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Related Concept Videos

Higher Mental Functions of the Brain: Language01:10

Higher Mental Functions of the Brain: Language

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Language is a system of communication that allows the expression of thoughts, ideas, and feelings. The brain processes language in both hemispheres.
Language formation and comprehension take place in the dominant hemisphere. The dominant hemisphere is responsible for understanding the meaning of spoken, written, or sign language, as well as the ability to communicate. For most people, the left hemisphere is the dominant one. The right hemisphere, then, gives tone and emotional context to the...
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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.
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Natural selection is an evolutionary process in which individuals with survival-promoting traits reproduce at higher rates. These favorable traits become more common within a population or species. Naturally selected traits initially arise via random genetic mutations. In order for selection to occur, there must be variation within a population, the trait controlling the variation must be heritable, and there must be an evolutionary advantage for variation in the trait.
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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.
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Language Development01:22

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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.
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Language serves as a bridge between ideas and communication, influencing how individuals perceive and interact with the world. Psychologists have long debated whether language shapes thought or vice versa. This discussion gained grip with Edward Sapir and Benjamin Lee Whorf in the 1940s, who proposed that language determines thought, a concept known as linguistic determinism. They suggested that the vocabulary and structure of a language influence how its speakers think and perceive reality.
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Related Experiment Video

Updated: Jan 25, 2026

Using Eye Movements Recorded in the Visual World Paradigm to Explore the Online Processing of Spoken Language
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Natural Language Processing for the Identification of Silent Brain Infarcts From Neuroimaging Reports.

Sunyang Fu1, Lester Y Leung2, Yanshan Wang1

  • 1Department of Health Sciences Research, Mayo Clinic, Rochester, MN, United States.

JMIR Medical Informatics
|May 9, 2019
PubMed
Summary

Natural language processing (NLP) effectively identifies silent brain infarction (SBI) and white matter disease (WMD) from neuroimaging reports. This approach aids in early detection and stroke risk mitigation through electronic health records.

Keywords:
electronic health recordsnatural language processingneuroimaging

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Area of Science:

  • Medical informatics
  • Computational linguistics
  • Neurology

Background:

  • Silent brain infarction (SBI) involves asymptomatic brain lesions from vascular occlusion, detectable via neuroimaging.
  • SBI is more prevalent than stroke and found in 20% of healthy elderly individuals.
  • Early SBI detection can enable preventative treatments to mitigate stroke risk.

Purpose of the Study:

  • To develop natural language processing (NLP) systems for identifying incidentally discovered SBIs.
  • To extract and classify SBI-related findings from neuroimaging reports at Mayo Clinic and Tufts Medical Center.

Main Methods:

  • Utilized both rule-based (MedTagger) and machine learning (CNN, random forest, SVM, logistic regression) NLP approaches.
  • Generated features for rule-based systems using pointwise mutual information.
  • Compared NLP algorithm performance against a gold standard dataset of 1000 radiology reports.

Main Results:

  • The rule-based system achieved high performance in predicting SBI (accuracy: 0.991, PPV: 1.000, NPV: 0.990).
  • The Convolutional Neural Network (CNN) demonstrated excellent performance in predicting white matter disease (WMD) (accuracy: 0.994, PPV: 0.994, NPV: 0.994).
  • High interannotator agreements (0.87-0.91) were observed for the gold standard dataset.

Conclusions:

  • Developed and validated NLP techniques for detecting incidental SBIs and WMDs from annotated neuroimaging reports.
  • Demonstrated the high feasibility of using NLP for SBI and WMD detection within electronic health records (EHRs).