Making Predictions Without Data: How an Instance-Based Learning Model Predicts Sequential Decisions in the Balloon Analog Risk Task

Abstract

Many models in Cognitive Science require data to calibrate parameters. Some modelers calibrate their models’ parameters for each individual in a data set, and others work at the aggregate level. Generally, the accuracy of a model is judged by the degree to which human data are replicated, and the model parameters are interpreted accordingly. It is not too surprising that models that are developed for a particular task and fit to each individual’s data in such a task replicate the human data well. The question is, however, whether those models can make predictions in the absence of human data. In this paper, we present a theory-driven model of a well-known sequential decision task (the Balloon Analog Risk Task, BART) which is able to make predictions in the absence of human data. The cognitive model is grounded on the processes and mechanisms of Instance-Based Learning (IBL) Theory of experiential choice. We demonstrate the simulation predictions from an IBL model and those of a well-known model of the BART, which depends on the fits to human data. We further show that when making predictions without data, the IBL model provides predictions that are both theoretically founded and accurate, while the Two-Parameter model performs much worse than when fit to data. We conclude with a discussion of the benefits of making theory-based predictions in the absence of human data for our community.

Date
Aug 29, 2022 11:43 AM — 11:43 AM
Erin H. Bugbee
Erin H. Bugbee
Cognitive Decision Science PhD Student at Carnegie Mellon

I study how humans learn and make sequential decisions from experience, and I do so by building computational cognitive models of human and artificial decision making and through behavioral experimentation.