Dynamics and control of human cognition and behavior for mental health applications

Understanding and predicting maladaptive human behavior and cognition, which are pertinent to many psychiatric disorders, requires understanding its underlying dynamics and dynamical mechanisms. Modifying these behaviors requires control over these dynamics. My research group focuses on inferring the dynamics underlying human behavioral and neural time series, utilizing both process-driven and data-driven modeling approaches. Based on these methods, we develop mental health applications where dynamics are used to predict or control future system states or to study their underlying generative mechanisms.
Our applications include: 1) Smartphone apps that assess psychological ratings as proxies for mental health states, forecast changes over time, and use these forecasts to tailor mental health exercises presented on the smartphone. 2) Social exchange games where we create human-like agents that can engage in social interactions and foster positive social experiences. 3) Web-based cognitive experimental platforms in which we specifically tailor experimental paradigms to reliably and validly measure complex decision making behavior. And 4) we develop and apply models for the robust and reliable detection of dynamical systems features predictive of psychiatric dysfunction.