Department of Brain and Cognitive Sciences.
Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology.
Psychol Rev. 2016 Jul;123(4):392-424. doi: 10.1037/a0039980. Epub 2016 Apr 14.
The notion of a compositional language of thought (LOT) has been central in computational accounts of cognition from earliest attempts (Boole, 1854; Fodor, 1975) to the present day (Feldman, 2000; Penn, Holyoak, & Povinelli, 2008; Fodor, 2008; Kemp, 2012; Goodman, Tenenbaum, & Gerstenberg, 2015). Recent modeling work shows how statistical inferences over compositionally structured hypothesis spaces might explain learning and development across a variety of domains. However, the primitive components of such representations are typically assumed a priori by modelers and theoreticians rather than determined empirically. We show how different sets of LOT primitives, embedded in a psychologically realistic approximate Bayesian inference framework, systematically predict distinct learning curves in rule-based concept learning experiments. We use this feature of LOT models to design a set of large-scale concept learning experiments that can determine the most likely primitives for psychological concepts involving Boolean connectives and quantification. Subjects' inferences are most consistent with a rich (nonminimal) set of Boolean operations, including first-order, but not second-order, quantification. Our results more generally show how specific LOT theories can be distinguished empirically. (PsycINFO Database Record
组合思维语言（LOT）的概念在认知计算的早期尝试（布尔，1854 年；福多尔，1975 年）到今天都一直是核心（费尔德曼，2000 年；彭，霍洛约克，和波维内利，2008 年；福多尔，2008 年；坎普，2012 年；古德曼，特南鲍姆，和格斯坦伯格，2015 年）。最近的建模工作表明，在组合结构的假设空间上进行统计推断，如何能够解释各种领域的学习和发展。然而，这些表示的基本组件通常是由建模者和理论家先验地假定的，而不是通过经验确定的。我们展示了在心理上现实的近似贝叶斯推理框架中嵌入的不同 LOT 基本组件如何系统地预测基于规则的概念学习实验中的不同学习曲线。我们利用 LOT 模型的这一特点设计了一组大规模的概念学习实验，可以确定涉及布尔连接词和量化的心理概念的最可能的基本组件。被试的推理与丰富的（非最小的）布尔运算集最一致，包括一阶但不包括二阶量化。我们的结果更广泛地表明了如何通过经验来区分具体的 LOT 理论。
