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A Neural Model of Rule Generation in Inductive Reasoning
Author(s) -
Rasmussen Daniel,
Eliasmith Chris
Publication year - 2011
Publication title -
topics in cognitive science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.191
H-Index - 56
eISSN - 1756-8765
pISSN - 1756-8757
DOI - 10.1111/j.1756-8765.2010.01127.x
Subject(s) - inductive reasoning , computer science , artificial intelligence , set (abstract data type) , task (project management) , human intelligence , raven's progressive matrices , domain (mathematical analysis) , field (mathematics) , rule based system , machine learning , cognition , mathematics , psychology , mathematical analysis , management , neuroscience , pure mathematics , economics , programming language
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.