Ashley Danielle Brown
Graduate Student in Personality/Health Psychology
Department of Psychology
Northwestern University
Evanston, Illinois, USA 60208

office: 109 Cresap Laboratory
telephone: 847-491-4515


B.S. in Physics and Mathematics, Marshall University, 1999-2004
M.S. in Physics, B.S. in Psychology, The University of Kentucky, 2004-2007, 2009-2011
Ph.D. Student in Personality/Health Psychology Northwestern University, 2011-present

Research interests

As a personality psychologist with an extensive background in physics, I have long been interested in what might be termed psychological Grand Unified Theories (GUTs). To that end, my research focuses on psychometrics and computational models of personality, with special emphasis on individual differences in traits and affective experience. Most recently, I have designed and am validating a simulation that blends elements of Jeffrey Gray’s Reinforcement Sensitivity Theory and Revelle and Condon’s Cues-Tendencies-Actions model.

Psychometrics and Scale Development

My interest in building integrative models (i.e. GUTs) of personality has guided my choice of research collaborators. During my undergraduate career, I worked with Thomas Widiger to develop a self-report measure of obsessive-compulsive personality disorder based on the Five-Factor Model of personality traits. Later, I chose William Revelle as my PhD adviser not only because of his extensive experience in quantitative and computational approaches to personality, but also because he has long been a champion of the idea that personality is the "last refuge of the generalist in psychology." During my first year at Northwestern, I used data collected on Dr. Revelle's SAPA Project website to investigate the relationships between personality traits, intelligence, and response styles on various tests of cognitive ability.

Psychometrics and Computational Modeling

Integrating disparate lines of research both within personality psychology (e.g. traits, abilities, interests) and between personality psychology and other subdisciplines (e.g. social, cognitive, clinical) requires rigorous attention to psychometric data. My statistical computational modeling research with William Revelle uses simulated SAPA data (which are Massively Missing Completely at Random) in order to pit various analytic techniques against each other; our results indicate that SAPA's simple available-case correlations are superior to those obtained using full-information maximum likelihood estimation.

Computational Modeling, Affect, and Personality

There's strong evidence to suggest that personality influences individuals' risk for unfavourable life outcomes like disease and incarceration; however, in order to better intervene in such problems, psychologists must understand personality traits more precisely. Because computational modeling has such great potential to lend much-needed precision to the study of personality, I have developed a program in R, "CTA-RST," that simulates behavior and emotion in accordance with the predictions of Reinforcement Sensitivity Theory. I am currently validating CTA-RST by using it to model archival data.

Selected publications and presentations