Abstract
Cognitive modeling methodologies form the groundwork of significant studies in cognitive science. In this work a prototype computational model, called IPSOM, is introduced, which charts the spatial cognitive human behaviour in completing interlocking puzzles. IPSOM is a neural network of the class of self-organizing maps, and has been implemented using an artificial data set that consists of synthesized patterns of puzzle completion. The results show that the model is particularly successful in depicting valid cognitive behavioural patterns with a very high degree of confidence. Based on IPSOM’s performance and structure, it is argued that a scaled-up version of this model could readily be used in representing real-life puzzle-completion patterns.
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Revithis, S., Wilson, W.H., Marcus, N. (2006). IPSOM: A Self-organizing Map Spatial Model of How Humans Complete Interlocking Puzzles. In: Sattar, A., Kang, Bh. (eds) AI 2006: Advances in Artificial Intelligence. AI 2006. Lecture Notes in Computer Science(), vol 4304. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11941439_32
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DOI: https://doi.org/10.1007/11941439_32
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-49787-5
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