Contributions of feedforward and feedback control in a manual trajectory-tracking task

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

doi:10.1016/j.ifacol.2019.01.025

Cited by 9 publications

(31 citation statements)

References 12 publications

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“…We found small but statistically significant differences between the transformations T ud , T ur estimated using data from disturbance-only (0, d) and reference-only (r, 0) trials and the combined reference-and-disturbance (r, d) trials. Thus, the controllers implemented by our participants to control a second-order system do not satisfy the superposition principle (1), in contrast to our previous findings for first-order systems [4]; we attribute this difference to the increased difficulty of the trajectory-tracking task for a second-order system. Similarly to our previous findings for first-order systems [4], we found higher variability in transformation estimates at higher frequencies compared to lower frequencies.…”

Section: B Response To Reference and Disturbance (Approximately) Supcontrasting

confidence: 96%

“…Our results suggest that participants use both feedback and feedforward control to continuously track reference trajectories and reject disturbances, consistent with previous results for first-order [1], [4], [5], [7] and fourth-order [6], [25] systems, lending further support for Hypothesis 1.…”

Section: A Combined Feedback and Feedforward Improved Predictionsupporting

confidence: 90%

“…3,pg. 4] system M , humans learn to behave like an LTI system for a range of reference and disturbance signals [2], [4], [5]. Therefore, the human's control u produced in response to reference r and output y satisfies the law of superposition, H(r, y) = H(r, 0) + H(0, y).…”

Section: A Combined Feedback and Feedforward Improves Predictionmentioning

confidence: 99%

“…1) Hypothesis 1: Feedback B was estimated for each participant by applying (6a) to data from disturbance-only trials (condition (0, d)) and averaging across trials; similarly, feedforward F was estimated for each participant by applying (6b) to data from reference-only trials (condition (r, 0)), using B estimated from disturbance-only trials and averaging across trials. These controller estimates were used to predict user input u by applying (4) to data from disturbance-plusreference trials (condition (r, d)). The coefficient of determination R 2 [23, Sec.…”

Section: Data Analysesmentioning

confidence: 99%

“…2) Hypothesis 2: We computed frequency-domain representations of the transformation from disturbance d and reference r to input u ( T ud , T ur , respectively) at each stimulus frequency using (4). We performed a Wilcoxon signed-rank test with confidence threshold α = 0.05 to assess whether the magnitudes and phases of T ud and T ur from the (0, d) and (r, 0) trials were similar to those obtained from the (r, d) trials.…”

Section: Data Analysesmentioning

confidence: 99%

See 4 more Smart Citations

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

Yamagami

Peterson

Howell

et al. 2020

Preprint

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In human-in-the-loop control systems, operators can learn to manually control dynamic machines with either hand using a combination of reactive (feedback) and predictive (feedforward) control. This paper studies the effect of handedness on learned controllers and performance during a continuous trajectory-tracking task. In an experiment with 18 participants, subjects perform an assay of unimanual trajectory-tracking and disturbance-rejection tasks through second-order machine dynamics, first with one hand then the other. To assess how hand preference (or dominance) affects learned controllers, we extend, validate, and apply a non-parametric modeling method to estimate the concurrent feedback and feedforward elements of subjects' controllers. We find that handedness does not affect the learned controller and that controllers transfer between hands. Observed improvements in time-domain tracking performance may be attributed to adaptation of feedback to reject disturbances arising exogenously (i.e. applied by the experimenter) and endogenously (i.e. generated by sensorimotor noise)

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Section: B Response To Reference and Disturbance (Approximately) Supcontrasting

confidence: 96%

Section: A Combined Feedback and Feedforward Improved Predictionsupporting

confidence: 90%

Section: A Combined Feedback and Feedforward Improves Predictionmentioning

confidence: 99%

Section: Data Analysesmentioning

confidence: 99%

Section: Data Analysesmentioning

confidence: 99%

See 3 more Smart Citations

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

Yamagami

Peterson

Howell

et al. 2020

Preprint

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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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De novo motor learning of a bimanual control task over multiple days of practice

Haith

Yang

Pakpoor

et al. 2021

Preprint

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Although much research on motor learning has focused on how we adapt our movements to maintain performance in the face of imposed perturbations, in many cases we must learn new skills from scratch, or de novo. In comparison to adaptation, relatively little is known about de novo learning. In part, this is because learning a new skill can involve many challenges, including learning to recognize new patterns of sensory input and generate new patterns of motor output. However, even with familiar sensory cues and well-practiced movements, the problem of quickly selecting the appropriate actions in response to the current state is challenging. Here, we devised a bimanual hand-to-cursor mapping which isolates this control problem. We find that participants’ initially struggled to control the cursor under this bimanual mapping, despite explicit knowledge of the mapping. Performance improved steadily over multiple days of practice, however. Participants exhibited no aftereffects when reverting to a veridical cursor, confirming that participant’s learned the new task de novo, rather than through adaptation. Corrective responses to mid-movement perturbations of the target were initially weak, but, with practice participants gradually became able to respond rapidly and robustly to these perturbations. After four days of practice, participants’ behavior under the bimanual mapping almost matched performance with a veridically mapped cursor. However, there remained a small but persistent difference in performance level. Our findings illustrate the dynamics and limitations of learning a novel controller and introduce a promising paradigm for tractably investigating this aspect of motor skill learning.

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Contributions of feedforward and feedback control in a manual trajectory-tracking task

Cited by 9 publications

References 12 publications

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

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

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

De novo motor learning of a bimanual control task over multiple days of practice

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