Cognitive Science 35 (7):1352-1389 (2011)
AbstractThis 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
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References found in this work
The Magical Number Seven, Plus or Minus Two: Some Limits on Our Capacity for Processing Information.George A. Miller - 1956 - Psychological Review 63 (2):81-97.
On Computable Numbers, with an Application to the Entscheidungsproblem.Alan Turing - 1936 - Proceedings of the London Mathematical Society 42 (1):230-265.
The Magical Number Seven, Plus or Minus Two: Some Limits on Our Capacity for Processing Information.George A. Miller - 1956 - Psychological Review 101 (2):343-352.
A Mathematical Theory of Communication.Claude E. Shannon - 1948 - Bell System Technical Journal 27:379–423.
Simplicity: A Unifying Principle in Cognitive Science?Nick Chater & Paul Vitányi - 2003 - Trends in Cognitive Sciences 7 (1):19-22.