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Related Concept Videos

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...
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence of...
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:
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...
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,...
Cognitive Learning01:21

Cognitive Learning

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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Related Experiment Video

Updated: Jun 19, 2026

Automated Interactive Video Playback for Studies of Animal Communication
07:21

Automated Interactive Video Playback for Studies of Animal Communication

Published on: February 9, 2011

Online reinforcement learning for dynamic multimedia systems.

Nicholas Mastronarde1, Mihaela van der Schaar

  • 1Department of Electrical and Computer Engineering,University of California, Los Angeles, CA 90095-1594, USA. nhmastro@ee.ucla.edu

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|November 4, 2009
PubMed
Summary
This summary is machine-generated.

Multimedia systems can now learn dynamically at runtime. New reinforcement learning algorithms optimize performance and meet real-time constraints, outperforming older methods.

Related Experiment Videos

Last Updated: Jun 19, 2026

Automated Interactive Video Playback for Studies of Animal Communication
07:21

Automated Interactive Video Playback for Studies of Animal Communication

Published on: February 9, 2011

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Multimedia Systems

Background:

  • Previous work proposed offline cross-layer optimization for dynamic multimedia systems using layered Markov decision processes.
  • Real-world systems require online learning as system dynamics are often unknown a priori.

Purpose of the Study:

  • To enable multimedia system layers to learn autonomously at run-time to optimize long-term performance.
  • To address challenges in layered learning, including inter-layer learning impact and balancing complexity with constraints.

Main Methods:

  • Proposed two reinforcement learning algorithms: one centralized and one decentralized, for cross-layer optimization.
  • Introduced a complementary accelerated learning algorithm leveraging partial system knowledge.
  • Analyzed algorithms for computation, memory, and communication overheads.

Main Results:

  • Decentralized learning achieved performance comparable to centralized learning, enabling autonomous layer operation.
  • The accelerated learning algorithm significantly improved system performance.
  • Proposed application-aware and foresighted learning methods outperformed existing application-independent and myopic algorithms.

Conclusions:

  • Autonomous, online, cross-layer optimization is feasible for dynamic multimedia systems.
  • Reinforcement learning, particularly with accelerated methods, offers a powerful approach for real-time system adaptation.
  • The proposed methods provide significant advantages over existing learning algorithms in multimedia applications.