Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Deconvolution

Deconvolution

Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...

Imaging Biological Samples with Optical Microscopy

Imaging Biological Samples with Optical Microscopy

Optical microscopy uses optic principles to provide detailed images of samples. Antonie van Leeuwenhoek designed the first compound optical microscope in the 17th century to visualize blood cells, bacteria, and yeast cells. In 1830, Joseph Jackson Lister created an essentially modern light microscope. The 20th century saw the development of microscopes with enhanced magnification and resolution.
In optical microscopy, the specimen to be viewed is placed on a glass slide and clipped on the stage...

¹³C NMR: Distortionless Enhancement by Polarization Transfer (DEPT)

¹³C NMR: Distortionless Enhancement by Polarization Transfer (DEPT)

When proton-coupled carbon-13 spectra are simplified by a broadband proton decoupling technique, structural information about the coupled protons is lost. Distortionless enhancement by polarization transfer (DEPT) is a technique that provides information on the number of hydrogens attached to each carbon in a molecule. While the DEPT experiment utilizes complex pulse sequences, the pulse delay and flip angle are specifically manipulated. The resulting signals have different phases depending on...

You might also read

Related Articles

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

Sort by

Same author

Diagnostic Images for Mild Cognitive Impairment Are Sensitive to Alzheimer's Disease Biomarkers and Reveal Abnormal Scene Processing.

Journal of cognitive neuroscience·2026

Same author

Reverse engineering what makes a symbol memorable.

Proceedings of the National Academy of Sciences of the United States of America·2026

Same author

What divides and unites our memories: Multifactor trial-wise predictions of memory across 6+ million trials.

The American psychologist·2026

Same author

Semantic centrality and emotional valence contribute to word memorability in an associative memory task for Chinese words.

Scientific reports·2026

Same author

Correction: Scene image memorability is learnable via feedback.

Scientific reports·2026

Same author

Challenging dual-coding theory: Picture superiority effects persist in aphantasia.

Neuropsychologia·2026

Search research articles

Related Experiment Video

Updated: Mar 28, 2026

Transient Optical Clearing Using Absorbing Molecules for Ex Vivo and In Vivo Imaging

Transient Optical Clearing Using Absorbing Molecules for Ex Vivo and In Vivo Imaging

Published on: July 11, 2025

A toolbox and sample object perception data for equalization of natural images.

Wilma A Bainbridge¹, Aude Oliva²

¹The Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139, United States.

|December 23, 2015

Summary

Scientists can now precisely control visual stimuli using the Natural Image Statistical Toolbox. This tool aids psychologists and neuroscientists in avoiding confounds and analyzing object perception with a new set of characterized images.

Keywords:

Natural image statistics Object interaction envelope Object perception Spatial frequency

More Related Videos

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

Published on: February 6, 2020

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

Related Experiment Videos

Last Updated: Mar 28, 2026

Transient Optical Clearing Using Absorbing Molecules for Ex Vivo and In Vivo Imaging

Transient Optical Clearing Using Absorbing Molecules for Ex Vivo and In Vivo Imaging

Published on: July 11, 2025

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

Published on: February 6, 2020

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

Area of Science:

Psychology
Neuroscience
Computer Vision

Background:

Accurate stimulus control is crucial in psychology and neuroscience to prevent low-level visual features from confounding experimental results.
Existing methods may not adequately address the control of diverse visual properties in stimulus sets.

Purpose of the Study:

To introduce the Natural Image Statistical Toolbox for measuring, visualizing, and controlling low-level visual properties of stimuli.
To provide a characterized set of object images for testing the toolbox and for use in perception studies.

Main Methods:

Development of the Natural Image Statistical Toolbox for quantitative analysis of visual stimuli.
Creation of a novel object image dataset with variations in physical and interaction envelope size.
Utilizing the toolbox to analyze and control the provided stimulus set.

Main Results:

The Natural Image Statistical Toolbox enables precise measurement and control of low-level visual features in stimulus sets.
The provided object image set is characterized and suitable for testing the toolbox and for perception research.
The toolbox facilitates the creation of controlled experimental stimuli for psychological and neuroscientific investigations.

Conclusions:

The Natural Image Statistical Toolbox is a valuable resource for researchers needing to control visual stimulus properties.
The characterized object image set serves as a practical test-bed and a useful resource for perception studies.
This integrated approach enhances the rigor and reliability of visual perception research in psychology and neuroscience.