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Related Concept Videos

Associative Learning01:27

Associative Learning

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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.
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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...
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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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Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
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In general, a schema is a mental construct consisting of a cluster or collection of related concepts (Bartlett, 1932). There are many different types of schemata, and they all have one thing in common: schemata are a method of organizing information that allows the brain to work more efficiently. When a schema is activated, the brain makes immediate assumptions about the person or object being observed.
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A schema is a mental construct that organizes related concepts, allowing the brain to process information efficiently. Upon activation, schemata facilitate assumptions about people or objects.
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Related Experiment Video

Updated: Apr 22, 2026

Morphology-Based Distinction Between Healthy and Pathological Cells Utilizing Fourier Transforms and Self-Organizing Maps
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Cooperation-controlled learning for explicit class structure in self-organizing maps.

Ryotaro Kamimura1

  • 1IT Education Center and School of Science and Technology, 1117 Kitakaname, Hiratsuka, Kanagawa 259-1292, Japan.

Thescientificworldjournal
|October 14, 2014
PubMed
Summary
This summary is machine-generated.

Cooperation-controlled learning enhances neural network analysis by distinguishing individual and collective neuron roles. This novel information-theoretic method improves class structure discovery compared to traditional self-organizing maps.

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Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Computational Neuroscience

Background:

  • Neural networks process information through interconnected neurons.
  • Understanding neuron behavior, both individually and collectively, is crucial for network performance.
  • Existing methods like self-organizing maps have limitations in revealing explicit class structures.

Purpose of the Study:

  • To introduce a novel information-theoretic method called cooperation-controlled learning.
  • To demonstrate the effectiveness of considering multiple points of view for neurons.
  • To improve the clarity of class boundaries in machine learning datasets.

Main Methods:

  • Developed cooperation-controlled learning by distinguishing individual and collective neurons.
  • Implemented two networks: cooperative and uncooperative, controlled by a cooperation parameter.
  • Applied the method to automobile and housing datasets, analyzing class structure.

Main Results:

  • Cooperation-controlled learning successfully produced clearer class structures.
  • The method demonstrated superiority over conventional self-organizing maps.
  • Adjusting the cooperation parameter effectively controlled the dominance of individual versus collective neuron characteristics.

Conclusions:

  • Cooperation-controlled learning offers a new perspective on neural network analysis.
  • This method effectively enhances the interpretability of class structures in data.
  • Considering input unit information within this framework further refines class discovery.