Source Themes

Integrating Trait and Neurocognitive Mechanisms of Externalizing Psychopathology: A Joint Modeling Framework for Measuring Impulsive Behavior
Dissertation is done wooooo!
Rapid, precise, and reliable measurement of delay discounting using a Bayesian learning algorithm
We show how Bayesian adaptive design optimization can improve measurement precision in behavioral tasks.
Moving beyond Ordinary Factor Analysis in Studies of Personality and Personality Disorder: A Computational Modeling Perspective
We discuss how computational modeling in combination with Bayesian analysis can advance the study of psychopathology in ways not afforded by the traditional theoretical and measurement models used throughout clinical psychology.
Anxiety Modulates Preference for Immediate Rewards among Trait-Impulsive Individuals: A Hierarchical Bayesian Analysis
We used hierarchical Bayesian analysis to identify determine how impulsivity and anxiety jointly determine impulsive behavior in the context of a delay discounting task.
A computational model of the Cambridge gambling task with applications to substance use disorders
We developed a novel model for the CGT and used it to investigate differences in decision mechanisms between groups of people with versus without substance use disorders.
Delay Discounting of Protected Sex: Relationship Type and Sexual Orientation Influence Sexual Risk Behavior
We conducted an experiment with a sexual discounting task to investigate differences in willingness to have immediate, unprotected versus delayed, protected sex in people identifying as straight or gay—we also looked at how such effects were modulated by relationship status.
Functionalist and Constructionist Perspectives on Emo­ tion Dysregulation
We review different perspectives on emotion dysregulation, and its relationship to emotion regulation.
Using automated computer vision and machine learning to code facial expressions of affect and arousal: Implications for emotion dysregulation research
We review the importance of facial expression recognition and perception acros development, and demonstrate how machine learning can help automate facial expression coding in research settings.
Using computer-vision and machine learning to automate facial coding of positive and negative affect intensity
We use computer vision and machine learning model to detect positive and negative facial affect intensity with accuracy comparable to trained human coders.
The Outcome‐Representation Learning Model: A Novel Reinforcement Learning Model of the Iowa Gambling Task
We developed a novel computational model for the IGT and used it to investigate differences in learning mechanisms between groups with versus without substance use disorders.