Schmajuk N A, Thieme A D
Department of Psychology, Northwestern University, Evanston, IL 60201.
Biol Cybern. 1992;67(2):165-74. doi: 10.1007/BF00201023.
This study presents a real-time, biologically plausible neural network approach to purposive behavior and cognitive mapping. The system is composed of (a) an action system, consisting of a goal-seeking neural mechanism controlled by a motivational system; and (b) a cognitive system, involving a neural cognitive map. The goal-seeking mechanism displays exploratory behavior until either (a) the goal is found or (b) an adequate prediction of the goal is generated. The cognitive map built by the network is a topological map, i.e., it represents only the adjacency, but not distances or directions, between places. The network has recurrent and non-recurrent properties that allow the reading of the cognitive map without modifying it. Two types of predictions are introduced: fast-time and real-time predictions. Fast-time predictions are produced in advance of what occurs in real time, when the information stored in the cognitive map is used to predict the remote future. Real-time predictions are generated simultaneously with the occurrence of environmental events, when the information stored in the cognitive map is being updated. Computer simulations show that the network successfully describes latent learning and detour behavior in rats. In addition, simulations demonstrate that the network can be applied to problem-solving paradigms such as the Tower of Hanoi puzzle.
本研究提出了一种用于目的性行为和认知地图构建的实时、具有生物学合理性的神经网络方法。该系统由两部分组成：（a）一个行动系统，它由一个受动机系统控制的目标寻求神经机制组成；（b）一个认知系统，它涉及一个神经认知地图。目标寻求机制会表现出探索行为，直到出现以下两种情况之一：（a）找到目标；（b）生成对目标的充分预测。由该网络构建的认知地图是一种拓扑地图，即它仅表示地点之间的邻接关系，而不表示距离或方向。该网络具有循环和非循环特性，这使得在不修改认知地图的情况下能够读取它。引入了两种类型的预测：快速时间预测和实时预测。快速时间预测是在实时事件发生之前，当利用存储在认知地图中的信息来预测遥远未来时产生的。实时预测是在环境事件发生的同时生成的，此时存储在认知地图中的信息正在更新。计算机模拟表明，该网络成功地描述了大鼠的潜在学习和迂回行为。此外，模拟还表明，该网络可应用于诸如河内塔谜题等问题解决范式。
