Trial reportProceedings of the National Academy of Sciences of the United States of America2021
Diversity-enabled sweet spots in layered architectures and speed-accuracy trade-offs in sensorimotor control.
Trial report in Proceedings of the National Academy of Sciences of the United States of America, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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The trial behind it
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Who cites it
13 citing papers in PubMed, 12 citations in OpenAlex.
- Diversity-enabled sweet spots in layered architectures and speed-accuracy trade-offs in sensorimotor control.Proceedings of the National Academy of Sciences of the United States of America · 2021Trial
- Effects of acute hypoxic exposure on multiple-object tracking ability of flight trainees.Frontiers in psychology · 2026Article
- Article
- Sensorimotor delays constrain robust locomotion in a 3D kinematic model of fly walking.bioRxiv : the preprint server for biology · 2025Article
- Optimal reaching subject to computational and physical constraints reveals structure of the sensorimotor control system.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Internal feedback in the cortical perception-action loop enables fast and accurate behavior.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Large Language Models and the Reverse Turing Test.Neural computation · 2023Article
- Article
- Layered feedback control overcomes performance trade-off in synthetic biomolecular networks.Nature communications · 2022Article
- Scaffolding layered control architectures through constraint closure: insights into brain evolution and development.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2022Article
- Why Do Big Data and Machine Learning Entail the Fractional Dynamics?Entropy (Basel, Switzerland) · 2021Article
- The unreasonable effectiveness of deep learning in artificial intelligence.Proceedings of the National Academy of Sciences of the United States of America · 2020Article
- Performance Limitations in Sensorimotor Control: Trade-Offs Between Neural Computation and Accuracy in Tracking Fast Movements.Neural computation · 2020Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.