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Classical and Operant Conditioning in Learning Models

Explore the fundamentals of classical and operant conditioning, from Pavlov's classical conditioning to Skinner's operant conditioning. Learn about neural bases of learning, reinforcement schedules, extinction, variations in learning, and the limitations and applications of behaviorism. Discover how behaviorist theories impact learning and behavior.

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Classical and Operant Conditioning in Learning Models

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  1. Today we will focus on two basic models of learning, classical and operant conditioning. Specific topics include: • Pavlov's classical conditioning and how it might be implemented in the nervous • system. • Thorndike's law of Effect and its evolution into Skinner's operant conditioning. • A model: How operant conditioning operates together with basic motivations as • an adaptive mechanism. • Uses and abuses of operant conditioning. • If there is time, we will consider the contributions of behaviorism against its • "overclaiming".

  2. Ivan Pavlov & lab

  3. The CS as a Signal • Evidence suggests that the CS serves as a signal for upcoming events. • Learning is less likely if the CS is simultaneous with the US or follows it. • 0.5 second lead time optimal • Learning occurs only if there is some contingency between the CS and US. • Contiguity isn’t enough

  4. Stimulus Generalization

  5. Neural Basis of Learning • The neural bases for learning involve diverse mechanisms, such as: • presynaptic facilitation • postsynaptic changes • long-term potentiation (LTP) • creation of new synapses • growth of new dendritic spines

  6. Edward Thorndike

  7. B. F. Skinner

  8. Schedules of Reinforcement • Partial reinforcement: The response is reinforced only some of the time. • schedule of reinforcement • in ratio schedules, reinforcement after a number of responses; the ratio used may be fixed or variable • in interval schedules, reinforcers for the first response made after a given interval since the last reinforcement; this interval can be fixed or variable

  9. Extinction under fixed & variable schedules of reinforcement

  10. Edward Tolman

  11. Variations in Learning: Behavioral Challenges • Observational learning • Biologically biased learning • Belongingness/expectations in learning • “Smart” learning

  12. Mirror Neurons

  13. Conclusions: Broad Applications Coupled with Severe Limitations of the Behaviorist Viewpoint Next: The Power Law of Practice: Learning just keeps going and going and going….

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