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

Behaviorism01:28

Behaviorism

The field of behaviorism was pioneered by figures such as Ivan Pavlov, John B. Watson, and B.F. Skinner fundamentally shifted the focus of psychology to the observable and controllable aspects of human and animal behavior. This shift marked a critical evolution in the discipline, emphasizing scientific rigor and experimental methodology.
The core premise of behaviorism is its focus on observable behavior rather than internal thoughts or feelings. This approach argues that true scientific...

You might also read

Related Articles

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

Sort by
Same author

Behavioral differences between humans and machines arise early in visual processing.

Journal of vision·2026
Same author

Estimating the contribution of early and late noise in vision from psychophysical data.

Journal of vision·2025
Same author

Plaid masking explained with input-dependent dendritic nonlinearities.

Scientific reports·2024
Same author

Standard models of spatial vision mispredict edge sensitivity at low spatial frequencies.

Vision research·2024
Same author

An objective measurement approach to quantify the perceived distortions of spectacle lenses.

Scientific reports·2024
Same author

Neither hype nor gloom do DNNs justice.

The Behavioral and brain sciences·2023
Same journal

High-resolution depth estimation for multiple wideband sources in deep sea via sparse Bayesian learninga).

The Journal of the Acoustical Society of America·2026
Same journal

Depression markers in speech: An approach based on tract variables dynamics.

The Journal of the Acoustical Society of America·2026
Same journal

The oyster toadfish (Opsanus tau) alters active and diurnal calling amid vessel noise in New York City.

The Journal of the Acoustical Society of America·2026
Same journal

Experimental noise characterisation of phase-locked tandem-rotor in edgewise flight.

The Journal of the Acoustical Society of America·2026
Same journal

The tune-text-temporal synergy: Prosodic effects of final segmental weakening in Neapolitan.

The Journal of the Acoustical Society of America·2026
Same journal

Monitoring vessel movement above critical offshore infrastructure using distributed acoustic sensing.

The Journal of the Acoustical Society of America·2026
See all related articles

Related Experiment Video

Updated: May 22, 2026

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

Sparse regularized regression identifies behaviorally-relevant stimulus features from psychophysical data.

Vinzenz H Schönfelder1, Felix A Wichmann

  • 1Department for Modeling of Cognitive Processes, Technical University Berlin, FR 6-4, Franklinstr 28/29, 10587 Berlin, Germany. vinzenz.schoenfelder@bccn-berlin.de

The Journal of the Acoustical Society of America
|May 8, 2012
PubMed
Summary
This summary is machine-generated.

This study introduces a machine learning method to identify key sensory cues influencing behavior. The L(1)-regularized logistic regression accurately determines which stimulus features drive decisions, even with complex data.

More Related Videos

Generating Strictly Controlled Stimuli for Figure Recognition Experiments
05:39

Generating Strictly Controlled Stimuli for Figure Recognition Experiments

Published on: March 18, 2019

Psychophysically-anchored, Robust Thresholding in Studying Pain-related Lateralization of Oscillatory Prestimulus Activity
07:28

Psychophysically-anchored, Robust Thresholding in Studying Pain-related Lateralization of Oscillatory Prestimulus Activity

Published on: January 21, 2017

Related Experiment Videos

Last Updated: May 22, 2026

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

Generating Strictly Controlled Stimuli for Figure Recognition Experiments
05:39

Generating Strictly Controlled Stimuli for Figure Recognition Experiments

Published on: March 18, 2019

Psychophysically-anchored, Robust Thresholding in Studying Pain-related Lateralization of Oscillatory Prestimulus Activity
07:28

Psychophysically-anchored, Robust Thresholding in Studying Pain-related Lateralization of Oscillatory Prestimulus Activity

Published on: January 21, 2017

Area of Science:

  • Cognitive Psychology
  • Machine Learning
  • Auditory Perception

Background:

  • Understanding how perceptual cues influence behavioral decisions is crucial for developing quantitative psychophysical models.
  • Traditional statistical methods often struggle to disentangle the effects of multiple, interdependent stimulus features on decision-making.
  • The need for robust analytical techniques that can handle complex, noisy behavioral data is paramount.

Purpose of the Study:

  • To demonstrate a novel method for analyzing stimulus-response data to determine the influence of specific perceptual cues on behavioral decisions.
  • To apply L(1)-regularized multiple logistic regression to identify essential stimulus features driving observer choices in a simulated auditory task.

Main Methods:

  • Utilized L(1)-regularized multiple logistic regression, a machine learning technique that promotes sparsity and prevents overfitting.
  • Generated simulated behavioral data from a classic auditory tone-in-noise detection task.
  • Analyzed stimulus-response data to estimate relative linear combination weights, reflecting the contribution of each stimulus feature.

Main Results:

  • The proposed method successfully identified critical observer cues from a large set of covarying stimulus features, outperforming standard regression techniques.
  • Accurate cue identification was achieved across a range of signal-to-noise ratios and for both deterministic and probabilistic observer models.
  • Reconstructed detailed decision rules by estimating linear model weights, enabling prediction of responses based on individual stimuli.

Conclusions:

  • L(1)-regularized logistic regression provides a powerful and accurate approach for dissecting the influence of perceptual cues in complex behavioral tasks.
  • This method offers a significant advancement for quantitative psychophysical modeling by precisely identifying the minimal set of relevant stimulus features.
  • The ability to reconstruct decision rules facilitates a deeper understanding of sensory processing and predictive modeling of behavior.