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Related Concept Videos

Criteria for Causality: Bradford Hill Criteria - II01:28

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:
Criteria for Causality: Bradford Hill Criteria - I01:30

Criteria for Causality: Bradford Hill Criteria - I

The Bradford Hill criteria are a group of principles that provide a framework to determine a causal relationship between a specific factor and a disease. There are nine criteria that are pivotal in assessing causality in epidemiological studies. Here's a closer look at Strength, Consistency, Specificity, and Temporality criteria with definitions and examples:
Correlation and Causation01:27

Correlation and Causation

Statistical tests can calculate whether there is a relationship, or correlation, between independent and dependent variables. An indirect relationship of the variables signifies a correlation, while a direct relationship shows causation. If it is determined that no connection exists between the variables, then the correlation is a coincidence.
Correlation versus Causation
If the dependent variable increases or decreases when the independent variable increases, there is a positive or negative...
Causality in Epidemiology01:21

Causality in Epidemiology

Causality or causation is a fundamental concept in epidemiology, vital for understanding the relationships between various factors and health outcomes. Despite its importance, there's no single, universally accepted definition of causality within the discipline. Drawing from a systematic review, causality in epidemiology encompasses several definitions, including production, necessary and sufficient, sufficient-component, counterfactual, and probabilistic models. Each has its strengths and...
Mechanistic Models: Overview of Compartment Models01:21

Mechanistic Models: Overview of Compartment Models

Mechanistic models, a category encompassing both physiological and compartmental modeling, differ from empirical models' approaches to incorporating known factors about the systems being modeled. Empirical models describe data with minimal assumptions, while mechanistic models aim to provide a robust description of available data by specifying assumptions and integrating known factors about the system. Compartmental analysis is a key example of a mechanistic model in pharmacokinetics and...
Fundamental Attribution Error01:14

Fundamental Attribution Error

According to some social psychologists, people tend to overemphasize internal factors as explanations—or attributions—for the behavior of other people. They tend to assume that the behavior of another person is a trait of that person, and to underestimate the power of the situation on the behavior of others. They tend to fail to recognize when the behavior of another is due to situational variables, and thus to the person’s state. This erroneous assumption is called the fundamental attribution...

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Related Experiment Video

Updated: May 29, 2026

Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment
08:43

Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment

Published on: August 7, 2017

Whole-to-parts causation mechanism.

Yoshiyuki Ohmura1, Yasuo Kuniyoshi1

  • 1Department of Mechano-Informatics, Graduate School of Information Science and Technology, The University of Tokyo, Tokyo, Japan.

Frontiers in Psychology
|May 28, 2026
PubMed
Summary
This summary is machine-generated.

This study proposes a novel mechanism for whole-to-parts causation in complex systems. An inter-level negative feedback control system allows macro-level functions to influence micro-level components, like neural synaptic weights.

Keywords:
algebraic structure controlasymmetry between cause and effectdownward causationinter-level causationneural network model

More Related Videos

Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

Related Experiment Videos

Last Updated: May 29, 2026

Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment
08:43

Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment

Published on: August 7, 2017

Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

Area of Science:

  • Neuroscience
  • Systems Biology
  • Philosophy of Mind

Background:

  • The concept of whole-to-parts causation faces challenges due to causal exclusion arguments.
  • Previous models did not account for hierarchical structures and feedback mechanisms.

Purpose of the Study:

  • To propose a theoretical framework for whole-to-parts causation.
  • To demonstrate how negative feedback control can enable macro-level influence on micro-level components.

Main Methods:

  • Modeling a hierarchical system with supervenient functions at the macro-level.
  • Defining an algebraic structure for macro-level equations governing feedback error.
  • Implementing inter-level negative feedback control to modify micro-level synaptic weights.

Main Results:

  • A macro-level equation independent of external causes was formulated, introducing causal power to the micro-level.
  • The proposed mechanism avoids causal overdetermination.
  • Inter-level negative feedback control was identified as a viable whole-to-parts causation mechanism.

Conclusions:

  • The study presents a theoretical basis for whole-to-parts causation through negative feedback control.
  • This mechanism offers a new perspective on how higher-level systems can influence their constituent parts.
  • The findings have implications for understanding complex systems, including neural networks.