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Related Concept Videos

Reinforcement01:23

Reinforcement

Positive and negative reinforcement are key concepts in operant conditioning, a learning process where the consequences of a behavior affect the likelihood of that behavior being repeated.
Positive reinforcement occurs when a behavior is followed by the presentation of a rewarding stimulus, increasing the frequency of that behavior. For example:
Reinforcement Schedules01:24

Reinforcement Schedules

Positive reinforcement is a powerful method for teaching new behaviors to both animals and humans. B.F. Skinner demonstrated this with his experiments using rats in a Skinner box. When a rat pressed a lever, it received a food pellet. This immediate reward encouraged the rat to repeat the behavior. This method, where a reward follows every instance of the behavior, is known as continuous reinforcement. It is highly effective for establishing new behaviors quickly.
Once a behavior is learned,...
Observational Learning01:12

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 because...
Associative Learning01:27

Associative Learning

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...
Purposive Learning01:22

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 bonus...
Long-term Potentiation01:35

Long-term Potentiation

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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Related Experiment Videos

Developing PFC representations using reinforcement learning.

Jeremy R Reynolds1, Randall C O'Reilly2

  • 1Department of Psychology, University of Denver, 2155 S. Race St., Denver, CO 80208, United States.

Cognition
|July 14, 2009
PubMed
Summary
This summary is machine-generated.

Computational models reveal how architectural constraints in the frontal cortex (PFC) create hierarchical representations, aiding in planning and goal-oriented behaviors through reinforcement learning.

Related Experiment Videos

Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Cognitive Science

Background:

  • The frontal cortex (PFC) is widely believed to support action production, planning, and goal-oriented behaviors hierarchically.
  • The precise nature and origins of this hierarchical organization remain unclear.

Purpose of the Study:

  • To investigate the origins of hierarchical representations in the PFC using biologically-inspired computational models.
  • To explore factors contributing to hierarchical organization, including connectivity and synaptic plasticity.

Main Methods:

  • Development of computational models employing reinforcement learning to generate representations.
  • Simulation of various architectural constraints: PFC connectivity hierarchy, PFC-subcortical connections, and differential synaptic plasticity.

Main Results:

  • Architectural constraints were found to promote the segregation of different types of representations within the PFC.
  • This segregation of representations was shown to facilitate learning processes.

Conclusions:

  • The study supports the existence of a functional hierarchy in the PFC.
  • Findings align with previous computational models and empirical data on PFC function and cognitive control.