INSERM and CNRS, Paris.
J Cogn Neurosci. 1993 Fall;5(4):390-407. doi: 10.1162/jocn.1993.5.4.390.
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.
摘要 尽管人类婴儿和一些动物物种没有语言能力，但它们具有一些基本的数值处理能力。这些能力包括识别视觉或听觉呈现的特定数量的能力，以及在发展的后期阶段，比较两个数量并确定哪个更大的能力。我们提出了一个在正式神经元网络中发展这些能力的模型。最初，该模型仅配备无序的数量探测器。因此，它可以检测输入集的数量，并可以根据需要进行反应。在后期，添加短期记忆网络被证明足以发展数量比较能力。我们的计算机模拟解释了数值域中的几个现象，包括距离效应和数字的费希纳定律。它们还表明，无需假设婴儿可以计数，就可以解释婴儿的数量检测能力。讨论了模型关键组件的神经生物学基础。
