Event Date
Nori Jacoby
Integrating human decisions into computer algorithms using PsyNet
Abstract: Over the past decade, psychology, sociology, and economics research has increasingly leveraged large-scale online participant pools. These platforms enable researchers to expand sample sizes, enhance participant diversity, and conduct experiments that would be nearly impossible in a laboratory setting. By reframing experiment design as algorithm design, we introduce new ways to integrate human decision-making into computational algorithms. Through this approach, we demonstrate how to implement a range of human-in-the-loop algorithms, including sampling methods (e.g., Markov chain Monte Carlo), optimization techniques (e.g., genetic algorithms), and simulation frameworks (e.g., message passing). We begin by introducing PsyNet (www.psynet.dev), an online platform designed to facilitate such experiments. We then explore research projects powered by PsyNet that investigate humans’ high-dimensional internal representations. We illustrate how this technology addresses key challenges, such as characterizing semantic representations, enabling effective semantic mining, and simulating cultural evolution. Additionally, we show how our experimental frameworks immerse participants in virtual worlds and social networks, where we can precisely manipulate the rules governing social interactions. This allows us to study collective behavior at scale, treating society itself as the unit of experimental manipulation. Our approach surpasses traditional agent-based simulations (which lack human participants) and the study of real social networks (which are constrained by ethical and practical limitations on experimental control).
Bio: Nori Jacoby is an assistant professor in the Department of Psychology at Cornell University, where he leads the Cornell Computational Cognition lab (COCOCO; https://www.norijacoby.com/cococo.html), and a Research Group Leader at the Max Planck Institute for Empirical Aesthetics in Frankfurt. His research investigates how our sensory and cognitive abilities are determined by experience and culture. His methodologies employ machine learning techniques such as deep generative synthesis algorithms alongside a significant data-intensive expansion of the scale and scope of experimental research both by means of massive online experiments and fieldwork in locations around the globe. Nori earned his Ph.D. at the Edmond and Lily Safra Center for Brain Sciences (ELSC) at the Hebrew University of Jerusalem, supervised by Naftali Tishby and Merav Ahissar. He then completed postdoctoral research at MIT’s Computational Audition Lab with Josh McDermott, UC Berkeley’s Computational Cognitive Science Lab with Tom Griffiths, and as a Presidential Scholar in Society and Neuroscience at Columbia University.