Integration of Human Inference Process Based on a Value Driven Associative Memory

Abstract

Human have two types of inferences: intuitive and logical. In traditional inference studies, the intuitive inference has modeled by a probabilistic method like the Bayesian, and the logical inference has modeled by a symbolic method like the Tree search. There are many studies that relate inference behavior with brain areas, but few have focused on the mechanism of logical inference that can emerge naturally from the brain neural circuit. So, in this study, we assumed that the human inference process should not be divided into intuitive and logical ones, but should be modeled as an operating mode switching of a single distributed neural network. We used an associative memory model for its verification. The intuitive inference function is realized by a combination of a memory association from a current state and an association of the memory state with the value recognition. Then, the logical inference like behavior is realized by repeatedly biasing the gain of the valued memory state found in the intuitive inference process. A computer simulation in a maze search task is conducted, and we confirmed the emergence of the symbolic tree search like inference behavior with pruning of low probability branch from the intuitive like probabilistic inference by the change of the calculation parameter of the same model.