Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Language and Cognition

Language and Cognition

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.

Components of Language

Components of Language

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. “eh”). Phonemes combine to...

Language Development

Language Development

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...

Cognitive Learning

Cognitive Learning

Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
E. C. Tolman's theory of purposive behavior emphasizes that much behavior is goal-directed. He argued that to understand behavior, we must look at the entire sequence of actions leading to a goal. For instance, high school students study hard, not just due to past reinforcement but also to achieve the goal of getting into a good college.
Tolman introduced the idea that behavior is influenced by...

Associative Learning

Associative Learning

Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...

Avoidance Learning and Learned Helplessness

Avoidance Learning and Learned Helplessness

Avoidance learning and learned helplessness are critical concepts in understanding behavioral responses to negative stimuli.
Avoidance learning occurs when an organism learns that a specific behavior can prevent an unpleasant outcome. For example, a student who receives a bad grade may start studying harder to avoid future poor grades. This behavior persists even when the negative outcome is no longer present. Avoidance learning is powerful because it maintains behavior in the absence of the...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Temporally selective contextual encoding in the dentate gyrus of the hippocampus.

Nature communications·2014

Same author

Improving the quality of statistics in regulatory ecotoxicity tests.

Ecotoxicology (London, England)·2013

Same author

Improved gross efficiency during long duration submaximal cycling following a short-term high carbohydrate diet.

International journal of sports medicine·2013

Same author

Effect of isometric exercise on resting blood pressure: a meta analysis.

Journal of human hypertension·2010

Same author

The application of machine learning techniques as an adjunct to clinical decision making in alcohol dependence treatment.

Substance use & misuse·2007

Same author

In good company.

Nursing standard (Royal College of Nursing (Great Britain) : 1987)·2002

Search research articles

Related Experiment Video

Updated: Jul 7, 2026

Transcranial Direct Current Stimulation (tDCS) of Wernicke's and Broca's Areas in Studies of Language Learning and Word Acquisition

Transcranial Direct Current Stimulation (tDCS) of Wernicke's and Broca's Areas in Studies of Language Learning and Word Acquisition

Published on: July 13, 2019

On learning context-free and context-sensitive languages.

M Boden¹, J Wiles

¹Sch. of Inf. Science, Comput. and Electr. Eng., Halmstad Univ.

IEEE Transactions on Neural Networks

|February 5, 2008

Summary

This summary is machine-generated.

Second-order sequential cascaded networks (SCNs) learn context-sensitive languages, offering an alternative to long short-term memory (LSTM) networks. SCNs demonstrate distinct dynamics and performance, enabling string processing beyond training data.

Related Experiment Videos

Last Updated: Jul 7, 2026

Transcranial Direct Current Stimulation (tDCS) of Wernicke's and Broca's Areas in Studies of Language Learning and Word Acquisition

Transcranial Direct Current Stimulation (tDCS) of Wernicke's and Broca's Areas in Studies of Language Learning and Word Acquisition

Published on: July 13, 2019

Area of Science:

Computational Linguistics
Artificial Intelligence
Machine Learning

Background:

Long short-term memory (LSTM) networks are prominent for processing sequential data and learning context-sensitive languages.
Existing models may have limitations in generalizing to unseen data or exhibit specific dynamic behaviors.

Purpose of the Study:

To introduce and analyze second-order sequential cascaded networks (SCNs) as an alternative to LSTMs for context-sensitive language learning.
To compare the dynamical behavior and performance of SCNs against LSTMs.

Main Methods:

Developing and implementing second-order sequential cascaded networks (SCNs).
Training SCNs on finite fragments of context-sensitive languages.
Analyzing the inductive capabilities of SCNs for processing strings outside the training set.
Comparing the dynamical properties and performance metrics of SCNs and LSTMs.

Main Results:

SCNs can successfully induce means from language fragments to process novel strings.
The dynamical behavior of SCNs is qualitatively different from that of LSTMs.
Performance differences and distinct dynamic characteristics between SCNs and LSTMs were observed and discussed.

Conclusions:

Second-order sequential cascaded networks (SCNs) represent a viable alternative to LSTMs for learning context-sensitive languages.
SCNs exhibit unique dynamic properties that differentiate them from LSTMs, offering new avenues for sequential data processing.