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Related Concept Videos

Cognitive Learning01:21

Cognitive Learning

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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...
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Purposive Learning01:22

Purposive Learning

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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...
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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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Associative Learning01:27

Associative Learning

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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...
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Introduction to Learning01:18

Introduction to Learning

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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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Long-term Potentiation

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Long-term potentiation, or LTP, is one of the ways by which synaptic plasticity—changes in the strength of chemical synapses—can occur in the brain. LTP is the process of synaptic strengthening that occurs over time between pre- and postsynaptic neuronal connections. The synaptic strengthening of LTP works in opposition to the synaptic weakening of long-term depression (LTD) and together are the main mechanisms that underlie learning and memory.
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Lifelong Teacher-Student Network Learning.

Fei Ye, Adrian G Bors

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    |June 25, 2021
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    Summary
    This summary is machine-generated.

    This study introduces a novel lifelong learning framework using a Teacher-Student model. This approach enables artificial intelligence to retain past knowledge while learning new information, overcoming limitations in current AI systems.

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    Area of Science:

    • Artificial Intelligence
    • Machine Learning
    • Cognitive Science

    Background:

    • Human learning involves acquiring new knowledge from experiences.
    • Current AI systems struggle to retain previously learned information when trained on new tasks.
    • This limitation hinders the development of truly adaptable AI.

    Purpose of the Study:

    • To propose a novel lifelong learning methodology for artificial intelligence.
    • To enable AI systems to retain past knowledge while learning new information.
    • To address the limitations of current AI in sequential learning tasks.

    Main Methods:

    • A Teacher-Student network framework is employed.
    • The Teacher module, a Generative Adversarial Network (GAN), preserves and replays past knowledge.
    • The Student module, a Variational Autoencoder (VAE), learns from both past and new data.
    • The framework supports supervised, semi-supervised, and unsupervised training.

    Main Results:

    • The proposed framework successfully enables lifelong learning in AI.
    • The Teacher module effectively replays past knowledge using probabilistic representations.
    • The Student module integrates past knowledge with new data for robust learning.
    • The system captures both continuous and discrete data representations across domains.

    Conclusions:

    • The Teacher-Student framework offers a viable solution for AI lifelong learning.
    • This methodology allows AI to build upon previous knowledge, mimicking human cognitive abilities.
    • The approach enhances AI adaptability and performance in dynamic learning environments.