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

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...

Introduction to Learning

Introduction to Learning

Learning is the process of acquiring knowledge or skills through practice or experience, leading to long-lasting behavioral changes. This acquisition occurs through interaction with the environment and requires practice or experience. For instance, mastering a skill such as surfing requires considerable practice and experience, highlighting the essential role of repeated interactions with the environment in learning.
In contrast to learned behaviors, unlearned behaviors such as crying, sexual...

Purposive Learning

Purposive Learning

E. C. Tolman emphasized the purposiveness of behavior — the idea that much of our behavior is goal-directed. For instance, employees who aim for a promotion work diligently to meet their targets. Tolman argued that when classical conditioning and operant conditioning occur, the organism acquires certain expectations. In classical conditioning, a child might fear a dog because they expect it to bite. In operant conditioning, a person might consistently work overtime because they expect a...

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...

Observational Learning

Observational Learning

Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...

Impact of Social Context on Individuals

Impact of Social Context on Individuals

Social psychology examines how the real or imagined presence of others influences individuals' thoughts, feelings, and behaviors. A key concept in this field is the role of social context in shaping behavior. The same individual may act differently depending on the social setting, due to the varying expectations and norms associated with each environment. This context-dependent behavior illustrates the influence of social roles, which prescribe appropriate conduct in specific situations.Social...

You might also read

Related Articles

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

Sort by

Same author

[Risk factors on the unintentional injuries among rural children aged 0-12 in Shaanxi province].

Zhonghua liu xing bing xue za zhi = Zhonghua liuxingbingxue zazhi·2013

Same author

Adcyap1r1 genotype, posttraumatic stress disorder, and depression among women exposed to childhood maltreatment.

Depression and anxiety·2013

Same author

Current status and challenge of Human Parasitology teaching in China.

Pathogens and global health·2012

Same author

Molecular characterization of heterogeneous mesenchymal stem cells with single-cell transcriptomes.

Biotechnology advances·2012

Same author

Surgical treatment of ossification of the ligamentum flavum associated with dural ossification in the thoracic spine.

Journal of clinical neuroscience : official journal of the Neurosurgical Society of Australasia·2012

Same author

Broadband focusing ultrasonic transducers based on dimpled LiNbO3 plate with inversion layer.

IEEE transactions on ultrasonics, ferroelectrics, and frequency control·2012

Search research articles

Related Experiment Video

Updated: Nov 14, 2025

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

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

Published on: December 6, 2024

Unsupervised Word Embedding Learning by Incorporating Local and Global Contexts.

Yu Meng¹, Jiaxin Huang¹, Guangyuan Wang¹

¹Department of Computer Science, University of Illinois at Urbana-Champaign, Champaign, IL, United States.

Frontiers in Big Data

|March 11, 2021

Summary

This study introduces novel unsupervised word embedding models that combine local and global contexts. These models enhance word representations, improving performance in word similarity and text classification tasks.

Keywords:

global contexts local contexts unsupervised learning word embedding word semantics

More Related Videos

Decoding Natural Behavior from Neuroethological Embedding

Decoding Natural Behavior from Neuroethological Embedding

Published on: October 3, 2025

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

Related Experiment Videos

Last Updated: Nov 14, 2025

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

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

Published on: December 6, 2024

Decoding Natural Behavior from Neuroethological Embedding

Decoding Natural Behavior from Neuroethological Embedding

Published on: October 3, 2025

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:

Natural Language Processing
Machine Learning

Background:

Word embeddings learn distributed representations to encode word semantics.
Current methods primarily model local word contexts, which only partially capture semantics.

Purpose of the Study:

To propose novel unsupervised word embedding models that integrate both local and global contexts.
To enhance the learning of word representations by capturing broader semantic aspects.

Main Methods:

Developed two simple yet effective unsupervised models for word embedding.
Jointly modeled local and global contexts, assuming a generative relationship between words and contexts.
Provided theoretical interpretations of the joint modeling approach.

Main Results:

The proposed models achieved superior performance on word similarity tasks.
Demonstrated enhanced effectiveness in text classification tasks.
Quantitative analysis and case studies validated the models' performance.

Conclusions:

Jointly modeling local and global contexts significantly improves unsupervised word embedding.
The proposed models offer a simple and effective approach to learning richer word representations.
These enhanced representations benefit downstream natural language processing applications.