论文标题
数字比较的神经模型与令人惊讶的鲁棒性概括
A Neural Model of Number Comparison with Surprisingly Robust Generalization
论文作者
论文摘要
我们提出了一个相对简单的计算神经网络比较模型。对整数的比较训练1-9使该模型能够有效，准确地模拟广泛的现象，包括距离和比率效应以及对多位数整数的鲁棒性概括，负数和小数数字。随附的逻辑数字比较模型提供了对数字比较及其与阿拉伯数字系统的关系的进一步见解。这些模型为数字比较的心理学和神经网络有效学习强大的概括性系统的能力提供了合理的基础。
We propose a relatively simple computational neural-network model of number comparison. Training on comparisons of the integers 1-9 enable the model to efficiently and accurately simulate a wide range of phenomena, including distance and ratio effects and robust generalization to multidigit integers, negative numbers, and decimal numbers. An accompanying logical model of number comparison provides further insights into the workings of number comparison and its relation to the Arabic number system. These models provide a rational basis for the psychology of number comparison and the ability of neural networks to efficiently learn a powerful system with robust generalization.
