Development of Elementary Numerical Abilities: A Neuronal Model

TLDR

Human infants and several animal species can detect numerosity and later compare quantities, indicating elementary numerical processing independent of language. The study proposes a formal neuronal network model to explain the development of these elementary numerical abilities. The model begins with unordered numerosity detectors that identify input set size, and later incorporates a short‑term memory module that enables number comparison. Simulations reproduce the distance effect and Fechner's law, and show that infants' numerosity detection can arise without counting.

Abstract

Abstract Despite their lack of language, human infants and several animal species possess some elementary abilities for numerical processing. These include the ability to recognize that a given numerosity is being presented visually or auditorily, and, at a later stage of development, the ability to compare two nume-rosities and to decide which is larger. We propose a model for the development of these abilities in a formal neuronal network. Initially, the model is equipped only with unordered numerosity detectors. It can therefore detect the numerosity of an input set and can be conditioned to react accordingly. In a later stage, the addition of a short-term memory network is shown to be sufficient for number comparison abilities to develop. Our computer simulations account for several phenomena in the numerical domain, including the distance effect and Fechner's law for numbers. They also demonstrate that infants' numerosity detection abilities may be explained without assuming that infants can count. The neurobiological bases of the critical components of the model are discussed.