Personality, Mental Health, and AI Research Lab
Advancing psychological science in three areas
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We use behaviors such as language, speech, movement, and facial expressions to develop AI tools for personality and mental health assessment.
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We examine relationships between personality and health across time and change in personality and mental health across time, including through therapy.
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We study how people see their own personality and mental health, how they are perceived by others, and and how these perspectives differ.
Artificial Intelligence
Harnessing machine learning to improve psychological assessment
We use machine learning to recognize nuanced behaviors indicative of certain traits or symptoms. These tools can then recognize personality and mental health in clinical and other settings, saving time and resources and streamlining behavioral detection of personality and mental health.
In general, knowledge of mental illnesses such as depression and PTSD is limited. This improvement of assessment methods can help us learn more about these disorders, better understand them, and improve our treatment of them.
Personality and Mental Health
Using science to understand personality and health
We use the five-factor model (FFM) of personality as a framework for understanding personality. The FFM has a strong scientific base and was developed from the English dictionary. The FFM personality traits overlap with mental health and have important effects on other life outcomes such as physical health. A goal of the lab is to improve the assessment of personality to uncover the causal relationships between personality and health.
Commitment to Diversity and Equity
Developing psychological science in the context of group differences
The development of psychological assessments and artificial intelligence has a history of bias and inequity. A primary goal of the Personality, Mental Health, and AI lab is to develop new behavioral assessments from a wide array of people with diverse backgrounds in order to understand group differences and remove bias in assessment from the beginning.
