Abstract
Quantitative modeling approaches are routinely used in cognitive science to make sense of behavior. Statistical models are designed to test *what* specific patterns are present in behavior, whereas cognitive computational models are developed to describe *how* specific behavioral patterns may emerge from latent cognitive processes. These two types of modeling approaches have successfully identified characteristic (and sometimes suboptimal) features of human learning and decision-making under uncertainty. In this talk, I will argue that cognitive computational models can be used to answer the distinct question of *why* these characteristic features are there. I will use recent studies that rely on different classes of models (low-dimensional algorithmic models, high-dimensional neural networks) to explain characteristic features of human cognition in terms of latent objectives and constraints.
