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

Associative Learning

Associative Learning

Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...

Constraints and Statical Determinacy

Constraints and Statical Determinacy

In structural engineering, the equilibrium of a system is not only determined by its equations of equilibrium but also with the help of constraints. Constraints refer to restrictions on the motion of a system. The proper combinations of constraints can minimize the total number of constraints needed to maintain a system in mechanical equilibrium. When this happens, the system is said to be statically determinate. For such systems, the unknown reaction supports can be estimated using equilibrium...

Real-World Application of Classical Conditioning

Real-World Application of Classical Conditioning

Classical conditioning not only includes the initial pairing of stimuli but also extends to more complex forms, such as higher-order conditioning. Higher-order conditioning involves creating associations beyond the primary conditioned stimulus, resulting in a chain of conditioned responses.
Higher-order, or second-order, conditioning occurs when a neutral stimulus becomes associated with an already established conditioned stimulus through repeated pairings. For instance, if a dog has been...

EDTA: Conditional Formation Constant

EDTA: Conditional Formation Constant

Each EDTA molecule has six binding sites: four carboxyl groups and two amino groups. The fully protonated form of EDTA is represented as H6Y2+. However, it can exist in different forms, H5Y+, H4Y, H3Y−, H2Y2−, and HY3−, depending on the pH of the solution. In very basic solutions with pH > 10.17, the fully deprotonated form, Y4−, is the predominant species that readily complexes with metal ions in a 1:1 ratio.
For the equilibrium reaction of the metal with the Y4− form of EDTA, the formation...

Cause and Effect

Cause and Effect

While variables are sometimes correlated because one does cause the other, it could also be that some other factor, a confounding variable, is actually causing the systematic movement in our variables of interest. For instance, as sales in ice cream increase, so does the overall rate of crime. Is it possible that indulging in your favorite flavor of ice cream could send you on a crime spree? Or, after committing crime do you think you might decide to treat yourself to a cone?

Criteria for Causality: Bradford Hill Criteria - II

Criteria for Causality: Bradford Hill Criteria - II

The Bradford Hill criteria serve as guidelines for establishing causative links in epidemiological research. Beyond Strength, Consistency, Specificity, and Temporality, key criteria also include Biological Gradient, Plausibility, Coherence, Experiment, and Analogy. These principles assist scientists in assessing the likelihood of causation in complex biological contexts. Below is a summary of these concepts:

You might also read

Related Articles

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

Sort by

Same author

What is meant when we say we are clustering multimorbidity?

The lancet. Healthy longevity·2026

Same author

Human-AI Cooperation in Healthcare and Rehabilitation.

Delaware journal of public health·2026

Same author

Accelerated deficit accumulation in frailty and associations with adverse outcomes: a longitudinal population data analysis.

The lancet. Healthy longevity·2026

Same author

Hidden in visible light: spectral-temporal unmixing of lung tissue autofluorescence in a fibre-based system.

Biomedical optics express·2026

Same author

Prevalence and representation of comorbidities and multimorbidity in randomised controlled trials in sepsis or septic shock: a systematic review.

Critical care (London, England)·2026

Same author

Training sparse convolutional deep predictive coding networks with attention.

Neural networks : the official journal of the International Neural Network Society·2026

Same journal

A Model-Free Reinforcement Learning Implementation of Decision Making Under Uncertainty by Sequential Sampling.

Neural computation·2026

Same journal

DROP: Distributional and Regular Optimism and Pessimism for Reinforcement Learning.

Neural computation·2026

Same journal

Hierarchical Active Inference Using Successor Representations.

Neural computation·2026

Same journal

W-Kernel and Its Principal Space for Frequentist Evaluation of Bayesian Estimators.

Neural computation·2026

Same journal

A Hidden Markov Model-Inspired Sequence Classification Method for Hyperdimensional Computing.

Neural computation·2026

Same journal

Sparse Graphical Modeling for Electrophysiological Phase-Based Connectivity Using Circular Statistics.

Neural computation·2026

See all related articles

Search research articles

Related Experiment Video

Updated: May 24, 2026

Classical Short-Delay Eyeblink Conditioning in One-Year-Old Children

Classical Short-Delay Eyeblink Conditioning in One-Year-Old Children

Published on: September 1, 2018

Conditional association.

Sohan Seth¹, José C Príncipe

¹Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL 32608, USA. sohan@cnel.ufl.edu

Neural Computation

|March 21, 2012

Summary

This summary is machine-generated.

This study introduces a new, computationally simple method for estimating conditional dependence in neuroscientific networks. It offers a parameter-free approach using data realizations, improving upon complex existing techniques.

More Related Videos

Whisker-signaled Eyeblink Classical Conditioning in Head-fixed Mice

Whisker-signaled Eyeblink Classical Conditioning in Head-fixed Mice

Published on: March 30, 2016

Using Eye Movements Recorded in the Visual World Paradigm to Explore the Online Processing of Spoken Language

Using Eye Movements Recorded in the Visual World Paradigm to Explore the Online Processing of Spoken Language

Published on: October 13, 2018

Related Experiment Videos

Last Updated: May 24, 2026

Classical Short-Delay Eyeblink Conditioning in One-Year-Old Children

Classical Short-Delay Eyeblink Conditioning in One-Year-Old Children

Published on: September 1, 2018

Whisker-signaled Eyeblink Classical Conditioning in Head-fixed Mice

Whisker-signaled Eyeblink Classical Conditioning in Head-fixed Mice

Published on: March 30, 2016

Using Eye Movements Recorded in the Visual World Paradigm to Explore the Online Processing of Spoken Language

Using Eye Movements Recorded in the Visual World Paradigm to Explore the Online Processing of Spoken Language

Published on: October 13, 2018

Area of Science:

Neuroscience
Complex Systems Analysis
Information Theory

Background:

Estimating conditional dependence is crucial for understanding causal networks in neuroscience.
Current methods like conditional mutual information are computationally intensive and difficult to apply to real-world data.

Purpose of the Study:

To develop a computationally simple, parameter-free estimator for conditional dependence.
To provide a practical approach for analyzing neuroscientific network architectures.

Main Methods:

Introduced a novel 'conditional association' approach based on generalizing association to metric spaces.
Utilized a finite set of data realizations instead of theoretical random variables.
Developed an efficient surrogate data generation method for significance testing.

Main Results:

The proposed conditional association estimator is computationally efficient and parameter-free.
The method effectively estimates conditional dependence using practical data realizations.
Surrogate data generation provides a robust way to assess statistical significance.

Conclusions:

The novel conditional association method offers a practical and efficient alternative for analyzing neuroscientific networks.
This approach overcomes limitations of existing computationally expensive methods.
The technique facilitates a deeper understanding of causal architectures in distributed systems.