title: A neural model of rule generation in inductive reasoning.
abstract: 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.
authors.affiliation: Centre for Theoretical Neuroscience, University of Waterloo.
authors.forename: Daniel
authors.initials: D
authors.lastname: Rasmussen
authors.forename: Chris
authors.initials: C
authors.lastname: Eliasmith
mesh_terms: D006801:Humans; D007360:Intelligence / Q000502:physiology*; D007361:Intelligence Tests; D007858:Learning / Q000502:physiology; D008568:Memory / Q000502:physiology; D008959:Models, Neurological; D013850:Thinking / Q000502:physiology*; D014825:Vocabulary
keywords: Cognitive modeling; Fluid intelligence; Inductive reasoning; Neural Engineering Framework; Raven's Progressive Matrices; Realistic neural modeling; Rule generation; Vector Symbolic Architectures