Polk Thad A, Simen Patrick, Lewis Richard L, Freedman Eric
Department of Psychology, University of Michigan, 525 E. University, Ann Arbor, MI 48109-1109, USA.
Brain Res Cogn Brain Res. 2002 Dec;15(1):71-83. doi: 10.1016/s0926-6410(02)00217-3.
Cognitive deficits associated with dorsolateral prefrontal cortex (DLPFC) damage are often most apparent in higher cognitive tasks that involve problem solving and managing multiple goals. However, computational models of prefrontal deficits on such tasks are difficult to construct. Problem solving is most naturally modeled with symbolic systems (e.g. production systems), but the effects of lesions are most naturally modeled with subsymbolic systems (neural networks). We show that when we adopt a simple and plausible model of neural computation, there is a natural and explicit mapping from symbolic, goal-driven cognition onto neural computation. We exploit this mapping to construct a neural network model that is capable of solving complex problems in the Tower of London task. The model leads to a specific hypothesis about the role of DLPFC in such tasks, namely, that DLPFC represents internally generated subgoals that modulate competition among posterior representations. When intact, the model accurately simulates the behavior of college students even on the most difficult problems. Furthermore, when the subgoal component is lesioned, it accurately simulates the behavior of prefrontal patients, including the fact that their deficits are most apparent on the most difficult tasks and that they have special difficulty with tasks that require inhibiting a prepotent response.
与背外侧前额叶皮质（DLPFC）损伤相关的认知缺陷，在涉及问题解决和管理多个目标的高级认知任务中往往最为明显。然而，针对此类任务构建前额叶缺陷的计算模型却很困难。问题解决最自然的建模方式是使用符号系统（例如产生式系统），但损伤的影响最自然的建模方式是使用亚符号系统（神经网络）。我们表明，当采用一种简单且合理的神经计算模型时，从符号化的、目标驱动的认知到神经计算存在一种自然且明确的映射。我们利用这种映射构建了一个能够解决伦敦塔任务中复杂问题的神经网络模型。该模型得出了一个关于DLPFC在此类任务中作用的具体假设，即DLPFC代表内部生成的子目标，这些子目标调节后部表征之间的竞争。在完整状态下，该模型甚至能准确模拟大学生在最困难问题上的行为。此外，当子目标组件受损时，它能准确模拟前额叶患者的行为，包括他们的缺陷在最困难任务中最为明显，以及他们在需要抑制优势反应的任务上有特殊困难这一事实。
