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Related Concept Videos

Purposive Learning01:22

Purposive Learning

210
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...
210
Language Development01:22

Language Development

462
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...
462
Cognitive Learning01:21

Cognitive Learning

672
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...
672
Components of Language01:24

Components of Language

416
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.
416
Language and Cognition01:27

Language and Cognition

466
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.
466
Introduction to Learning01:18

Introduction to Learning

551
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...
551

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Related Experiment Video

Updated: Sep 19, 2025

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

693

Prompt-Enhanced: Leveraging language representation for prompt continual learning.

Wei Li1, Dezhi Li1, Shitong Shao1

  • 1School of Instrument Science and Engineering, Southeast University, Nanjing, 210096, Jiangsu, China.

Neural Networks : the Official Journal of the International Neural Network Society
|June 3, 2025
PubMed
Summary
This summary is machine-generated.

Prompt-E enhances continual learning by using language-guided regularization to prevent forgetting. This method improves model accuracy on evolving data streams without significant memory or privacy costs.

Keywords:
Continual learningContrastive learningPrompt learningPrompt regularization

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Continual learning allows models to learn sequentially without forgetting past knowledge.
  • Traditional methods face memory and privacy issues; prompt-based methods offer an alternative but can lose information.
  • Large-scale pre-trained vision transformers have advanced prompt-based approaches in continual learning.

Purpose of the Study:

  • To introduce Prompt-E, a novel method to enhance prompt-based continual learning.
  • To address generalized information loss and catastrophic forgetting in long-term continual learning.
  • To improve task-specific relevance and stability of prompts across evolving data.

Main Methods:

  • Prompt-E incorporates language-guided regularization into prompt-based continual learning.
  • It dynamically enhances prompt features using language representation for task relevance.
  • The method constrains CLS tokens and prompts to mitigate prompt conflict and forgetting.

Main Results:

  • Prompt-E demonstrates significant accuracy gains in class-incremental tasks over existing prompt-based methods.
  • It improved L2P accuracy on DomainNet by 3.29% with minimal additional parameters (1.89M).
  • Ablation studies confirmed the effectiveness of language-guided prompt regularization.

Conclusions:

  • Prompt-E offers a lightweight and effective solution for prompt-based continual learning.
  • The proposed language-guided regularization successfully combats catastrophic forgetting and prompt conflict.
  • Prompt-E represents a significant advancement for long-term continual learning with vision transformers.