Machine Learning for Computational Psychology

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.