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Language Development01:22

Language Development

1.0K
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...
1.0K
Higher Mental Functions of the Brain: Language01:10

Higher Mental Functions of the Brain: Language

4.1K
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...
4.1K
Language and Cognition01:27

Language and Cognition

956
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.
956
Components of Language01:24

Components of Language

918
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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Language01:16

Language

1.0K
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...
1.0K
Neural Circuits01:25

Neural Circuits

3.2K
Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...
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Related Experiment Video

Updated: Mar 29, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

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Bayesian Recurrent Neural Network for Language Modeling.

Jen-Tzung Chien, Yuan-Chu Ku

    IEEE Transactions on Neural Networks and Learning Systems
    |December 2, 2015
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a Bayesian approach to regularize recurrent neural network language models (RNN-LMs), improving word prediction accuracy. The Bayesian RNN-LM (BRNN-LM) offers a sparser, more robust model for continuous speech recognition systems.

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    Area of Science:

    • Artificial Intelligence
    • Machine Learning
    • Natural Language Processing

    Background:

    • Recurrent Neural Network Language Models (RNN-LMs) excel at capturing sequential data dynamics.
    • Training RNN-LMs is challenging due to a large parameter space and high-dimensional hidden layers.
    • Existing models face ill-posed training problems, limiting their effectiveness.

    Purpose of the Study:

    • To present a Bayesian approach for regularizing RNN-LMs.
    • To apply the regularized model to continuous speech recognition.
    • To enhance model sparsity and robustness.

    Main Methods:

    • Developed a Bayesian approach to regularize RNN-LMs using a Gaussian prior.
    • Formulated the objective function as a regularized cross-entropy error.
    • Implemented Bayesian RNN-LM (BRNN-LM) with a rapid Hessian matrix approximation.
    • Estimated Gaussian hyperparameters by maximizing marginal likelihood.

    Main Results:

    • The proposed BRNN-LM achieves a sparser model compared to standard RNN-LMs.
    • Demonstrated robustness of the BRNN-LM across various experimental conditions and corpora.
    • Showcased improved performance in continuous speech recognition tasks.

    Conclusions:

    • Bayesian regularization effectively addresses the ill-posed training problem in RNN-LMs.
    • BRNN-LM offers a more efficient and robust alternative for language modeling.
    • The method shows significant promise for advancing continuous speech recognition technology.