A Neural Model of Rule Generation in Inductive Reasoning

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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DOI 10.1111/j.1756-8765.2010.01127.x
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