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Related Concept Videos

Associative Learning01:27

Associative Learning

469
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...
469
Cognitive Learning01:21

Cognitive Learning

459
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...
459
Observational Learning01:12

Observational Learning

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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...
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Purposive Learning01:22

Purposive Learning

174
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...
174
Avoidance Learning and Learned Helplessness01:14

Avoidance Learning and Learned Helplessness

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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...
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Introduction to Learning01:18

Introduction to Learning

486
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...
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Related Experiment Video

Updated: Jul 30, 2025

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques
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Cross Domain Lifelong Learning Based on Task Similarity.

Shuojin Yang, Zhanchuan Cai

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |May 17, 2023
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    Summary

    This study introduces a Cross-Domain Lifelong Learning (CDLL) framework to prevent catastrophic forgetting in AI. The new method enhances AI

    Area of Science:

    • Artificial Intelligence
    • Machine Learning
    • Deep Learning

    Background:

    • Deep neural networks excel in single-domain tasks but suffer from catastrophic forgetting when learning new tasks.
    • Human learning demonstrates continuous adaptation without significant knowledge loss across diverse domains.
    • Current AI models struggle to replicate human-like lifelong learning capabilities, especially across different domains.

    Purpose of the Study:

    • To develop a Cross-Domain Lifelong Learning (CDLL) framework that mimics human learning by minimizing catastrophic forgetting.
    • To enhance the ability of deep neural networks to learn continuously across multiple domains.
    • To explore and leverage task similarities to improve knowledge retention and acquisition.

    Main Methods:

    • Employed a Dual Siamese Network (DSN) to identify and learn essential similarity features between tasks from different domains.

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  • Introduced a Domain-Invariant Feature Enhancement Module (DFEM) for robust extraction of domain-invariant features.
  • Developed a Spatial Attention Network (SAN) guided by similarity features to dynamically weight task importance.
  • Utilized a Structural Sparsity Loss (SSL) to optimize the SAN for parameter efficiency and accuracy.

    • Main Results:

    • The CDLL framework significantly reduced catastrophic forgetting compared to state-of-the-art methods in multi-domain, continuous learning scenarios.
    • The proposed method demonstrated minimal forgetting of previously acquired knowledge while improving performance on learned tasks.
    • Experimental results indicate a learning process more aligned with human capabilities, retaining old knowledge and enhancing new learning.

    Conclusions:

    • The CDLL framework offers an effective solution for lifelong learning in deep neural networks, addressing the challenge of catastrophic forgetting.
    • Leveraging task similarities and domain-invariant features is crucial for building robust lifelong learning systems.
    • The approach paves the way for AI systems that can learn continuously and adaptively, similar to human cognition.