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

Sampling Theorem01:15

Sampling Theorem

In signal processing, the analysis of continuous-time signals, denoted as x(t), often involves sampling techniques to convert these signals into discrete-time signals. This process is essential for digital representation and manipulation. A critical component in sampling is the train of impulses, characterized by the sampling interval and the sampling frequency. The relationship between these parameters and the original signal's properties dictates the success of the sampling process.
Parallel Processing01:20

Parallel Processing

The brain processes sensory information rapidly due to parallel processing, which involves sending data across multiple neural pathways at the same time. This method allows the brain to manage various sensory qualities, such as shapes, colors, movements, and locations, all concurrently. For instance, when observing a forest landscape, the brain simultaneously processes the movement of leaves, the shapes of trees, the depth between them, and the various shades of green. This enables a quick and...

You might also read

Related Articles

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

Sort by
Same author

Probing the Underlying Mechanisms of Spectro-Temporal Modulation Discrimination.

Trends in hearing·2026
Same author

Perceptual Processes as Charting Operators.

Neural computation·2026
Same author

Reverse correlation of natural statistics for ecologically relevant characterization of human perceptual templates.

Journal of neurophysiology·2025
Same author

State-dependent dynamics of cuttlefish mantle activity.

The Journal of experimental biology·2024
Same author

Human sensory adaptation to the ecological structure of environmental statistics.

Journal of vision·2024
Same author

Deep networks may capture biological behavior for shallow, but not deep, empirical characterizations.

Neural networks : the official journal of the International Neural Network Society·2022
Same journal

Topological dependence of viral mutation spread in complex host-interaction networks.

Chaos (Woodbury, N.Y.)·2026
Same journal

Multifractal signatures of Hamiltonian chaos in Hyperion's rotational dynamics.

Chaos (Woodbury, N.Y.)·2026
Same journal

Exploring mechanisms for reversal of flow in tunicate hearts.

Chaos (Woodbury, N.Y.)·2026
Same journal

State estimation in spatiotemporal chaos via low-rank StatFEM.

Chaos (Woodbury, N.Y.)·2026
Same journal

Universal response functions in driven dissipative tunneling dynamics.

Chaos (Woodbury, N.Y.)·2026
Same journal

A network-based approach to characterize the dynamics of the coupling field of thermoacoustic oscillators in annular geometry.

Chaos (Woodbury, N.Y.)·2026
See all related articles

Related Experiment Video

Updated: Jun 5, 2026

A System for Tracking the Dynamics of Social Preference Behavior in Small Rodents
08:38

A System for Tracking the Dynamics of Social Preference Behavior in Small Rodents

Published on: November 21, 2019

Stochastic characterization of small-scale algorithms for human sensory processing.

Peter Neri1

  • 1Aberdeen Medical School, Institute of Medical Sciences, Aberdeen, Scotland AB25 2ZD, United Kingdom. peter.neri@abdn.ac.uk

Chaos (Woodbury, N.Y.)
|January 5, 2011
PubMed
Summary
This summary is machine-generated.

Understanding human sensory processing requires characterizing the mapping from stimuli to decisions. This study uses a reverse correlation technique to analyze how target signals and decision-making affect system identification, improving functional approximations.

More Related Videos

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions
07:34

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions

Published on: March 25, 2014

New Methods to Study Gustatory Coding
10:59

New Methods to Study Gustatory Coding

Published on: June 29, 2017

Related Experiment Videos

Last Updated: Jun 5, 2026

A System for Tracking the Dynamics of Social Preference Behavior in Small Rodents
08:38

A System for Tracking the Dynamics of Social Preference Behavior in Small Rodents

Published on: November 21, 2019

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions
07:34

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions

Published on: March 25, 2014

New Methods to Study Gustatory Coding
10:59

New Methods to Study Gustatory Coding

Published on: June 29, 2017

Area of Science:

  • Cognitive Science
  • Neuroscience
  • Psychophysics

Background:

  • Human sensory processing involves a functional mapping from stimulus vectors (s) to internal decisional variables (r).
  • Direct measurement of decisional variables is not possible; only the resulting behavioral decisions can be observed.
  • Decisional transduction, the process of converting internal variables into observable decisions, complicates the characterization of sensory systems.

Purpose of the Study:

  • To explore a behavioral variant of reverse correlation techniques for characterizing human sensory processing.
  • To address challenges in system identification arising from decisional transducers and target signals.
  • To develop methods for accurate system characterization, including second-order functional approximations.

Main Methods:

  • Utilizing a behavioral reverse correlation approach with controlled noisy perturbations added to the input stimulus.
  • Analyzing the impact of a target signal, which can distort unbiased noise sources.
  • Investigating methods to handle distortions caused by both the decisional transducer and the target signal.

Main Results:

  • The study identifies challenges in system identification due to the nature of decisional transduction and the presence of target signals.
  • It proposes effective strategies for handling these challenges to achieve more accurate system characterizations.
  • The research facilitates extending functional approximations to second-order models, including small-scale cascade models.

Conclusions:

  • Accurate characterization of human sensory processing requires accounting for decisional transduction and target signal effects.
  • Behavioral reverse correlation offers a viable method for system identification in the presence of these complexities.
  • The developed techniques enable more precise functional approximations of sensory systems.