50 publications

(44 citation statements)

References 71 publications

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“…Here, we studied linear movements in a specific range of mean velocity (1.4-10 cm/s). This range overlaps with ranges used in previous studies of so-called "slow" movements (Vallbo and Wessberg 1993;Doeringer and Hogan 1998;Park et al 2017) and corresponds to movements with pervading velocity fluctuations (Fig. 2B).…”

Section: Task Designsupporting

confidence: 73%

“…the profile becomes more irregular and contains more peaks as duration increases. This has been observed qualitatively for movements of varying durations obtained by different instructions and conditions: duration imposed by a tempo (Wadman et al 1979;Darling et al 1988;van der Wel et al 2009;Shmuelof et al 2012;Park et al 2017;Salmond et al 2017), duration imposed by velocity instructions (e.g. slow, natural, fast; Darling and Cole 1990;Messier et al 2003;Ambike and Schmiedeler 2013;Rand and Shimansky 2013), duration constrained by target size (Boyle and Shea 2011;Boyle et al 2012a,b;Michmizos and Krebs 2014).…”

Section: Introductionmentioning

confidence: 92%

“…2B). As in Park et al (2017), our participants were instructed to match the period of a metronome. In other studies, the participants tracked a "velocity"…”

Section: Task Designmentioning

confidence: 99%

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Experimental and theoretical study of velocity fluctuations during slow movements in humans

Guigon

Chafik

Jarrassé

et al. 2019

Journal of Neurophysiology

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Moving smoothly is generally considered as a higher-order goal of motor control and moving jerkily as a witness of clumsiness or pathology, yet many common and well-controlled movements (e.g., tracking movements) have irregular velocity profiles with widespread fluctuations. The origin and nature of these fluctuations have been associated with the operation of an intermittent process but in fact remain poorly understood. Here we studied velocity fluctuations during slow movements, using combined experimental and theoretical tools. We recorded arm movement trajectories in a group of healthy participants performing back-and-forth movements at different speeds, and we analyzed velocity profiles in terms of series of segments (portions of velocity between 2 minima). We found that most of the segments were smooth (i.e., corresponding to a biphasic acceleration) and had constant duration irrespective of movement speed and linearly increasing amplitude with movement speed. We accounted for these observations with an optimal feedback control model driven by a staircase goal position signal in the presence of sensory noise. Our study suggests that one and the same control process can explain the production of fast and slow movements, i.e., fast movements emerge from the immediate tracking of a global goal position and slow movements from the successive tracking of intermittently updated intermediate goal positions. NEW & NOTEWORTHY We show in experiments and modeling that slow movements could result from the brain tracking a sequence of via points regularly distributed in time and space. Accordingly, slow movements would differ from fast movement by the nature of the guidance and not by the nature of control. This result could help in understanding the origin and nature of slow and segmented movements frequently observed in brain disorders.

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Section: Task Designsupporting

confidence: 73%

Section: Introductionmentioning

confidence: 92%

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Experimental and theoretical study of velocity fluctuations during slow movements in humans

Guigon

Chafik

Jarrassé

et al. 2019

Journal of Neurophysiology

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“…Studies indicate that performing slow rhythmic motion is hard for humans, due to the organization of the system controlling movement, rather than musculature factors. For this reason, it might be very hard to overcome limitations in this area and show significant improvements 25 . As opposed to the target task, the analogy group performed better than the explicit group at this task in a low-frequency movement.…”

Section: Discussionmentioning

confidence: 99%

Analogies can speed up the motor learning process

Zacks

Friedman

2020

Sci Rep

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Analogies have been shown to improve motor learning in various tasks and settings. in this study we tested whether applying analogies can shorten the motor learning process and induce insight and skill improvement in tasks that usually demand many hours of practice. Kinematic measures were used to quantify participant's skill and learning dynamics. For this purpose, we used a drawing task, in which subjects drew lines to connect dots, and a mirror game, in which subjects tracked a moving stimulus. After establishing a baseline, subjects were given an analogy, explicit instructions or no further instruction. We compared their improvement in skill (quantified by coarticulation or smoothness), accuracy and movement duration. Subjects in the analogy and explicit groups improved their coarticulation in the target task, while significant differences were found in the mirror game only at a slow movement frequency between analogy and controls. We conclude that a verbal analogy can be a useful tool for rapidly changing motor kinematics and movement strategy in some circumstances, although in the tasks selected it did not produce better performance in most measurements than explicit guidance. Furthermore, we observed that different movement facets may improve independently from others, and may be selectively affected by verbal instructions. these results suggest an important role for the type of instruction in motor learning. Using analogies in the acquisition of a motor skill has been shown to improve motor learning in various tasks and settings, from throwing a basketball to performing a high jump, and in populations ranging from young children to older adults 1-4. Analogies in the case of motor learning combine various task-relevant rules into a single biomechanical metaphor, usually given to the learner as a verbal instruction. A survey study among researchers and practitioners 5 defined analogy learning as follows: "Learning facilitated by metaphors. The complex structure of the to-be-learned skill is integrated into a simple metaphor that the learner is provided with". Two examples of this method are given: "reach for an apple up in the tree" (for a jumping pattern) and "putting your hand into the cookie jar" (for a basketball shot). The use of analogies in motor learning has been shown to be beneficial both during the learning process and in maintaining performance under pressure 2,6. Most of the research in this field has been done under the implicit/explicit knowledge paradigm. Researchers have defined explicit motor learning as conscious control of a motor task. This type of knowledge structure relies on working memory and is characterized by learning rules that govern the movement and being able to explicitly state such rules. In contrast, implicit knowledge of a motor task doesn't necessitate conscious awareness of specific rules and isn't as demanding on working memory 7. Experiments show that explicit knowledge can hinder performance during the learning process, although this occurs mostly under stressful con...

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“…These representations are full-fledged models in the original definition. However, they can also be thought as specific patterns of motor actions necessary to produce a certain behavior (e.g., dynamic primitives [7,8] ).…”

Section: Introductionmentioning

confidence: 99%

Modeling Previous Trial Effect in Human Manipulation through Iterative Learning Control

Cenceschi

Santina

Averta

et al. 2020

Advanced Intelligent Systems

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In the execution of repetitive tasks, humans can capitalize on experience to improve their motor performance. Prominent examples of this ability can be recognized in our capacity of grasping and manipulating in uncertain conditions. With the aim of providing a mathematical description for such behavior, experiments are considered where participants are required to lift an object with an unexpected mass distribution. By repeating multiple times the same lifting action, participants can learn the correct motor command for task accomplishment. Three models are proposed that combine reactive terms and a learned anticipatory action to explain experimental data. The models feature intratrial and intertrial memory, and the effect of slowly and fast adaptive sensory receptors. The architectures’ effectiveness in explaining experimental data is compared with a general‐purpose state of the art model. The proposed algorithms conspicuously outperform the state of the art in all the considered validation routines. Global and within‐trial human behavior is predicted with 88% of accuracy in nominal conditions. When the object's center of mass is moved, the accuracy is maintained up to 83%. Finally, convergence properties of proposed algorithms are analytically discussed, and their stability and robustness against measurement noise are evaluated in simulation.

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Moving slowly is hard for humans: limitations of dynamic primitives

Cited by 50 publications

References 71 publications

Experimental and theoretical study of velocity fluctuations during slow movements in humans

Experimental and theoretical study of velocity fluctuations during slow movements in humans

Analogies can speed up the motor learning process

Modeling Previous Trial Effect in Human Manipulation through Iterative Learning Control

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