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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...

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...

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...

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...

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...

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.

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Related Experiment Video

Updated: Jan 13, 2026

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

Lifelong Learning of Large Language Model Based Agents: A Roadmap.

Junhao Zheng, Chengming Shi, Xidi Cai

IEEE Transactions on Pattern Analysis and Machine Intelligence

|January 6, 2026

Summary

Lifelong learning enables artificial general intelligence (AGI) agents to adapt continuously. This survey details techniques for integrating lifelong learning into large language models (LLMs) agents to overcome static system limitations.

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Area of Science:

Artificial Intelligence
Machine Learning

Background:

Lifelong learning is essential for advancing Artificial General Intelligence (AGI).
Current large language models (LLMs) agents are often static and cannot adapt to dynamic environments.
There is a need for systematic approaches to enable LLM agents to learn continuously.

Purpose of the Study:

To provide the first systematic survey of techniques for incorporating lifelong learning into LLM-based agents.
To categorize core components of lifelong learning LLM agents.
To offer a roadmap for developing adaptive LLM agents.

Main Methods:

Categorization of LLM agent components into perception, memory, and action modules.
Systematic review of existing literature on lifelong learning techniques for LLMs.
Analysis of how these components enable continuous adaptation and mitigate catastrophic forgetting.

Main Results:

Identification of key modules (perception, memory, action) for lifelong learning in LLM agents.
Highlighting techniques that enable continuous adaptation and combat catastrophic forgetting.
Summarizing emerging trends, evaluation metrics, and applications for lifelong learning LLM agents.

Conclusions:

Lifelong learning is critical for developing adaptable and robust LLM agents.
A modular approach (perception, memory, action) facilitates continuous learning.
This survey serves as a foundational resource for future research in lifelong learning LLM agents.