The Logical Primitives of Thought: Empirical Foundations for Compositional Cognitive Models

• Published in: Psychological review Vol. 123; no. 4; pp. 392 - 424
• Main Authors: Piantadosi, Steven T., Tenenbaum, Joshua B., Goodman, Noah D.
• Format: Journal Article
• Language: English
• Published: United States American Psychological Association 01-07-2016
• Subjects: Bayes Theorem; Bayesian analysis; Cognition; Cognition & reasoning; Concept Formation; Human; Humans; Language; Learning; Logic; Models, Psychological; Simulation; Statistical Probability; Thinking; concept learning; language of thought; Bayesian modeling
• ISSN: 0033-295X; 1939-1471
• DOI: 10.1037/a0039980

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.