Department of Experimental Psychology, University College London.
Gatsby Computational Neuroscience Unit, University College London.
Psychol Rev. 2017 Apr;124(3):301-338. doi: 10.1037/rev0000061. Epub 2017 Feb 27.
Higher-level cognition depends on the ability to learn models of the world. We can characterize this at the computational level as a structure-learning problem with the goal of best identifying the prevailing causal relationships among a set of relata. However, the computational cost of performing exact Bayesian inference over causal models grows rapidly as the number of relata increases. This implies that the cognitive processes underlying causal learning must be substantially approximate. A powerful class of approximations that focuses on the sequential absorption of successive inputs is captured by the metaphor in philosophy of science, where theory change is cast as a stochastic and gradual process shaped as much by people's limited willingness to abandon their current theory when considering alternatives as by the ground truth they hope to approach. Inspired by this metaphor and by algorithms for approximating Bayesian inference in machine learning, we propose an algorithmic-level model of causal structure learning under which learners represent only a single global hypothesis that they update locally as they gather evidence. We propose a related scheme for understanding how, under these limitations, learners choose informative interventions that manipulate the causal system to help elucidate its workings. We find support for our approach in the analysis of 3 experiments. (PsycINFO Database Record
更高层次的认知依赖于学习世界模型的能力。我们可以在计算层面上把它描述为一个结构学习问题，其目标是最好地识别一组关系项之间普遍存在的因果关系。然而，随着关系项数量的增加，对因果模型进行精确贝叶斯推断的计算成本会迅速增加。这意味着因果学习背后的认知过程必须是实质性的近似。一类强大的近似方法，专注于连续吸收连续输入，被哲学中的隐喻所捕捉，其中理论变化被视为一个随机和渐进的过程，既受到人们在考虑替代方案时放弃当前理论的有限意愿的影响，也受到他们希望接近的事实真相的影响。受这一隐喻和机器学习中用于近似贝叶斯推断的算法的启发，我们提出了一个算法层面的因果结构学习模型，在这个模型中，学习者只表示一个单一的全局假设，他们在收集证据时局部更新这个假设。我们提出了一个相关的方案，来理解在这些限制下，学习者如何选择有信息量的干预措施来操纵因果系统，以帮助阐明其工作原理。我们在 3 个实验的分析中找到了对我们方法的支持。
