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Related Concept Videos

Correlations02:20

Correlations

36.4K
Correlation means that there is a relationship between two or more variables (such as ice cream consumption and crime), but this relationship does not necessarily imply cause and effect. When two variables are correlated, it simply means that as one variable changes, so does the other. We can measure correlation by calculating a statistic known as a correlation coefficient. A correlation coefficient is a number from -1 to +1 that indicates the strength and direction of the relationship between...
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Correlation and Causation01:27

Correlation and Causation

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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...
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Correlation01:09

Correlation

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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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Correlation and Regression00:53

Correlation and Regression

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In statistics, correlation describes the degree of association between two variables. In the subfield of linear regression, correlation is mathematically expressed by the correlation coefficient, which describes the strength and direction of the relationship between two variables. The coefficient is symbolically represented by 'r' and ranges from -1 to +1. A positive value indicates a positive correlation where the two variables move in the same direction. A negative value suggests a...
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Coefficient of Correlation01:12

Coefficient of Correlation

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The correlation coefficient, r, developed by Karl Pearson in the early 1900s, is numerical and provides a measure of strength and direction of the linear association between the independent variable x and the dependent variable y.
If you suspect a linear relationship between x and y, then r can measure how strong the linear relationship is.
What the VALUE of r tells us:
The value of r is always between –1 and +1: –1 ≤ r ≤ 1.
The size of the correlation r indicates the...
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Spearman's Rank Correlation Test01:20

Spearman's Rank Correlation Test

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Spearman's rank correlation test, also known as Spearman's rho, is a nonparametric method for assessing the strength and direction of association between two variables. This test is particularly valuable when the data distribution is unknown or when the assumption of normality does not hold. Named after the English psychologist and statistician Dr. Charles Edward Spearman, it serves as the nonparametric counterpart to Pearson's correlation coefficient.
Spearman's test calculates correlation by...
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The Measurement and Treatment of Suppression in Amblyopia
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The Measurement and Treatment of Suppression in Amblyopia

Published on: December 14, 2012

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Cortical correlates of amblyopia.

Lynne Kiorpes1, Nigel Daw2

  • 1Center for Neural Science,New York University,New York,New York.

Visual Neuroscience
|June 16, 2018
PubMed
Summary

Amblyopia, or lazy eye, causes vision deficits beyond reduced acuity. This review explores higher-order visual processing and binocular vision issues in the amblyopic brain, suggesting new research directions.

Keywords:
Extrastriate cortexInterocular suppressionSensitive periodsStriate cortexVisual-motor integration

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Area of Science:

  • Neuroscience
  • Ophthalmology
  • Vision Science

Background:

  • Amblyopia, or lazy eye, presents with diverse visual deficits, including acuity loss, impaired contrast sensitivity, and abnormal binocular vision.
  • Current amblyopia treatments, like patching, primarily target visual acuity but often fail to resolve other visual deficiencies.
  • The underlying causes for persistent higher-order visual processing deficits in amblyopia remain incompletely understood.

Purpose of the Study:

  • To review the known cortical correlates of visual deficits in amblyopia.
  • To highlight deficits in binocular vision and higher-order visual processing beyond primary visual cortex (V1/V2).
  • To propose future research directions for understanding amblyopic visual dysfunction.

Main Methods:

  • Literature review of studies investigating cortical mechanisms in amblyopia.
  • Analysis of neural correlates in both striate and extrastriate cortex.
  • Synthesis of findings related to binocular vision and perceptual deficits.

Main Results:

  • Neural correlates in V1 and V2 partially explain acuity loss but not all behavioral deficits.
  • Cortical abnormalities extend beyond early visual areas, impacting higher-order processing.
  • Suppression and oculomotor factors may play significant roles in amblyopia's complex visual deficits.

Conclusions:

  • Cortical processing deficits in amblyopia are widespread, affecting binocular vision and perception.
  • Further research is needed on suppression, oculomotor control, and top-down influences.
  • A comprehensive understanding requires investigating mechanisms beyond V1/V2 in the amblyopic brain.