The logical primitives of thought: Empirical foundations for compositional cognitive models.

TLDR

The compositional language of thought has been central to computational cognition, yet its primitive components are usually assumed rather than empirically determined. The study aims to show how different sets of LOT primitives predict distinct learning curves and to design large‑scale experiments that identify the most likely primitives for Boolean connectives and quantification. Using a psychologically realistic approximate Bayesian inference framework, the authors model rule‑based concept learning and design experiments to test these predictions. Results indicate that subjects’ inferences align with a rich set of Boolean operations, including first‑order but not second‑order quantification, demonstrating that specific LOT theories can be empirically distinguished. PsycINFO database record.

Abstract

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