WheelCon: A Wheel Control-Based Gaming Platform for Studying Human Sensorimotor Control

Nervous systems sense, communicate, compute, and actuate movement using distributed components with trade-offs in speed, accuracy, sparsity, noise, and saturation. Nevertheless, the resulting control can achieve remarkably fast, accurate, and robust performance due to a highly effective layered control architecture. However, there is no theory explaining the effectiveness of layered control architectures that connects speed-accuracy trade-offs (SATs) in neurophysiology to the resulting SATs in sensorimotor control. In this paper, we introduce a theoretical framework that provides a synthetic perspective to explain why there exists extreme diversity across layers and within levels. This framework characterizes how the sensorimotor control SATs are constrained by the hardware SATs of neurons communicating with spikes and their sensory and muscle endpoints, in both stochastic and deterministic models. The theoretical predictions of the model are experimentally confirmed using driving experiments in which the time delays and accuracy of the control input from the wheel are varied. These results show that the appropriate diversity in the properties of neurons and muscles across layers and within levels help create systems that are both fast and accurate despite being built from components that are individually slow or inaccurate. This novel concept, which we call "diversity-enabled sweet spots" (DESSs), explains the ubiquity of heterogeneity in the sizes of axons within a nerve as well the resulting superior performance of sensorimotor control.

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“…We developed a platform for a driving game that simulates riding a mountain bike (43). The platform is inexpensive and easy to implement.…”

Section: Methodsmentioning

confidence: 99%

Theoretical foundations for layered architectures and speed-accuracy tradeoffs in sensorimotor control

Nakahira

Liu

Bernat

et al. 2019

2019 American Control Conference (ACC)

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“…We used the WheelCon platform in the experiment (Fig. 2) 33 . All experiments involved in this study have institutional review board (IRB) approval from the California Institute of Technology, and all experiments were performed in accordance with relevant guidelines and regulations.…”

Section: Methodsmentioning

confidence: 99%

Diversity deconstrains component limitations in sensorimotor control

Nakahira

Liu

Sejnowski

et al. 2022

Preprint

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Human sensorimotor control is remarkably fast and accurate at the system level despite severe speed-accuracy trade-offs (SATs) at the component level. The discrepancy between the contrasting SATs at these two levels is a paradox. Here we describe reaching experiments in which sensorimotor delays and quantization errors were manipulated to test their effects on reaching speed and accuracy as a surrogate for manipulating the biophysical properties. A mechanistic model was developed for how component SATs constrain sensorimotor control that is consistent with the experimental results and with Fitts' Law for reaching. The model and experiments suggest that diversity among components deconstrains their individual limitations in sensorimotor control. Such "diversity-enabled sweet spots" (DESSs) are ubiquitous in both engineering and biology, explaining why large heterogeneities exist in the components of artificial and biological systems and how both engineers and natural selection routinely evolve into systems with fast and accurate responses using imperfect components.

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“…We developed a platform for biking games that simulates some aspects of riding a mountain bike (57). The platform is inexpensive and easy to implement.…”

Section: Methodsmentioning

confidence: 99%

Diversity-enabled sweet spots in layered architectures and speed–accuracy trade-offs in sensorimotor control

Nakahira

Liu

Sejnowski

et al. 2021

Proc. Natl. Acad. Sci. U.S.A.

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Nervous systems sense, communicate, compute, and actuate movement using distributed components with severe trade-offs in speed, accuracy, sparsity, noise, and saturation. Nevertheless, brains achieve remarkably fast, accurate, and robust control performance due to a highly effective layered control architecture. Here, we introduce a driving task to study how a mountain biker mitigates the immediate disturbance of trail bumps and responds to changes in trail direction. We manipulated the time delays and accuracy of the control input from the wheel as a surrogate for manipulating the characteristics of neurons in the control loop. The observed speed–accuracy trade-offs motivated a theoretical framework consisting of two layers of control loops—a fast, but inaccurate, reflexive layer that corrects for bumps and a slow, but accurate, planning layer that computes the trajectory to follow—each with components having diverse speeds and accuracies within each physical level, such as nerve bundles containing axons with a wide range of sizes. Our model explains why the errors from two control loops are additive and shows how the errors in each control loop can be decomposed into the errors caused by the limited speeds and accuracies of the components. These results demonstrate that an appropriate diversity in the properties of neurons across layers helps to create “diversity-enabled sweet spots,” so that both fast and accurate control is achieved using slow or inaccurate components.

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WheelCon: A Wheel Control-Based Gaming Platform for Studying Human Sensorimotor Control

Cited by 4 publications

References 31 publications

Theoretical foundations for layered architectures and speed-accuracy tradeoffs in sensorimotor control

Theoretical foundations for layered architectures and speed-accuracy tradeoffs in sensorimotor control

Diversity deconstrains component limitations in sensorimotor control

Diversity-enabled sweet spots in layered architectures and speed–accuracy trade-offs in sensorimotor control

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