Effect of Handedness on Learned Controllers and Sensorimotor Noise During Trajectory-Tracking

Yamagami, Momona; Peterson, Lauren N.; Howell, Darrin; Roth, Eatai; Burden, Samuel A.

doi:10.1101/2020.08.01.232454

Cited by 4 publications

(2 citation statements)

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“…But at high frequencies, the target may move too fast for feedback control to be exerted, leaving only inappropriate feedforward responses. It is not possible to dissociate the contributions of feedforward and feedback control on the basis of our current dataset, but in principle our approach can be extended to do so by including perturbations to the cursor position in addition to target movement ( Yamagami et al, 2019 ; Yamagami et al, 2020 ).…”

Section: Discussionmentioning

confidence: 99%

“…Our characterization of learning made use of frequency-based system identification, a powerful tool that has been previously used to study biological motor control such as insect flight (Fuller et al, 2014;Sponberg et al, 2015;Roth et al, 2016), electric fish refuge tracking (Cowan and Fortune, 2007;Madhav et al, 2013), human posture (Oie et al, 2002;Kiemel et al, 2006), and human manual tracking (Yamagami et al, 2019(Yamagami et al, , 2020Zimmet et al, 2020). System identification and other sinusoidal perturbation techniques have previously been applied to characterize the trial-bytrial dynamics of learning from errors in adaptation tasks (Baddeley et al, 2003;Ueyama, 2017;Miyamoto et al, 2020).…”

Section: System Identification As a Tool For Characterizing Motor Learningmentioning

confidence: 99%

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De novo learning versus adaptation of continuous control in a manual tracking task

Yang

Cowan

Haith

2021

eLife

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How do people learn to perform tasks that require continuous adjustments of motor output, like riding a bicycle? People rely heavily on cognitive strategies when learning discrete movement tasks, but such time-consuming strategies are infeasible in continuous control tasks that demand rapid responses to ongoing sensory feedback. To understand how people can learn to perform such tasks without the benefit of cognitive strategies, we imposed a rotation/mirror reversal of visual feedback while participants performed a continuous tracking task. We analyzed behavior using a system identification approach which revealed two qualitatively different components of learning: adaptation of a baseline controller and formation of a new, task-specific continuous controller. These components exhibited different signatures in the frequency domain and were differentially engaged under the rotation/mirror reversal. Our results demonstrate that people can rapidly build a new continuous controller de novo and can simultaneously deploy this process with adaptation of an existing controller.

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Section: Discussionmentioning

confidence: 99%

Section: System Identification As a Tool For Characterizing Motor Learningmentioning

confidence: 99%

De novo learning versus adaptation of continuous control in a manual tracking task

Yang

Cowan

Haith

2021

eLife

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De novolearning versus adaptation of continuous control in a manual tracking task

Yang

Cowan

Haith³

2020

Preprint

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11Learning to perform feedback control is critical for learning many real-world tasks that involve continuous 12 control such as juggling or bike riding. However, most motor learning studies to date have investigated 13 how humans learn feedforward but not feedback control, making it unclear whether people can learn new 14 continuous feedback control policies. Using a manual tracking task, we explicitly examined whether people 15 could learn to counter either a 90 • visuomotor rotation or mirror-reversal using feedback control. We 16 analyzed participants' performance using a frequency domain system identification approach which revealed 17 two distinct components of learning: 1) adaptation of baseline control, which was present only under the 18 rotation, and 2) de novo learning of a continuous feedback control policy, which was present under both 19 rotation and mirror reversal. Our results demonstrate for the first time that people are capable of acquiring 20 a new, continuous feedback controller via de novo learning. 21 Introduction 22 Many real-world motor tasks require continuous feedback control in order to perform skillfully. For example, 23 when one is riding a bicycle, one must assess the environment and the state of the body to inform future 24 motor commands that will minimize the chance one will topple off the bicycle. But when one learns how 25 to ride a bicycle, does one learn a new feedback control policy to do so? Most studies investigate motor 26 learning within the context of discrete, point-to-point movements which can be executed by predominantly 27 using feedforward control. Therefore, while it may seem intuitive that one should be able to learn a new 28 feedback controller, little is known about whether this is actually the case. 29 Humans can learn to perform new motor tasks through a variety of learning processes [1]. One of the most 30 well-characterized mechanisms is adaptation, an error-driven learning process by which task performance is 31 improved by experiencing and subsequently reducing sensory prediction errors [2][3][4]. Adaptation is known 32 to support learning in a variety of laboratory settings including under imposed visuomotor rotations [5][6][7], 33 prism goggles [8,9], split-belt treadmills [10,11], and force fields [12,13]. However, adaptation is known to be 34 limited in the extent to which it can change behavior [6,[14][15][16], suggesting that it cannot account for learning 35 more complex motor skills. It has therefore been suggested that more complex skills must be learned by 36 building a new control policy from scratch, a process termed de novo learning ( Figure 1A) [17][18][19]. However, 37 the exact mechanisms and properties of de novo learning remain unclear. 38 A simple visuomotor perturbation that is thought to require de novo learning is a mirror reversal of 39 visual feedback. After having learned to compensate for mirror-reversed feedback, participants do not exhibit 40 2 Adaptation D e n o v o le a r n in g CP 1 u = f(x t ,t,θ) CP...

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Evaluating a Human/Machine Interface with Redundant Motor Modalities for Trajectory-Tracking

Chou

Yamagami

Burden

2022

Preprint

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In human/machine interfaces (HMI), humans can interact with dynamic machines through a variety of sensory and motor modalities. Redundant motor modalities are known to have advantages in both human sensorimotor control and human-computer interaction: motor redundancy in sensorimotor control provides abundant solutions to achieve tasks; and incorporating diverse features from different modalities has improved the performance of gesture-, movement-, and brain-controlled computer interfaces. Our objective is to investigate whether redundant motor modalities enhance performance for a continuous trajectory-tracking task. We designed a multimodal human/machine interface with combined manual (joystick) and muscle (surface electromyography, sEMG) inputs and evaluated its closed-loop performance for tracking trajectories through second-order machine dynamics. In a human subjects experiment with 15 participants, we found that the multimodal interface outperformed the manual-only interface while performing comparably to the muscle-only interface; and that the multimodal interface enabled users to coordinate sensorimotor noise in individual modalities to improve performance. Multimodal human/machine interfaces could be beneficial in systems that require stability and robustness against perturbations such as motor rehabilitation and robotic manipulation.

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Effect of Handedness on Learned Controllers and Sensorimotor Noise During Trajectory-Tracking

Cited by 4 publications

References 43 publications

De novo learning versus adaptation of continuous control in a manual tracking task

De novo learning versus adaptation of continuous control in a manual tracking task

De novolearning versus adaptation of continuous control in a manual tracking task

Evaluating a Human/Machine Interface with Redundant Motor Modalities for Trajectory-Tracking

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