Rapid visuomotor feedback gains are tuned to the task dynamics

Franklin, Sae; Wolpert, Daniel M.; Franklin, David W.

doi:10.1152/jn.00748.2016

Cited by 36 publications

(40 citation statements)

References 75 publications

(146 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…One might therefore imagine that the condition with the fastest initial movements (early-peak velocity) requires the largest initial forces and could therefore produce larger initial feedback intensities as a default. However, several studies have shown that there is no scaling of visuomotor feedback gains with background loads or muscle activity (Franklin et al (2012(Franklin et al ( , 2017). More critically, this condition actually shows the lowest feedback gains early in the movement, whereas the condition with the slowest initial acceleration produces the highest feedback gains.…”

Section: Discussionmentioning

confidence: 99%

“…We suggest that this limited sensitivity could be the outcome of the time-to-target model in action, providing simplified, yet still flexible control in the early phase of the visuomotor response. Such a system would then only need to be adapted as the dynamics or overall task goals change, allowing for fine tuning of the feedback gains according to changes in the environment (Franklin et al (2017)).…”

Section: Discussionmentioning

confidence: 99%

“…The hand movement was not constrained in these maintained perturbation trials. These maintained perturbations have now been used in several studies Franklin et al ( , 2017; de Brouwer et al 2017.…”

Section: Probe Trialsmentioning

confidence: 99%

“…From intercepting a basketball pass between opponents to catching a vase accidentally knocked off the shelf -visuomotor feedback responses play a familiar role in human motor behaviour. Previous research has extensively analysed these responses in human reaching movements (Day and Lyon (2000); Reichenbach et al (2014); de Brouwer et al (2017de Brouwer et al ( , 2018; Saunders and Knill (2003); Saunders (2004); Saunders and Knill (2005); Sarlegna et al (2003); Knill et al (2011)), and showed an interesting combination of task-dependent variability on the timescale of a single movement (Dimitriou et al (2013); Franklin et al (2014Franklin et al ( , 2017; Cross et al (2019)), as well as sub-voluntary feedback onset times (Prablanc and Martin (1992); Day and Lyon (2000); Franklin and Wolpert (2008); Zhang et al (2018); Oostwoud Wijdenes et al…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Time-to-Target Simplifies Optimal Control of Visuomotor Feedback Responses

Česonis

Franklin

2020

eNeuro

Self Cite

View full text Add to dashboard Cite

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Probe Trialsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Time-to-Target Simplifies Optimal Control of Visuomotor Feedback Responses

Česonis

Franklin

2020

eNeuro

Self Cite

View full text Add to dashboard Cite

show abstract

“…Angular position and velocity were digitized at 1 KHz and low-pass filtered with a third order, zero-phase lag, 20 Hz Butterworth filter. Differences of each participant's mean lateral velocity for left and right cursor jumps of the same magnitude (i.e., ϩ8 and Ϫ8 cm) was averaged in two epochs: 180 -230 and 230 -280 ms, which coincide with earlier studies (Franklin and Wolpert, 2008;Dimitriou et al, 2013;Franklin et al, 2017;de Brouwer et al, 2017). Reach endpoints were determined on a trial-by-trial basis by finding the first time point the hand speed fell Ͻ5% of its maximum.…”

Section: Discussionmentioning

confidence: 99%

Visual Feedback Processing of the Limb Involves Two Distinct Phases

et al. 2019

View full text Add to dashboard Cite

Muscle responses to mechanical disturbances exhibit two distinct phases: a response starting at ϳ20 ms that is fairly stereotyped, and a response starting at ϳ60 ms modulated by many behavioral contexts including goal-redundancy and environmental obstacles. Muscle responses to disturbances of visual feedback of the hand arise within ϳ90 ms. However, little is known whether these muscle responses are sensitive to behavioral contexts. We had 49 human participants (27 male) execute goal-directed reaches with visual feedback of their hand presented as a cursor. On random trials, the cursor jumped laterally to the reach direction, and thus, required a correction to attain the goal. The first experiment demonstrated that the response amplitude starting at 90 ms scaled with jump magnitude, but only for jumps Ͻ2 cm. For larger jumps, the duration of the muscle response scaled with the jump size starting after 120 ms. The second experiment demonstrated that the early response was sensitive to goal redundancy as wider targets evoked a smaller corrective response. The third experiment demonstrated that the early response did not consider the presence of obstacles, as this response routinely drove participants directly to the goal even though this path was blocked by an obstacle. Instead, the appropriate muscle response to navigate around the obstacle started after 120 ms. Our findings highlight that visual feedback of the limb involves two distinct phases: a response starting at 90 ms with limited sensitivity to jump magnitude and sensitive to goal-redundancy, and a response starting at 120 ms with increased sensitivity to jump magnitude and environmental factors.

show abstract

Behavioral Motor Performance

Leib,

Howard,

Millard

et al. 2023

Comprehensive Physiology

View full text Add to dashboard Cite

The human sensorimotor control system has exceptional abilities to perform skillful actions. We easily switch between strenuous tasks that involve brute force, such as lifting a heavy sewing machine, and delicate movements such as threading a needle in the same machine. Using a structure with different control architectures, the motor system is capable of updating its ability to perform through our daily interaction with the fluctuating environment. However, there are issues that make this a difficult computational problem for the brain to solve. The brain needs to control a nonlinear, nonstationary neuromuscular system, with redundant and occasionally undesired degrees of freedom, in an uncertain environment using a body in which information transmission is subject to delays and noise. To gain insight into the mechanisms of motor control, here we survey movement laws and invariances that shape our everyday motion. We then examine the major solutions to each of these problems in the three parts of the sensorimotor control system, sensing, planning, and acting. We focus on how the sensory system, the control architectures, and the structure and operation of the muscles serve as complementary mechanisms to overcome deviations and disturbances to motor behavior and give rise to skillful motor performance. We conclude with possible future research directions based on suggested links between the operation of the sensorimotor system across the movement stages. © 2024 American Physiological Society. Compr Physiol 14:5179‐5224, 2024.

show abstract

Rapid visuomotor feedback gains are tuned to the task dynamics

Cited by 36 publications

References 75 publications

Time-to-Target Simplifies Optimal Control of Visuomotor Feedback Responses

Time-to-Target Simplifies Optimal Control of Visuomotor Feedback Responses

Visual Feedback Processing of the Limb Involves Two Distinct Phases

Behavioral Motor Performance

Contact Info

Product

Resources

About