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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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Language01:16

Language

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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.
Corballis and Suddendorf (2007) and Tomasello and Rakoczy (2003) highlight the role of language in...
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Higher Mental Functions of Brain: Learning and Memory01:26

Higher Mental Functions of Brain: Learning and Memory

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Memory is one of the most vital higher mental functions of the brain. Memory is closely related to learning because it enables us to retain information and experiences from our past to use them in our present life. It also helps us to remember facts, events, and skills, such as riding a bike or swimming. There are two types of memory — declarative memory, which involves memorizing facts or events, and procedural memory, which enables us to remember how to do something like writing or...
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What is Natural Selection?01:32

What is Natural Selection?

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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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Components of Language01:24

Components of Language

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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

Language Development

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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.
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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Related Experiment Video

Updated: Jan 28, 2026

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
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Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

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Natural language processing and machine learning algorithm to identify brain MRI reports with acute ischemic stroke.

Chulho Kim1,2,3, Vivienne Zhu2,3, Jihad Obeid2,3

  • 1Department of Neurology, Hallym University College of Medicine, Chuncheon, Korea.

Plos One
|March 1, 2019
PubMed
Summary
This summary is machine-generated.

Machine learning and natural language processing algorithms can accurately classify brain MRI reports for acute ischemic stroke (AIS). A single decision tree model achieved the highest performance in identifying AIS cases.

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Area of Science:

  • Medical informatics
  • Radiology
  • Computational linguistics

Background:

  • Automatic classification of brain MRI reports is crucial for identifying acute ischemic stroke (AIS).
  • Natural language processing (NLP) and machine learning (ML) offer potential solutions for this classification task.

Purpose of the Study:

  • To assess the performance of various NLP and ML algorithms in classifying brain MRI radiology reports into AIS and non-AIS phenotypes.
  • To evaluate the impact of n-grams and term frequency-inverse document frequency (TF-IDF) weighting on algorithm performance.

Main Methods:

  • A dataset of 3,204 brain MRI reports was randomly split into training (70%) and testing (30%) sets.
  • NLP techniques using the "quanteda" package were employed to process text data into a frequency matrix.
  • Binary logistic regression, naïve Bayesian classification, single decision tree, and support vector machine algorithms were applied and evaluated using F1-measure.

Main Results:

  • 14.3% of reports were labeled as AIS, with AIS reports being significantly longer than non-AIS reports.
  • The single decision tree algorithm achieved the highest F1-measure (93.2%) and accuracy (98.0%).
  • Incorporating bigrams improved the F1-measure for naïve Bayesian classification, while TF-IDF weighting did not yield additional performance gains.

Conclusions:

  • Supervised ML-based NLP algorithms are effective for the automatic classification of brain MRI reports to identify AIS patients.
  • The single decision tree model demonstrated superior performance as a classifier for identifying brain MRI reports indicative of AIS.