论文标题
从隐性学习到明确表示
From implicit learning to explicit representations
论文作者
论文摘要
使用储层计算框架，我们演示了一个简单的模型如何在没有明确工作内存的情况下解决交替任务。为此，一个配备了传感器的简单机器人在8形迷宫内导航，并在迷宫中的同一交叉路口左右旋转。对模型内部活动的分析表明，内存实际上是在网络动力学内部编码的。但是，这种动态的工作记忆无法访问，例如将行为偏置到两个吸引子之一（左右）之一。为此，外部提示被馈送到机器人中，以便可以遵循提示指示的任意序列。该模型强调了可以解散程序学习及其内部表示形式的观念。如果前者允许产生行为，则不足以进行显式和细粒度的操作。
Using the reservoir computing framework, we demonstrate how a simple model can solve an alternation task without an explicit working memory. To do so, a simple bot equipped with sensors navigates inside a 8-shaped maze and turns alternatively right and left at the same intersection in the maze. The analysis of the model's internal activity reveals that the memory is actually encoded inside the dynamics of the network. However, such dynamic working memory is not accessible such as to bias the behavior into one of the two attractors (left and right). To do so, external cues are fed to the bot such that it can follow arbitrary sequences, instructed by the cue. This model highlights the idea that procedural learning and its internal representation can be dissociated. If the former allows to produce behavior, it is not sufficient to allow for an explicit and fine-grained manipulation.
