Composing intuitive theories across domains and in real time

Abstract

This dissertation investigates the computational mechanisms that enable people to flexibly and efficiently reason about the world. Building on the framework of intuitive theories as generative programs, I address two fundamental questions about cognitive architecture: (1) how do distinct intuitive theories communicate with each other, and (2) how can these theories be executed efficiently? Using intuitive physics as a primary case study, I present three empirical investigations and two computational models that contribute to our understanding of these questions. In Chapter 1, I examine how intuitive physics and intuitive psychology interact in moral and causal reasoning. Through computational modeling and behavioral experiments involving visual vignettes of harmful actions, I demonstrate that moral judgments can be modeled as linear combinations of features extracted from both intuitive physics and intuitive psychology. This work provides a computational account of how separate cognitive systems coordinate to solve problems spanning multiple domains. Chapters 2 and 3 investigate a phenomenon I term ``blended reasoning'', wherein people achieve computational efficiency by flexibly combining simulation-based and abstraction-based reasoning strategies in real time. Using novel computational and empirical methods, including eye-tracking, I show that people strategically arbitrate between different reasoning approaches based on observable features of physical reasoning tasks. This blended approach enables rapid physical reasoning at the cost of accuracy. Together, these studies advance our understanding of cognitive architecture by revealing how intuitive theories interact and how computational resources are efficiently allocated during reasoning. The findings suggest that human cognition is characterized not only by rich, structured knowledge representations but also by strategic mechanisms for integrating and deploying this knowledge.