Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Feedback control systems01:26

Feedback control systems

Feedback control systems are categorized in various ways based on their design, analysis, and signal types.
Linear feedback systems are theoretical models that simplify analysis and design. These systems operate under the principle that their output is directly proportional to their input within certain ranges. For instance, an amplifier in a control system behaves linearly as long as the input signal remains within a specific range. However, most physical systems exhibit inherent nonlinearity...
Effects of feedback01:24

Effects of feedback

Feedback in control systems plays a critical role in shaping various operational parameters, extending beyond simple error reduction to influence stability, bandwidth, gain, impedance, and sensitivity. Understanding these effects requires examining a basic feedback system characterized by defined input, output, error, and feedback signals.
Feedback significantly modifies the gain of a control system. The gain of a system without feedback is altered by a factor of one plus GH, where G represents...
Control Systems01:10

Control Systems

Control systems are everywhere in contemporary society, influencing diverse applications from aerospace to automated manufacturing. These systems can be found naturally within biological processes, such as blood sugar regulation and heart rate adjustment in response to stress, as well as in man-made systems like elevators and automated vehicles. A control system is essentially a network of subsystems and processes that collaboratively convert specific inputs into desired outputs.
At the heart...
Open and closed-loop control systems01:17

Open and closed-loop control systems

Control systems are foundational elements in automation and engineering. They are broadly categorized into open-loop and closed-loop systems. These classifications hinge on the presence or absence of feedback mechanisms, significantly influencing the system's performance, complexity, and application.
An open-loop control system operates without feedback from the output. It consists of two primary elements: the controller and the controlled process. The controller receives an input signal and...
Feedback Loops01:01

Feedback Loops

In most cases, excessive hormone production is prevented by negative feedback—a loop that starts with a stimulus inducing the release of a particular substance, like a hormone, to maintain a certain level before triggering a signal that results in a decrease in further release of the hormone.
Controller Configurations01:22

Controller Configurations

Controller configurations are crucial in a car's cruise control system because they manage speed over time to maintain a consistent pace regardless of road conditions, thereby meeting design goals. In traditional control systems, fixed-configuration design involves predetermined controller placement. System performance modifications are known as compensation.
Control-system compensation involves various configurations, most commonly series or cascade compensation, in which the controller aligns...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Creative foraging and the explore-exploit trade-off in knowledge networks.

Cognition·2026
Same author

Functional ultrasound imaging combined with machine learning for whole-brain analysis of drug-induced hemodynamic changes.

Imaging neuroscience (Cambridge, Mass.)·2025
Same author

Human spinal cord activation during filling and emptying of the bladder.

Nature communications·2025
Same author

Exploring the Feasibility of Bidirectional Spinal Cord Machine Interface Through Sensing and Stimulation of Axonal Bundles.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society·2025
Same author

Information-Theoretic Neural Decoding Reproduces Several Laws of Human Behavior.

Open mind : discoveries in cognitive science·2023
Same author

Decoding Depression Severity From Intracranial Neural Activity.

Biological psychiatry·2023
Same journal

A Model-Free Reinforcement Learning Implementation of Decision Making Under Uncertainty by Sequential Sampling.

Neural computation·2026
Same journal

DROP: Distributional and Regular Optimism and Pessimism for Reinforcement Learning.

Neural computation·2026
Same journal

Hierarchical Active Inference Using Successor Representations.

Neural computation·2026
Same journal

W-Kernel and Its Principal Space for Frequentist Evaluation of Bayesian Estimators.

Neural computation·2026
Same journal

A Hidden Markov Model-Inspired Sequence Classification Method for Hyperdimensional Computing.

Neural computation·2026
Same journal

Sparse Graphical Modeling for Electrophysiological Phase-Based Connectivity Using Circular Statistics.

Neural computation·2026
See all related articles

Related Experiment Video

Updated: May 31, 2026

Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping
09:41

Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping

Published on: April 21, 2023

An optimal feedback control framework for grasping objects with position uncertainty.

Vassilios N Christopoulos1, Paul R Schrater

  • 1Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN 55455, USA. vchristo@cs.umn.edu

Neural Computation
|July 8, 2011
PubMed
Summary
This summary is machine-generated.

Humans compensate for uncertainty during grasping by adjusting motor control strategies. This study shows that stochastic optimal feedback control explains these human behaviors and individual differences in compensation.

More Related Videos

Force and Position Control in Humans - The Role of Augmented Feedback
06:31

Force and Position Control in Humans - The Role of Augmented Feedback

Published on: June 19, 2016

Design and Use of an Apparatus for Presenting Graspable Objects in 3D Workspace
09:11

Design and Use of an Apparatus for Presenting Graspable Objects in 3D Workspace

Published on: August 8, 2019

Related Experiment Videos

Last Updated: May 31, 2026

Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping
09:41

Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping

Published on: April 21, 2023

Force and Position Control in Humans - The Role of Augmented Feedback
06:31

Force and Position Control in Humans - The Role of Augmented Feedback

Published on: June 19, 2016

Design and Use of an Apparatus for Presenting Graspable Objects in 3D Workspace
09:11

Design and Use of an Apparatus for Presenting Graspable Objects in 3D Workspace

Published on: August 8, 2019

Area of Science:

  • Neuroscience
  • Robotics
  • Motor Control

Background:

  • Human object interaction involves navigating uncertainty from noisy motor commands and sensory information.
  • The brain appears to represent and compensate for location uncertainty in motor control strategies.
  • Previous research identified specific compensation strategies for location uncertainty.

Purpose of the Study:

  • To develop a stochastic optimal feedback control (SOFC) model for evaluating human grasping strategies.
  • To determine if human grasping compensation aligns with SOFC principles.
  • To explain individual differences in grasping based on control costs.

Main Methods:

  • Developed a SOFC model tailored for grasping tasks.
  • Conducted simulation experiments to analyze model properties.
  • Compared model predictions with observed human grasping behaviors.

Main Results:

  • The SOFC model successfully explains key aspects of human uncertainty compensation strategies in grasping.
  • The model accounts for individual differences in compensation based on the balance between performance benefits and control costs.
  • Demonstrated that compensation is optimized when performance gains outweigh increased control costs.

Conclusions:

  • SOFC provides a viable framework for understanding uncertainty compensation in complex natural tasks like grasping.
  • The model offers insights into the neural mechanisms underlying adaptive motor control.
  • Highlights the role of cost-benefit analysis in motor planning and execution.