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Related Concept Videos

Language01:16

Language

917
Language is a unique communication system that uses words and systematic rules to organize and transmit information. Unlike other forms of communication, which may involve postures, movements, odors, or vocalizations, language relies on symbols and grammar. This makes human communication distinct from that of other species, who also communicate but do not use language in the same way humans do.
Corballis and Suddendorf (2007) and Tomasello and Rakoczy (2003) highlight the role of language in...
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Correlations02:20

Correlations

36.1K
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...
36.1K
Correlation and Causation01:27

Correlation and Causation

42.8K
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...
42.8K
Range00:59

Range

14.2K
The range is one of the measures of variation. It can be defined as the difference between a dataset's highest and lowest values. For example, in the study of seven 16-ounce soda cans, the filled volume of soda was measured, thus producing the following amount (in ounces) of soda:
15.9; 16.1; 15.2; 14.8; 15.8; 15.9; 16.0; 15.5
Measurements of the amount of soda in a 16-ounce can vary since different subjects record these measurements or since the exact amount - 16 ounces of liquid, was not...
14.2K
Components of Language01:24

Components of Language

821
Language, whether spoken, signed, or written, consists of specific components: lexicon and grammar. The lexicon is the vocabulary of a language, comprising its words. Grammar is the set of rules used to convey meaning through the lexicon. For example, English grammar adds “-ed” to most verbs to indicate past tense. Words are formed by combining phonemes, which are the basic sound units of a language. Different languages have different sets of phonemes (e.g., “ah” vs.
821
Language Development01:22

Language Development

916
Children master language quickly and with relative ease, supported by both biological predisposition and reinforcement. B. F. Skinner (1957) proposed that language is learned through reinforcement, while Noam Chomsky (1965) argued that language acquisition mechanisms are biologically determined.
The critical period for language acquisition suggests that the ability to acquire language is at its peak early in life. As people age, this proficiency decreases. Language development begins very...
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Updated: Feb 4, 2026

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Long-Range Correlation Underlying Childhood Language and Generative Models.

Kumiko Tanaka-Ishii1

  • 1Research Center for Advanced Science and Technology, University of Tokyo, Tokyo, Japan.

Frontiers in Psychology
|October 5, 2018
PubMed
Summary

Long-range correlation, a statistical dependence in time series, is found in CHILDES language data. A new model combining Simon and Pitman-Yor models explains this phenomenon and vocabulary growth.

Keywords:
CHILDESPitman-Yor modelSimon Modelfluctuation analysisgenerative modelslong-range correlation

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Area of Science:

  • Linguistics
  • Statistical Physics
  • Cognitive Science

Background:

  • Long-range correlation, a statistical dependence between distant time series subsequences, is primarily studied in statistical physics.
  • Previous research has indicated the presence of long-range correlation in natural language.

Purpose of the Study:

  • To investigate the occurrence of long-range correlation in CHILDES datasets.
  • To explore generative stochastic models of language to understand the underlying mechanisms of long-range correlation.
  • To develop a novel model that accurately reflects both long-range correlation and vocabulary growth in natural language.

Main Methods:

  • Utilized a state-of-the-art method for analyzing long-range correlation on extensive CHILDES datasets.
  • Evaluated representative generative stochastic language models, including the Simon and Pitman-Yor models.
  • Devised a new hybrid model by combining elements of the Simon and Pitman-Yor models.

Main Results:

  • Long-range correlation was confirmed in long CHILDES datasets.
  • The Simon model exhibited significant long-range correlation, while the Pitman-Yor model did not.
  • The newly devised conjunct model demonstrated both long-range correlation and accurate vocabulary growth rates.

Conclusions:

  • Uniform sampling is identified as a potential cause of long-range correlation in language.
  • The findings suggest a connection between uniform sampling mechanisms and actual linguistic processes.
  • The developed model offers a more comprehensive explanation for language properties than previously existing models.