Much of philosophy is concerned with the specification of concepts, whether abstract (like numbers), empirical (like inflammation) or normative (like justice). Methodologically, this work often relies on intuitions, thought experiments, linguistic data and appeals to domain specialists.

In this talk, Javier Gomez-Lavin uses the case of “working memory,” a central concept in cognitive psychology that explains how we keep information in mind, to show how machine learning methods can support this project. Specifically, he uses latent topic modeling to map how different literatures treat a concept, making it easier to track variation over time and across disciplines and ideally to clarify points of interdisciplinary convergence and disagreement.

Javier Gomez-Lavin is an assistant professor with the Department of Philosophy at Purdue University. He is the director of the College of Liberal Arts’ recently launched Virtual Reality and Artificial Intelligence (VRAI) Lab and the associated Purdue Normativity and Cognitions (PuNCs) lab. His interdisciplinary work blends methods from experimental social psychology and the analytical philosophy of science to detail the multifaceted ways in which cognition both shapes and is shaped by our social, aesthetic and moral worlds.