工作记忆、心理计算和心理定势的非经典符号理论
A nonclassical symbolic theory of working memory, mental computations, and mental set
摘要：
本文探讨了与人脑作为一个学习系统相关的四个基本问题。大脑如何学习：（1）在精神上模拟不同的外部记忆辅助工具;（2）在原则上，使用想象的记忆辅助工具进行任何精神计算;（3）回忆真实的感觉和运动事件，并合成一定数量的想象事件;（4）动态地改变其心理设置，以匹配一定数量的背景组合？基于人类新皮层以“非经典”方式处理符号信息这一普遍假设，我们对（1）-（4）提出了一个统一的答案。它不像经典的符号系统那样操纵读写记忆中的符号，而是操纵动态记忆的状态，这些状态代表了存储在长期记忆中的不可移动的符号结构的不同暂时属性。该方法被形式化为E-machine的概念。直观地说，E机是一个系统，它主要处理代表内存指针子集的特征函数，而不是指针本身。这种非经典的符号范式是图灵普适的，并且与经典范式不同，它可以有效地在同质神经网络中实现，其时间调制拓扑结构类似于新皮层。
英文摘要：
The paper tackles four basic questions associated with human brain as a learning system. How can the brain learn to (1) mentally simulate different external memory aids, (2) perform, in principle, any mental computations using imaginary memory aids, (3) recall the real sensory and motor events and synthesize a combinatorial number of imaginary events, (4) dynamically change its mental set to match a combinatorial number of contexts? We propose a uniform answer to (1)-(4) based on the general postulate that the human neocortex processes symbolic information in a "nonclassical" way. Instead of manipulating symbols in a read/write memory, as the classical symbolic systems do, it manipulates the states of dynamical memory representing different temporary attributes of immovable symbolic structures stored in a long-term memory. The approach is formalized as the concept of E-machine. Intuitively, an E-machine is a system that deals mainly with characteristic functions representing subsets of memory pointers rather than the pointers themselves. This nonclassical symbolic paradigm is Turing universal, and, unlike the classical one, is efficiently implementable in homogeneous neural networks with temporal modulation topologically resembling that of the neocortex.
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