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Related Concept Videos

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...
Higher Mental Functions of Brain: Learning and Memory01:26

Higher Mental Functions of Brain: Learning and Memory

Memory is one of the most vital higher mental functions of the brain. Memory is closely related to learning because it enables us to retain information and experiences from our past to use them in our present life. It also helps us to remember facts, events, and skills, such as riding a bike or swimming. There are two types of memory — declarative memory, which involves memorizing facts or events, and procedural memory, which enables us to remember how to do something like writing or playing an...
Real-World Application of Classical Conditioning01:15

Real-World Application of Classical Conditioning

Classical conditioning not only includes the initial pairing of stimuli but also extends to more complex forms, such as higher-order conditioning. Higher-order conditioning involves creating associations beyond the primary conditioned stimulus, resulting in a chain of conditioned responses.
Higher-order, or second-order, conditioning occurs when a neutral stimulus becomes associated with an already established conditioned stimulus through repeated pairings. For instance, if a dog has been...
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...
Randomized Experiments01:13

Randomized Experiments

The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...
Random Variables01:09

Random Variables

A random variable is a single numerical value that indicates the outcome of a procedure. The concept of random variables is fundamental to the probability theory and was introduced by a Russian mathematician, Pafnuty Chebyshev, in the mid-nineteenth century.
Uppercase letters such as X or Y denote a random variable. Lowercase letters like x or y denote the value of a random variable. If X is a random variable, then X is written in words, and x is given as a number.
For example, let X = the...

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Related Experiment Video

Updated: Jul 7, 2026

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques
08:05

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

Published on: June 30, 2020

Associative learning in random environments using neural networks.

K S Narendra1, S Mukhopadhyay

  • 1Dept. of Electr. Eng., Yale Univ., New Haven, CT.

IEEE Transactions on Neural Networks
|January 1, 1991
PubMed
Summary

This study explores associative learning using neural networks and reinforcement learning to find optimal actions in random environments. It compares three neural network methods for decision-making, offering practical solutions for varying computational needs.

Related Experiment Videos

Last Updated: Jul 7, 2026

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques
08:05

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

Published on: June 30, 2020

Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Computational Neuroscience

Background:

  • Associative learning is fundamental to decision-making in dynamic environments.
  • Neural networks and learning automata provide frameworks for modeling learning processes.
  • Reinforcement learning is crucial for optimizing actions based on environmental feedback.

Purpose of the Study:

  • To investigate associative learning using neural networks within a reinforcement learning paradigm.
  • To determine optimal actions for a single decision-maker in a stochastic environment.
  • To compare different neural network-based methods for generating discriminant functions.

Main Methods:

  • Utilized neural networks and learning automata concepts for associative learning.
  • Employed reinforcement learning to train a single decision-maker in a random environment.
  • Developed and compared three distinct neural network approaches for action selection and weight updates.

Main Results:

  • The study presents simulation results demonstrating the feasibility of the proposed methods.
  • The most general method uses network output to determine action probabilities, with weights updated via environmental response.
  • Modifications were introduced to enhance the practical viability of the general method.

Conclusions:

  • All proposed neural network methods are feasible for associative learning and decision-making.
  • The choice of method depends on desired accuracy and available computational resources.
  • The research extends to decentralized decision-making frameworks within a context space.