This tutorial will aim to give the audience a brief methodological survey of the two-way interaction between the study of human behavior and data science. In the first part of the tutorial, I will present an example of how cognitive science research can potentially improve machine learning methods. Specifically, I will describe how human similarity judgments can be used to improve visual representations learned by conventional deep neural networks. In the second, I will present an example of how machine learning tools can be used to achieve a richer scientific understanding of human behavior. Specifically, I will present a hierarchical model of visual information search that yokes multiple parallel stochastic accumulator processes to a TD-learning meta-controller and show how it explains humans' decisions to terminate information search better than existing models in cognitive science.

Nisheeth Sreevastava

Nisheeth teaches Computer Science at IIT Kanpur. He appreciates brevity in biographical sketches.