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

Causes of Similarity-Dissimilarity Effect01:26

Causes of Similarity-Dissimilarity Effect

262
The similarity-dissimilarity effect, a fundamental concept in social psychology, explains how interpersonal similarities and differences influence attraction and social interactions. This effect is supported by three key psychological perspectives: balance theory, social comparison theory, and consensual validation.Balance Theory and Cognitive ConsistencyBalance theory, developed by Fritz Heider, posits that individuals seek cognitive consistency in their relationships. When two people share...
262
Variation: Normal Distribution, Range, and Standard Deviation02:32

Variation: Normal Distribution, Range, and Standard Deviation

27.0K
In the field of psychology, there are several ways to organize measurements of a trait, feature, or characteristic (i.e., variables). Qualitative data, such as ethnicity, can be tabulated into a frequency count to provide information about the proportion, as well as the variety of groups in a sample or population. On the other hand, researchers can perform a wider set of calculations on quantitative data. The mean, mode, and median, for instance, are central tendency measures to identify a...
27.0K
What is Variation?01:14

What is Variation?

17.6K
Apart from the measures of central tendency, distribution, outliers, and the changing characteristics of data with time, an important characteristic of any data set is its variation or spread. In some data sets, the data values are concentrated closely near the mean; in others, the data values are more widely spread out from the mean.
The range, standard deviation, standard error, and variance are the different measures of variation.
Range: The range is the difference between its maximum and...
17.6K
Control Volume and System Representations01:16

Control Volume and System Representations

1.5K
Two key frameworks are employed to analyze mass, energy, and momentum transfer: the control volume approach and the system approach. These frameworks offer different perspectives, depending on whether the focus is on a specific region in space (control volume approach) or a defined mass of fluid (system approach).
The control volume approach considers a stationary region in space through which fluid flows. This region is bounded by a control surface.  For instance, in the case of water...
1.5K
State Space Representation01:27

State Space Representation

540
The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
Consider an RLC circuit, a...
540
Factors Influencing Attraction III: Similarity01:23

Factors Influencing Attraction III: Similarity

724
The similarity hypothesis suggests that individuals are more likely to form relationships with others who share similar attitudes, beliefs, values, and interests. This concept has been widely studied in social psychology, demonstrating that perceived similarity fosters interpersonal attraction. In an experiment supporting this hypothesis, participants were presented with fabricated information indicating that strangers held attitudes similar to their own. The results showed that participants...
724

You might also read

Related Articles

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

Sort by
Same author

Precision Functional Parcellation of the Human Cortex via Rest-Task fMRI Fusion.

bioRxiv : the preprint server for biology·2026
Same author

Online planning of sequential actions.

Trends in cognitive sciences·2026
Same author

Reading ability in both deaf and hearing adults is linked to neural representations of abstract phonology derived from visual speech.

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

Cerebellar growth is associated with domain-specific cerebral maturation and socio-linguistic behavior.

Nature communications·2026
Same author

Sociodemographic Variability in Pediatric Emergency Decisions by AI.

Pediatrics·2026
Same author

Sequence preparation is not always associated with a reaction time cost.

Journal of neurophysiology·2026
Same journal

Lifespan Trajectories of the Brain's Functional Complexity Characterized by Multiscale Sample Entropy.

NeuroImage·2026
Same journal

Pleasant fragrance modulates dyadic social sharing of positive emotion: Sharer-centered socioemotional enhancement effect and its neural couplings.

NeuroImage·2026
Same journal

Altered Functional Hierarchical and Sequential Organization in Individuals with Schizophrenia during Auditory Processing.

NeuroImage·2026
Same journal

Mechanical Deformation Explains Distinct Neuroimaging Patterns and Etiologies in Brain Trauma.

NeuroImage·2026
Same journal

Ventral striatum temporal interference brain stimulation enhances the reward-positivity event-related potential and reduces anxiety.

NeuroImage·2026
Same journal

NeuroHarm‑Kit: An Open‑Source Toolbox for Benchmarking Deep‑Learning Harmonization of Multi‑Site T1‑Weighted MRI.

NeuroImage·2026
See all related articles

Related Experiment Video

Updated: Jan 22, 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

2.9K

Variational representational similarity analysis.

Karl J Friston1, Jörn Diedrichsen2, Emma Holmes1

  • 1Wellcome Centre for Human Neuroimaging, Institute of Neurology, UCL, WC1N 3AR, UK.

Neuroimage
|July 1, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces a Bayesian approach to representational similarity analysis (RSA) and pattern component modeling (PCM). These methods quantify response patterns in neuroimaging data and identify consistent patterns across subjects.

Keywords:
BayesianMultivariatePattern component modellingRSARepresentational similarity analysisVariational

More Related Videos

Conscious and Non-conscious Representations of Emotional Faces in Asperger's Syndrome
08:31

Conscious and Non-conscious Representations of Emotional Faces in Asperger's Syndrome

Published on: July 31, 2016

14.4K
Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation
07:15

Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation

Published on: January 16, 2019

11.3K

Related Experiment Videos

Last Updated: Jan 22, 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

2.9K
Conscious and Non-conscious Representations of Emotional Faces in Asperger's Syndrome
08:31

Conscious and Non-conscious Representations of Emotional Faces in Asperger's Syndrome

Published on: July 31, 2016

14.4K
Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation
07:15

Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation

Published on: January 16, 2019

11.3K

Area of Science:

  • Neuroimaging
  • Cognitive Neuroscience
  • Machine Learning

Background:

  • Representational Similarity Analysis (RSA) and Pattern Component Modeling (PCM) are powerful techniques for analyzing neuroimaging data.
  • Existing methods may not fully leverage Bayesian inference for model comparison and parameter estimation.

Purpose of the Study:

  • To present a variational Bayesian implementation of RSA and PCM.
  • To frame RSA and PCM as Bayesian model comparison procedures.
  • To demonstrate the application of these methods for analyzing neuroimaging data within and between subjects.

Main Methods:

  • Utilized variational inference procedures to quantify pattern contributions to data.
  • Employed parametric empirical Bayes (PEB) for inferring subject-consistent patterns.
  • Applied Bayesian model comparison and selection for hypothesis testing at the between-subject level.
  • Demonstrated methods using a worked example in Statistical Parametric Mapping (SPM) software.

Main Results:

  • Quantified contributions of specific response patterns using second-order parameters (hyperparameters).
  • Inferred stimulus or condition-specific effects that are consistent across subjects.
  • Assessed evidence for different hypotheses regarding condition-specific effects using Bayesian model comparison.
  • Selected the best hypothesis explaining response patterns via Bayesian model selection.

Conclusions:

  • The proposed Bayesian framework provides a unified approach to RSA and PCM.
  • This implementation facilitates robust analysis of neuroimaging data, bridging univariate and multivariate methods.
  • Highlights the utility of Bayesian inference for understanding neural representations and condition-specific effects.