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Related Concept Videos

Purposive Learning01:22

Purposive Learning

546
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...
546
Observational Learning01:12

Observational Learning

1.1K
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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Generalization, Discrimination, and Extinction01:24

Generalization, Discrimination, and Extinction

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Generalization, discrimination, and extinction are key concepts in operant conditioning that influence how behaviors are learned and maintained.
Generalization occurs when a behavior reinforced in one context is performed in similar situations. For instance, a student who studies diligently for calculus and receives excellent grades might apply the same study habits to psychology and history, expecting similar results. Generalization shows how learning in one setting can influence behavior in...
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Cognitive Learning01:21

Cognitive Learning

1.5K
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...
1.5K
Timing and Consequences on Behavior01:08

Timing and Consequences on Behavior

528
In operant conditioning, the timing of reinforcement is crucial. For animals like rats and cats, immediate reinforcement (within a few seconds) is much more effective than delayed reinforcement. For example, a food reward for a rat needs to follow within 30 seconds of pressing a bar to be effective. 
Humans, however, can respond to delayed reinforcers. We often make decisions between immediate small rewards and delayed larger rewards. This ability to delay gratification is a significant...
528
Law of Effect01:06

Law of Effect

4.6K
B.F. Skinner, a prominent figure in behavioral psychology, introduced operant conditioning by emphasizing the role of consequences in shaping behavior. This theory builds upon the law of effect proposed by Edward Thorndike, which posits that behaviors followed by satisfying outcomes are likely to be repeated. In contrast, those followed by unsatisfying outcomes are less likely to recur.
Edward Thorndike's foundational work involved studying learning in animals, particularly using puzzle...
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Related Experiment Video

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Pavlovian Conditioned Approach Training in Rats
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Learning to Predict Consequences as a Method of Knowledge Transfer in Reinforcement Learning.

Eric Chalmers, Edgar Bermudez Contreras, Brandon Robertson

    IEEE Transactions on Neural Networks and Learning Systems
    |April 25, 2017
    PubMed
    Summary

    Reinforcement learning agents can improve future task performance by predicting action consequences. This knowledge transfer method, using agent-centric data, enables faster and more cost-effective learning in new environments.

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    Area of Science:

    • Artificial Intelligence
    • Machine Learning
    • Robotics

    Background:

    • Reinforcement learning (RL) agents learn through trial-and-error.
    • Efficient long-term learning requires transferring knowledge to new tasks.
    • Predicting action consequences is key to effective knowledge transfer.

    Purpose of the Study:

    • To propose a novel knowledge transfer method for RL agents.
    • To leverage agent-centric information for predicting environmental consequences.
    • To improve learning efficiency and reduce costs in novel environments.

    Main Methods:

    • Developed an RL approach using both agent-centric and environment-centric information.
    • Trained agents to predict action consequences based on agent-centric data.
    • Evaluated the method on spatial navigation and network routing tasks.

    Main Results:

    • The proposed knowledge transfer approach demonstrated faster learning.
    • The method resulted in lower learning costs compared to alternatives.
    • Agent-centric predictions effectively transferred knowledge to environment-centric learning.

    Conclusions:

    • Predicting action consequences using agent-centric information facilitates efficient knowledge transfer in RL.
    • This approach enhances an agent's ability to adapt to new tasks and environments.
    • The method shows significant advantages for complex domains like navigation and routing.