Modelling Human Intelligence: A Learning Mechanism

Abstract

We propose a novel, high-level model of human learning and cognition, based on association forming. The model configures any input data stream featuring a high incidence of repetition into an association network whose node clusters represent data ‘concepts’. It relies on the hypothesis that, irrespective of the high parallelism of the neural structures involved in cognitive processes taking place in the brain cortex, the channel through which the information is conveyed from the real world environment to its final location (in whatever form of neural structure) can transmit only one data item per time unit. Several experiments are performed on the ability of the resulting system to reconstruct a given underlying ‘world graph’ of concepts and to form and eventually maintain a stable, long term core of memory that we call ‘semantic’ memory. The existence of discontinuous, first order phase transitions in the dynamics of the system is supported with experiments. Results on clustering and association are shown as well.