Machine Learning for Computational Psychology

Abstract

Advances in sensing and imaging have provided psychology researchers new tools to understand how the brain creates the mind and simultaneously revealed the need for a new paradigm of mind-brain correspondence-- a set of basic theoretical tenets and an overhauled methodology. I develop machine learning methods to overcome three initial technical barriers to application of the new paradigm. I assess candidate solutions to these problems using two test datasets representing different areas of psychology: the first aiming to build more objective Post-Traumatic Stress Disorder(PTSD) diagnostic tools using virtual reality and peripheral physiology, the second aiming to verify theoretical tenets of the new paradigm in a study of basic affect using functional Magnetic Resonance Imaging(fMRI). Specifically I address three technical challenges: assessing performance in small, real datasets through stability; learning from labels of varying quality; and probabilistic representations of dynamical systems.