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Related Concept Videos

Relative Frequency Histogram01:14

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The relative frequency depicts the proportion of data points that have each value. The frequency tells the number of data points that have each value. Like the histogram, a relative frequency histogram also has the same shape with a horizontal scale (the x-axis), but the vertical scale (the y-axis) is marked with relative frequencies (percentages of the whole) instead of actual frequencies. A relative frequency histogram is a graphical representation of a frequency distribution where the...
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A relative frequency distribution is the proportion or fraction of times a value occurs in a data set. To find the relative frequencies, one can divide each frequency by the total number of data points in the sample. It is very similar to a regular frequency distribution, except that instead of reporting how many data values fall in a class, a relative frequency distribution reports the fraction of data values that fall in a class. These fractions or proportions are called relative frequencies...
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The electron of an atom can be abstracted from a compound by a relatively unstable radical to generate a new radical of relatively greater stability. For example, an initiator which forms radicals by homolysis can abstract a suitable species like a hydrogen atom or a halogen atom from a compound to generate a new radical. This ability of radicals to propagate by abstraction is a crucial feature of radical chain reactions.
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When the fitness of a trait is influenced by how common it is (i.e., its frequency) relative to different traits within a population, this is referred to as frequency-dependent selection. Frequency-dependent selection may occur between species or within a single species. This type of selection can either be positive—with more common phenotypes having higher fitness—or negative, with rarer phenotypes conferring increased fitness.
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Related Experiment Video

Updated: Feb 8, 2026

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
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Differences in relative frequency facilitate learning abstract rules.

Júlia Monte-Ordoño1, Juan M Toro2,3

  • 1Universitat Pompeu Fabra, C. Ramon Trias Fargas, 25-27, CP 08005, Barcelona, Spain. julia.monte@upf.edu.

Psychological Research
|June 28, 2018
PubMed
Summary
This summary is machine-generated.

Listeners can learn abstract language rules even from noisy or mixed input. Relative frequency differences help distinguish and learn multiple linguistic patterns from heterogeneous signals.

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Area of Science:

  • Cognitive Science
  • Linguistics
  • Psychology

Background:

  • Language acquisition typically occurs with non-homogeneous input, including noise or multiple linguistic systems.
  • Understanding how abstract rules are learned in complex environments is crucial for cognitive science.

Purpose of the Study:

  • To investigate the conditions under which abstract rules are learned from heterogeneous linguistic input.
  • To determine if listeners can discern and learn a target rule amidst competing regularities.

Main Methods:

  • Six experiments were conducted to test rule learning from varied input contexts.
  • Participants were familiarized with linguistic streams containing one or more abstract rules.
  • Varying the proportion and homogeneity of rule exemplars to assess learning thresholds.

Main Results:

  • Listeners successfully learned abstract rules even when presented with conflicting patterns or noise.
  • Learning occurred even when the target rule was present in only 10% of the input.
  • No learning was observed when target and non-target rules were equally represented in the input.

Conclusions:

  • Listeners can effectively learn abstract linguistic rules from heterogeneous input.
  • Relative frequency differences are key mechanisms for segregating and learning distinct linguistic rules.
  • This demonstrates a robust capacity for abstract rule extraction in complex language environments.