Abstract

Numeracy is regarded as an emergent property of the human brain, suggesting that neural network based simulations may provide some insight into the “cerebral substrate” used in operations related to numeracy. Two operations, subitization – the so-called phenomenon of the discrimination of visual number – and counting – a recurrent operation – have been studied within a multi-net framework. A multi-net architecture comprising unsupervised networks has been developed which successfully simulates aspects of subitization, especially when compared to similar work using supervised learning algorithms. Another multi-net architecture comprising unsupervised networks, and a recurrent backpropagation network, appears to learn numerosity and successfully simulates errors children make when they are learning to count. The systems for subitizing and counting were incorporated into a gated multi-net system for simulating the dual existence of both subitization and counting. Multi-net architectures provide a good basis for studying the emergent properties of an intelligent system in that a single monolithic network may be used to fit almost any data available.

Citations