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Related Concept Videos

Cause and Effect01:53

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?
Correlation and Causation01:27

Correlation and Causation

Correlation and CausationStatistical tests can calculate whether there is a relationship, or correlation, between independent and dependent variables. A relationship between variables shows correlation, but it does not show cause-and-effect. A direct cause-and-effect relationship requires additional controlled experiments. If no consistent relationship exists between the variables, then there is no correlation.Correlation versus CausationIf the dependent variable increases or decreases when the...
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:
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...
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:
Correlation01:09

Correlation

In statistics, two variables are said to be correlated if the values of one variable are associated with the other variable. Depending on the relationship between two variables, correlation can be of three types– positive correlation, negative correlation, and zero correlation.
Two variables, for example, a and b, are said to be positively correlated if both variables move in the same direction. In other words, a positive correlation exists between two variables, a and b, if:

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New Framework for Understanding Cross-Brain Coherence in Functional Near-Infrared Spectroscopy (fNIRS) Hyperscanning Studies
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Macroscopically local correlations can violate information causality.

Daniel Cavalcanti1, Alejo Salles, Valerio Scarani

  • 1Centre for Quantum Technologies, National University of Singapore, Block S15, 2 Science Drive 3, 117542, Singapore. dcavalcanti@gmail.com

Nature Communications
|January 27, 2011
PubMed
Summary
This summary is machine-generated.

Quantum information theory offers physical axioms for quantum mechanics. This study shows Information Causality (IC) is distinct from macroscopic locality (ML), suggesting IC better defines observable quantum correlations.

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Area of Science:

  • Quantum Information Theory
  • Foundations of Quantum Mechanics
  • Theoretical Physics

Background:

  • Quantum mechanics, despite its success, lacks physically-grounded axioms.
  • Current axiomatic approaches rely on abstract mathematical concepts.
  • Quantum information theory offers a potential path towards physical principles.

Purpose of the Study:

  • To explore an extension of the Information Causality (IC) principle.
  • To investigate the relationship between IC and macroscopic locality (ML).
  • To determine if IC or ML better defines physically allowed quantum correlations.

Main Methods:

  • Developing an extension of the Information Causality principle.
  • Analyzing the logical consequences of this extended principle.
  • Comparing the scope of correlations allowed by IC and ML.

Main Results:

  • Information Causality (IC) and macroscopic locality (ML) are shown to be inequivalent principles.
  • Correlations satisfying ML would violate IC.
  • This inequivalence strengthens the case for IC as a fundamental physical principle.

Conclusions:

  • The study provides evidence that Information Causality is a more suitable physical principle for defining quantum correlations.
  • The findings suggest that ML may not be a universally applicable constraint in quantum information processing.
  • This work advances the search for a physically grounded axiomatic foundation for quantum mechanics.