title: MDLChunker: a MDL-based cognitive model of inductive learning.
abstract: 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.
authors.affiliation: University of Grenoble. vivien.robinet@inria.fr
authors.forename: Vivien
authors.initials: V
authors.lastname: Robinet
authors.forename: Benoît
authors.initials: B
authors.lastname: Lemaire
authors.forename: Mirta B
authors.initials: MB
authors.lastname: Gordon
mesh_terms: D001499:Bayes Theorem; D003198:Computer Simulation; D007257:Information Theory; D007858:Learning