Applications of Instance-Based Learning Theory: Using the SpeedyIBL Library to Construct Computational Models.

Many decisions people face in their lives are made in the context of dynamic environments. Understanding how humans learn to make these decisions and being able to predict their choices is a challenging and important goal of computational cognitive science. This tutorial provides an introduction to Instance-Based Learning Theory (IBLT), a theory of how humans make decisions based on experience in dynamic situations. The mechanisms proposed in IBLT can be implemented computationally in models that can be applied to a variety of tasks. In this tutorial, we introduce IBLT and its implementation in the open-source library, SpeedyIBL. We demonstrate the capabilities of this library through two hands-on exercises, the Binary Choice Task and the Iowa Gambling Task. Participants will implement IBL models and explore how these cognitive models predict human decisions in increasingly complex environments.