Modeling Stimulus Equivalence with Multi Layered Neural Networks
Prior studies showed that stimulus equivalence did not emerge in nonhuman and it may be what distinguish humans from non-humans. We think that stimulus equivalence is the origin of human fs illogical reasoning.
For applying neural networks to stimulu equivalence, a problem of missing input features and self-supervised learning must be solved. In this paper, we propose a neural network model based on the iterative inversion method which has a potential possibility to explain the stimulus equivalence and demonstrated the validity of the proposed model by computer simulations. Furthermore, it was discussed that the proposed model was an appropriate model of symmetry for human reasoning.
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