Tracking Sensitivity
Tracking Sensitivity - Related Videos
10:10
Evaluating Tests of Cognition using a Computerized Touch-Sensitive Tablet, Eye Tracking, and Functional Magnetic Resonance Imaging
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2026
A protocol for simultaneous recording of visuomotor behavior and brain activity during standard paper-based cognitive tests using an MRI-compatible tablet and eye-tracking technology alongside functional MRI, towards improving the usage of such tests. Preliminary results are presented from a young healthy adult performing a Trail-Making test.
13:30
Mass-Sensitive Particle Tracking to Characterize Membrane-Associated Macromolecule Dynamics
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Cited by 3 •
2022
This protocol describes an iSCAT-based image processing and single-particle tracking approach that enables the simultaneous investigation of the molecular mass and the diffusive behavior of macromolecules interacting with lipid membranes. Step-by-step instructions for sample preparation, mass-to-contrast conversion, movie acquisition, and post-processing are provided alongside directions to prevent potential pitfalls.
06:07
Exploring Infant Sensitivity to Visual Language using Eye Tracking and the Preferential Looking Paradigm
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Cited by 7 •
2019
Eye tracking studies using a preferential looking paradigm can be used to study infants' emerging understanding of, and attention to, their external visual world.
06:19
Eye Tracking in Cognitive Experiments
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2023
Eye tracking as the name suggests involves tracking of eye-movements. It is a non-invasive, sensitive tool that quantifies and measures eye-movements to describe an individuals' cognitive state. An eye-movement between two fixation points is called a saccade, which is one of the fastest motor movements in our body. By observing the profiles of these eye movements, scientists can better understand neural deficits in patients with cognitive impairments. In this video, we will first look at an...
05:55
Multiple Object Tracking
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2023
Source: Laboratory of Jonathan Flombaum—Johns Hopkins University In a staggeringly complex and engaging world, it is crucial to selectively process some stimuli at the expense of others. Experimental psychologists call this ability attention. Specifically, visual attention refers to the ability to selectively process aspects of a visual scene. Many paradigms used to study visual attention involve brief, punctuated, and repeated trials. However, everyday situations often place sustained demands...
FAQs
Derivatives measure how tracking error changes when a model quantity changes by a small amount. A derivative with respect to a parameter indicates local influence, while derivatives with respect to incoming data show how data perturbations affect performance. Comparing these rates helps identify the quantities that most strongly control accuracy and provides a mathematical basis for sensitivity functions and perturbation analysis.
Poor conditioning means that relatively small changes in parameters, model assumptions, or incoming data can produce comparatively large changes in tracking error. This behavior signals that the model or tracking task may respond unreliably to uncertainty. Sensitivity analysis exposes such weaknesses, helping researchers distinguish well-behaved performance from responses that may indicate instability or numerical difficulty.
The analysis can vary model parameters and incoming data separately, then compare their effects on tracking error. Parameter perturbations reveal which characteristics of the model influence accuracy, whereas data perturbations show how strongly the system responds to changes in its input information. This distinction supports more precise interpretation and helps target improvements where they matter most.
A typical analysis first identifies the prescribed signal or trajectory and the quantities that may influence tracking. It then measures the corresponding tracking error, introduces controlled changes to parameters or incoming data, and compares the resulting errors. Derivatives, perturbation analysis, or sensitivity functions can express those comparisons and reveal the dominant sources of performance variation.
Tracking sensitivity is useful when a controller must follow a prescribed signal reliably despite changes in the model or incoming data. By showing which quantities have the greatest effect on tracking error, the analysis guides robust controller design. It also helps reveal poorly conditioned or unstable behavior before that behavior undermines tracking accuracy.
Sensitivity results indicate how strongly predictions or tracking outcomes depend on uncertain quantities. Numerical algorithms can use this information to identify poorly conditioned behavior and improve reliability, while model interpretation can focus attention on influential parameters or data. The same analysis supports optimization by showing which changes are most likely to improve accuracy or performance.