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Related Concept Videos

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...
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...
Inductive Reasoning00:59

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Chunking01:12

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Related Experiment Video

Updated: May 30, 2026

A Flexible Platform for Monitoring Cerebellum-Dependent Sensory Associative Learning
11:32

A Flexible Platform for Monitoring Cerebellum-Dependent Sensory Associative Learning

Published on: January 19, 2022

MDLChunker: a MDL-based cognitive model of inductive learning.

Vivien Robinet1, Benoît Lemaire, Mirta B Gordon

  • 1University of Grenoble. vivien.robinet@inria.fr

Cognitive Science
|August 10, 2011
PubMed
Summary

This study introduces MDLChunker, a computational model that simulates human concept formation using the Minimum Description Length (MDL) principle. The model accurately predicts how people group stimuli into concepts based on simplicity.

Related Experiment Videos

Last Updated: May 30, 2026

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Published on: January 19, 2022

Area of Science:

  • Cognitive Science
  • Computational Neuroscience
  • Artificial Intelligence

Background:

  • Human concept identification involves aggregating low-level stimuli.
  • Existing models often require adjustable parameters for simulating chunking mechanisms.

Purpose of the Study:

  • To present a computational model, MDLChunker, for inductive concept identification.
  • To investigate the role of simplicity in hierarchical chunking.
  • To validate the model's predictions against human experimental data.

Main Methods:

  • Implementation of a dynamic hierarchical chunking mechanism.
  • Application of the Minimum Description Length (MDL) principle as an information-theoretic criterion.
  • Experimental validation using participants exposed to meaningless symbols.

Main Results:

  • MDLChunker accurately predicts the types of chunks formed by participants.
  • The model precisely forecasts the timing of concept creation events.
  • The simplicity principle effectively models human chunking mechanisms.

Conclusions:

  • The MDL principle provides an efficient and parameter-free approach to modeling concept formation.
  • MDLChunker offers a robust computational framework for understanding inductive concept aggregation.
  • The findings suggest simplicity is a key driver in human hierarchical learning.