scholarly journals High-fidelity Musculoskeletal Modeling Reveals a Motor Planning Contribution to the Speed-Accuracy Tradeoff

2019 ◽  
Author(s):  
Mazen Al Borno ◽  
Saurabh Vyas ◽  
Krishna V. Shenoy ◽  
Scott L. Delp
Keyword(s):  
Optimal Control ◽  
Optimal Control Theory ◽  
Three Dimensional ◽  
Motor Planning ◽  
Reaching Movements ◽  
High Fidelity ◽  
Movement Duration ◽  
Fitts Law ◽  
Speed Accuracy ◽  
Accuracy Tradeoff

AbstractThe speed-accuracy tradeoff is a fundamental aspect of goal-directed motor behavior, empirically formalized by Fitts’ law, which relates movement duration to movement distance and target width. Here, we introduce a computational model of three-dimensional upper extremity movements that reproduces well-known features of reaching movements and is more biomechanically realistic than previous models. Critically, these features arise without the need of signal-dependent noise. We analyzed motor cortical neural activity from monkeys reaching to targets of different sizes. We found that the contribution of preparatory neural states to movement duration variability was greater for smaller targets than larger targets, and that movements to smaller targets exhibited less variability in preparatory neural states, but greater movement duration variability. Taken together, these results suggest that Fitts’ law emerges from greater task demands constraining the optimization landscape in a fashion that reduces the number of “good” control solutions (i.e., faster reaches). Thus, the speed-accuracy tradeoff could be a consequence of motor planning variability and optimal control theory, and not exclusively signal-dependent noise, as is currently held.Significance StatementA long-standing challenge in motor neuroscience is to understand the relationship between movement speed and accuracy, known as the speed-accuracy tradeoff. We introduce a computational model of reaching movements based on optimal control theory using a realistic model of musculoskeletal dynamics. The model synthesizes three-dimensional point-to-point reaching movements that reproduce kinematics features reported in motor control studies. Such high-fidelity modeling reveals that the speed-accuracy tradeoff as described by Fitts’ law emerges even without the presence of motor noise, which is commonly believed to underlie the speed-accuracy tradeoff. This suggests an alternative theory based on suboptimal control solutions. The crux of this theory is that some features of human movement are attributable to planning variability rather than execution noise.

scholarly journals High-fidelity musculoskeletal modeling reveals that motor planning variability contributes to the speed-accuracy tradeoff

eLife ◽  
2020 ◽  
Vol 9 ◽  
Author(s):  
Mazen Al Borno ◽  
Saurabh Vyas ◽  
Krishna V Shenoy ◽  
Scott L Delp
Keyword(s):  
Neural Activity ◽  
Three Dimensional ◽  
Motor Planning ◽  
Good Control ◽  
Population Level ◽  
Task Demands ◽  
Movement Speed ◽  
Fitts Law ◽  
Speed Accuracy ◽  
Accuracy Tradeoff

A long-standing challenge in motor neuroscience is to understand the relationship between movement speed and accuracy, known as the speed-accuracy tradeoff. Here, we introduce a biomechanically realistic computational model of three-dimensional upper extremity movements that reproduces well-known features of reaching movements. This model revealed that the speed-accuracy tradeoff, as described by Fitts’ law, emerges even without the presence of motor noise, which is commonly believed to underlie the speed-accuracy tradeoff. Next, we analyzed motor cortical neural activity from monkeys reaching to targets of different sizes. We found that the contribution of preparatory neural activity to movement duration (MD) variability is greater for smaller targets than larger targets, and that movements to smaller targets exhibit less variability in population-level preparatory activity, but greater MD variability. These results propose a new theory underlying the speed-accuracy tradeoff: Fitts’ law emerges from greater task demands constraining the optimization landscape in a fashion that reduces the number of ‘good’ control solutions (i.e., faster reaches). Thus, contrary to current beliefs, the speed-accuracy tradeoff could be a consequence of motor planning variability and not exclusively signal-dependent noise.

scholarly journals Prolonged response time helps eliminate residual errors in visuomotor adaptation

2021 ◽  
Author(s):  
Lisa Langsdorf ◽  
Jana Maresch ◽  
Mathias Hegele ◽  
Samuel D. McDougle ◽  
Raphael Schween
Keyword(s):  
Cognitive Strategies ◽  
Motor Planning ◽  
Visuomotor Adaptation ◽  
Movement Planning ◽  
Planning Time ◽  
Full Compensation ◽  
Speed Accuracy ◽  
Residual Errors ◽  
Prolonged Response ◽  
Accuracy Tradeoff

AbstractOne persistent curiosity in visuomotor adaptation tasks is that participants often do not reach maximal performance. This incomplete asymptote has been explained as a consequence of obligatory computations within the implicit adaptation system, such as an equilibrium between learning and forgetting. A body of recent work has shown that in standard adaptation tasks, cognitive strategies operate alongside implicit learning. We reasoned that incomplete learning in adaptation tasks may primarily reflect a speed-accuracy tradeoff on time-consuming motor planning. Across three experiments, we find evidence supporting this hypothesis, showing that hastened motor planning may primarily lead to under-compensation. When an obligatory waiting period was administered before movement start, participants were able to fully counteract imposed perturbations (Experiment 1). Inserting the same delay between trials – rather than during movement planning – did not induce full compensation, suggesting that the motor planning interval influences the learning asymptote (Experiment 2). In the last experiment (Experiment 3), we asked participants to continuously report their movement intent. We show that emphasizing explicit re-aiming strategies (and concomitantly increasing planning time) also lead to complete asymptotic learning. Findings from all experiments support the hypothesis that incomplete adaptation is, in part, the result of an intrinsic speed-accuracy tradeoff, perhaps related to cognitive strategies that require parametric attentional reorienting from the visual target to the goal.

Exploring speed–accuracy tradeoff in reaching movements: a neurocomputational model

2020 ◽  
Vol 32 (17) ◽  
pp. 13377-13403 ◽  
Author(s):  
Antonio Parziale ◽  
Rosa Senatore ◽  
Angelo Marcelli
Keyword(s):  
Reaching Movements ◽  
Speed Accuracy ◽  
Accuracy Tradeoff

Accuracy Characteristics of Throwing as a Result of Maximum Force Effort

1998 ◽  
Vol 86 (3_suppl) ◽  
pp. 1211-1217 ◽  
Author(s):  
Bruce R. Etnyre
Keyword(s):  
The Other ◽  
Average Error ◽  
Maximal Level ◽  
Maximal Force ◽  
Fitts Law ◽  
Variable Error ◽  
Throwing Accuracy ◽  
Dart Throwing ◽  
Speed Accuracy ◽  
Accuracy Tradeoff

Fitts' law predicts the accuracy of movement to a target decreases as the velocity of the movement increases. This speed-accuracy tradeoff has been examined under numerous conditions. During some tasks, however, increased force to nearly maximal level decreases the variability of the movement (Sherwood & Schmidt, 1980). This condition apparently produced results different from what would be predicted by Fitts' law. The purpose of the present study was to examine the effects of maximal force on dart throwing accuracy and variability. 54 subjects were categorized into groups based upon their experience with dart throwing: Advanced, Intermediate, or Beginners. Each subject performed two sessions of 15 trials. Subjects were instructed to “throw normally” for one session and “throw as hard as you can” for the other session. Distances from the target (triple-20 area) on the regulation dart board were measured and recorded after each of three darts was thrown. Average Error and Variable Error were calculated for each condition for each subject. The Average Error and Variable Error were greatest for the Beginner group and least for the Advanced group. For all three experience categories both Average Error and Variable Error were significantly greater when subjects performed with maximal force. The greater average error for the maximal force for all subjects suggested the speed-accuracy tradeoff applied to this aiming task. The greater variability in accuracy with maximal force suggested a ceiling effect, which reduced variability in previous studies, was not achieved.

scholarly journals Modelling 3D saccade generation by feedforward optimal control

2021 ◽  
Vol 17 (5) ◽  
pp. e1008975
Author(s):  
Akhil John ◽  
Carlos Aleluia ◽  
A. John Van Opstal ◽  
Alexandre Bernardino
Keyword(s):  
Optimal Control ◽  
Degrees Of Freedom ◽  
Main Sequence ◽  
Open Loop ◽  
Common Mechanism ◽  
Movement Duration ◽  
Listing's Law ◽  
Listing’S Law ◽  
Speed Accuracy ◽  
Saccade Generation

An interesting problem for the human saccadic eye-movement system is how to deal with the degrees-of-freedom problem: the six extra-ocular muscles provide three rotational degrees of freedom, while only two are needed to point gaze at any direction. Measurements show that 3D eye orientations during head-fixed saccades in far-viewing conditions lie in Listing’s plane (LP), in which the eye’s cyclotorsion is zero (Listing’s law, LL). Moreover, while saccades are executed as single-axis rotations around a stable eye-angular velocity axis, they follow straight trajectories in LP. Another distinctive saccade property is their nonlinear main-sequence dynamics: the affine relationship between saccade size and movement duration, and the saturation of peak velocity with amplitude. To explain all these properties, we developed a computational model, based on a simplified and upscaled robotic prototype of an eye with 3 degrees of freedom, driven by three independent motor commands, coupled to three antagonistic elastic muscle pairs. As the robotic prototype was not intended to faithfully mimic the detailed biomechanics of the human eye, we did not impose specific prior mechanical constraints on the ocular plant that could, by themselves, generate Listing’s law and the main-sequence. Instead, our goal was to study how these properties can emerge from the application of optimal control principles to simplified eye models. We performed a numerical linearization of the nonlinear system dynamics around the origin using system identification techniques, and developed open-loop controllers for 3D saccade generation. Applying optimal control to the simulated model, could reproduce both Listing’s law and and the main-sequence. We verified the contribution of different terms in the cost optimization functional to realistic 3D saccade behavior, and identified four essential terms: total energy expenditure by the motors, movement duration, gaze accuracy, and the total static force exerted by the muscles during fixation. Our findings suggest that Listing’s law, as well as the saccade dynamics and their trajectories, may all emerge from the same common mechanism that aims to optimize speed-accuracy trade-off for saccades, while minimizing the total muscle force during eccentric fixation.

Neuroticism and Speed-Accuracy Tradeoff in Self-Paced Speeded Mental Addition and Comparison

2010 ◽  
Vol 31 (3) ◽  
pp. 130-137 ◽  
Author(s):  
Hagen C. Flehmig ◽  
Michael B. Steinborn ◽  
Karl Westhoff ◽  
Robert Langner
Keyword(s):  
Reaction Time ◽  
Response Speed ◽  
Time Variability ◽  
Reaction Time Variability ◽  
Comparison Task ◽  
Processing Efficiency ◽  
Mental Addition ◽  
Speed Accuracy ◽  
The Relationship ◽  
Accuracy Tradeoff

Previous research suggests a relationship between neuroticism (N) and the speed-accuracy tradeoff in speeded performance: High-N individuals were observed performing less efficiently than low-N individuals and compensatorily overemphasizing response speed at the expense of accuracy. This study examined N-related performance differences in the serial mental addition and comparison task (SMACT) in 99 individuals, comparing several performance measures (i.e., response speed, accuracy, and variability), retest reliability, and practice effects. N was negatively correlated with mean reaction time but positively correlated with error percentage, indicating that high-N individuals tended to be faster but less accurate in their performance than low-N individuals. The strengthening of the relationship after practice demonstrated the reliability of the findings. There was, however, no relationship between N and distractibility (assessed via measures of reaction time variability). Our main findings are in line with the processing efficiency theory, extending the relationship between N and working style to sustained self-paced speeded mental addition.

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