A Neural Model of Rule Generation in Inductive Reasoning

• Published in: Topics in cognitive science Vol. 3; no. 1; pp. 140 - 153
• Main Authors: Rasmussen, Daniel, Eliasmith, Chris
• Format: Journal Article
• Language: English
• Published: Oxford, UK Blackwell Publishing Ltd 01.01.2011
• Subjects: Cognitive modeling; Fluid intelligence; Humans; Inductive reasoning; Intelligence - physiology; Intelligence Tests; Learning - physiology; Memory - physiology; Models, Neurological; Neural Engineering Framework; Raven's Progressive Matrices; Realistic neural modeling; Rule generation; Thinking - physiology; Vector Symbolic Architectures; Vocabulary; Cognitive modeling; Raven's Progressive Matrices; Realistic neural modeling; Neural Engineering Framework; Inductive reasoning; Vector Symbolic Architectures; Rule generation; Fluid intelligence
• ISSN: 1756-8757; 1756-8765; 1756-8765
• DOI: 10.1111/j.1756-8765.2010.01127.x

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.