Donnarumma Francesco, Maisto Domenico, Pezzulo Giovanni
Institute of Cognitive Sciences and Technologies, National Research Council, Rome, Italy.
Institute for High Performance Computing and Networking, National Research Council, Naples, Italy.
PLoS Comput Biol. 2016 Apr 13;12(4):e1004864. doi: 10.1371/journal.pcbi.1004864. eCollection 2016 Apr.
How do humans and other animals face novel problems for which predefined solutions are not available? Human problem solving links to flexible reasoning and inference rather than to slow trial-and-error learning. It has received considerable attention since the early days of cognitive science, giving rise to well known cognitive architectures such as SOAR and ACT-R, but its computational and brain mechanisms remain incompletely known. Furthermore, it is still unclear whether problem solving is a "specialized" domain or module of cognition, in the sense that it requires computations that are fundamentally different from those supporting perception and action systems. Here we advance a novel view of human problem solving as probabilistic inference with subgoaling. In this perspective, key insights from cognitive architectures are retained such as the importance of using subgoals to split problems into subproblems. However, here the underlying computations use probabilistic inference methods analogous to those that are increasingly popular in the study of perception and action systems. To test our model we focus on the widely used Tower of Hanoi (ToH) task, and show that our proposed method can reproduce characteristic idiosyncrasies of human problem solvers: their sensitivity to the "community structure" of the ToH and their difficulties in executing so-called "counterintuitive" movements. Our analysis reveals that subgoals have two key roles in probabilistic inference and problem solving. First, prior beliefs on (likely) useful subgoals carve the problem space and define an implicit metric for the problem at hand-a metric to which humans are sensitive. Second, subgoals are used as waypoints in the probabilistic problem solving inference and permit to find effective solutions that, when unavailable, lead to problem solving deficits. Our study thus suggests that a probabilistic inference scheme enhanced with subgoals provides a comprehensive framework to study problem solving and its deficits.
人类和其他动物如何面对那些没有预定义解决方案的新问题？人类解决问题的方式与灵活的推理和推断相关，而不是缓慢的试错学习。自认知科学早期以来，它就受到了相当多的关注，催生了诸如SOAR和ACT-R等著名的认知架构，但它的计算和大脑机制仍不完全清楚。此外，解决问题是否是一种“专门化”的认知领域或模块，即它是否需要与支持感知和行动系统的计算根本不同的计算，这一点仍不明确。在这里，我们提出了一种关于人类解决问题的新观点，即带有子目标的概率推理。从这个角度来看，保留了认知架构的关键见解，比如使用子目标将问题分解为子问题的重要性。然而，这里的底层计算使用的是类似于在感知和行动系统研究中越来越流行的概率推理方法。为了测试我们的模型，我们聚焦于广泛使用的汉诺塔（ToH）任务，并表明我们提出的方法可以重现人类问题解决者的特征性特质：他们对ToH“社区结构”的敏感性以及执行所谓“违反直觉”动作时的困难。我们的分析表明，子目标在概率推理和问题解决中具有两个关键作用。首先，关于（可能）有用子目标的先验信念划分了问题空间，并为手头的问题定义了一个隐式度量——一种人类敏感的度量。其次，子目标在概率性问题解决推理中用作路径点，并允许找到有效的解决方案，当无法找到时，就会导致问题解决出现缺陷。因此，我们的研究表明，带有子目标增强的概率推理方案为研究问题解决及其缺陷提供了一个全面的框架。
