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DOI: 10.1111/j.1551-6709.2011.01188.x

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Cognitive Science 35, 7 (2011) 1352-1389

MDLChunker: A MDL-Based Cognitive Model of Inductive Learning

Vivien Robinet (  Corresponding author ) 1, Benoît Lemaire 2, Mirta B. Gordon 3

(2011-09-01)

This paper presents a computational model of the way humans inductively identify and aggregate concepts from the low-level stimuli they are exposed to. Based on the idea that humans tend to select the simplest structures, it implements a dynamic hierarchical chunking mechanism in which the decision whether to create a new chunk is based on an information-theoretic criterion, the Minimum Description Length (MDL) principle. We present theoretical justifications for this approach together with results of an experiment in which participants, exposed to meaningless symbols, have been implicitly encouraged to create high-level concepts by grouping them. Results show that the designed model, called hereafter MDLChunker, makes precise quantitative predictions both on the kind of chunks created by the participants and also on the moment at which these creations occur. They suggest that the simplicity principle used to design MDLChunker is particularly efficient to model chunking mechanisms. The main interest of this model over existing ones is that it does not require any adjustable parameter.

1:	Expertise et spatialisation des connaissances en environnement (ESPACE)
	Institut de recherche pour le développement [IRD]
2:	Laboratoire de psychologie et neurocognition (LPNC)
	CNRS : UMR5105 – Université Pierre-Mendès-France - Grenoble II – Université Joseph Fourier - Grenoble I – Université de Savoie
3:	Laboratoire d'Informatique de Grenoble (LIG)
	Université Joseph Fourier - Grenoble I – Institut Polytechnique de Grenoble - Grenoble Institute of Technology – Université Pierre-Mendès-France - Grenoble II – CNRS : UMR5217

Research team: AMA

Subject

:

Cognitive science/Computer science Computer Science/Learning Computer Science/Information Theory and Coding Mathematics/Information Theory

Keyword(s): Computational cognitive modeling – Minimum Description Length – Chunking – Information theory – Machine learning – Speech segmentation – Data compression – Simplicity

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From: Vivien Robinet <>

Submitted on: Monday, 19 September 2011 21:12:20

Updated on: Monday, 15 October 2012 18:58:10