David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Ezio Di Nucci
Jack Alan Reynolds
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Topics in Cognitive Science 3 (1):140-153 (2011)
Inductive reasoning is a fundamental and complex aspect of human intelligence. In particular, how do subjects, given a set of particular examples, generate general descriptions of the rules governing that set? We present a biologically plausible method for accomplishing this task and implement it in a spiking neuron model. We demonstrate the success of this model by applying it to the problem domain of Raven's Progressive Matrices, a widely used tool in the field of intelligence testing. The model is able to generate the rules necessary to correctly solve Raven's items, as well as recreate many of the experimental effects observed in human subjects
|Keywords||Raven's Progressive Matrices Rule generation Vector Symbolic Architectures Neural Engineering Framework Inductive reasoning Realistic neural modeling Cognitive modeling Fluid intelligence|
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References found in this work BETA
Tony A. Plate (2003). Holographic Reduced Representation: Distributed Representation for Cognitive Structures. Center for the Study of Language and Information.
Citations of this work BETA
Chris Eliasmith (2012). The Complex Systems Approach: Rhetoric or Revolution. Topics in Cognitive Science 4 (1):72-77.
Timothy T. Rogers & James L. McClelland (2014). Parallel Distributed Processing at 25: Further Explorations in the Microstructure of Cognition. Cognitive Science 38 (6):1024-1077.
Peter Blouw, Eugene Solodkin, Paul Thagard & Chris Eliasmith (2015). Concepts as Semantic Pointers: A Framework and Computational Model. Cognitive Science 40 (1):n/a-n/a.
Jean-Frédéric de Pasquale & Pierre Poirier (2016). Convolution and Modal Representations in Thagard and Stewart’s Neural Theory of Creativity: A Critical Analysis. Synthese 193 (5):1535-1560.
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