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

Standard Error of the Mean01:13

Standard Error of the Mean

13.1K
The sampling variability of a statistic is defined as how much the statistic varies from one sample to another. The sampling variability of a statistic is typically measured by measuring its standard error.
The standard error of the mean is an example of a standard error. It is a unique standard deviation known as the standard deviation of the sampling distribution of the mean. The standard error of the mean is a statistic that calculates how correctly a sample distribution represents a...
13.1K
Uncertainty in Measurement: Accuracy and Precision03:37

Uncertainty in Measurement: Accuracy and Precision

113.7K
Scientists typically make repeated measurements of a quantity to ensure the quality of their findings and to evaluate both the precision and the accuracy of their results. Measurements are said to be precise if they yield very similar results when repeated in the same manner. A measurement is considered accurate if it yields a result that is very close to the true or the accepted value. Precise values agree with each other; accurate values agree with a true value. 
113.7K
Margin of Error01:27

Margin of Error

8.1K
The margin of error is also called the maximum error of an estimate. The margin of error is the maximum possible or expected difference between the observed sample parameter value and the actual population parameter value. For proportion, it is the maximum difference between the value of sample proportion obtained from the data and the true value of population proportion. As the true value of the population parameter is not known, the margin of error is calculated using the sample statistic.
8.1K
Random and Systematic Errors01:20

Random and Systematic Errors

16.2K
Scientists always try their best to record measurements with the utmost accuracy and precision. However, sometimes errors do occur. These errors can be random or systematic. Random errors are observed due to the inconsistency or fluctuation in the measurement process, or variations in the quantity itself that is being measured. Such errors fluctuate from being greater than or less than the true value in repeated measurements. Consider a scientist measuring the length of an earthworm using a...
16.2K
Random and Systematic Errors01:20

Random and Systematic Errors

946
946
Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

1.6K
The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...
1.6K

You might also read

Related Articles

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

Sort by
Same author

Deltas' and Spindles' Cross-Area Synchronization and Ripple Subtypes.

Sleep·2026
Same author

A Qualitative Exploration of EMG Visual Feedback for Spinal Cord Injury Rehabilitation.

IEEE ... International Conference on Rehabilitation Robotics : [proceedings]·2025
Same author

Differential contributions of CA3 and entorhinal cortex inputs to ripple patterns in the hippocampus.

iScience·2025
Same author

Generalizing Upper Limb Force Modeling With Transfer Learning: A Multimodal Approach Using EMG and IMU for New Users and Conditions.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society·2024
Same author

CBD lengthens sleep but shortens ripples and leads to intact simple but worse cumulative memory.

iScience·2023
Same author

Increased cortical plasticity leads to memory interference and enhanced hippocampal-cortical interactions.

eLife·2023

Related Experiment Video

Updated: Apr 4, 2026

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

3.1K

Do Cost Functions for Tracking Error Generalize across Tasks with Different Noise Levels?

Jonathon Sensinger1, Adrian Aleman-Zapata2, Kevin Englehart1

  • 1Institute of Biomedical Engineering, University of New Brunswick, Fredericton, New Brunswick, Canada; Department of Electrical and Computer Engineering, University of New Brunswick, Fredericton, New Brunswick, Canada.

Plos One
|August 28, 2015
PubMed
Summary
This summary is machine-generated.

Human control of interfaces is modeled by cost functions. A power function, not quadratic, best explains user error costs in myoelectric control tasks, showing consistency across different scales.

More Related Videos

Utilizing vmTracking to Improve the Accuracy of Multi-Animal Pose Estimation in Rodent Social Behavior Studies
07:34

Utilizing vmTracking to Improve the Accuracy of Multi-Animal Pose Estimation in Rodent Social Behavior Studies

Published on: November 7, 2025

481

Related Experiment Videos

Last Updated: Apr 4, 2026

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

3.1K
Utilizing vmTracking to Improve the Accuracy of Multi-Animal Pose Estimation in Rodent Social Behavior Studies
07:34

Utilizing vmTracking to Improve the Accuracy of Multi-Animal Pose Estimation in Rodent Social Behavior Studies

Published on: November 7, 2025

481

Area of Science:

  • Human-Computer Interaction
  • Computational Neuroscience
  • Robotics

Background:

  • Human control of interfaces is often modeled using computational control models and quadratic cost functions for mathematical simplicity.
  • Previous research suggests human cost functions deviate from quadratic forms in reaching tasks, but the specific functions and their generalizability remain unclear.

Purpose of the Study:

  • To identify the cost function that best explains human behavior in controlling a myoelectric interface.
  • To determine if identified cost functions generalize across tasks of similar nature but different scales.

Main Methods:

  • Employed an inverse-decision-theory technique to reconstruct cost functions from empirical data.
  • Collected data from 24 able-bodied subjects controlling a myoelectric interface with a velocity-mapped cursor.
  • Compared linear-quadratic, inverted Gaussian, power function, and quadratic cost functions against subject behavior.

Main Results:

  • A power function (cost ∝ errorα) with α = 1.69, along with linear-quadratic and inverted Gaussian functions, significantly outperformed the quadratic cost function (p<0.05).
  • The power function's parameter value was consistent with previous studies, despite differences in the myoelectric control paradigm, noise levels, and task scale.

Conclusions:

  • Human error cost in myoelectric control tasks is better represented by a power function than a quadratic function.
  • The power function demonstrates generalizability across varying noise amplitudes and task scales for pointing and tracking tasks.