#  Current Projects 

 



       ![Current Projects](/sites/g/files/omnuum13121/files/styles/hwp_21_9__1920x825/public/2026-11/MC_WebsiteGraphics_Cropped_Current%20Porjects.png?itok=4ZERB5Kh) 

 

 



 

 



 

Students in the EPIC lab are encouraged to develop independent research projects while making use of a wide range of data we have collected previously. Additionally, we have access to a 32-channel BioSemi system, a VR device with eye-tracking features, stationary eye-tracking, and MindWare peripheral physiology system (ICG, ECG, EMG, EDA, stimulation), and ambulatory physiology (Nervi vest, Garmin).

**Putting emotion regulation in context**

Across several ecological momentary assessment (EMA) studies with participants from different cultures, with varying clinical presentations and trauma exposures, and completing additional behavioral and physiological assessments, we examine how cultural, developmental, and situational contexts shape the use and function of emotion regulation, and we model flexible regulatory behavior across time.

Using these data, we are increasingly interested in using computational methods to understand emotion regulation processes. First, we use Monte Carlo simulations to estimate how sampling quantity and quality shapes how emotion regulation count and variability are linked to affective outcomes, answering "is the more and greater variability the merrier?" Second, we ask whether, when people repeat or change strategies in daily life, they are learning from the affective outcomes of prior regulation or following the accumulated history of recent strategy use. Third, we have recently started to build theory-driven models of emotion regulation as a transdiagnostic process in the emergence of distress disorders. For example, does the short-term affective relief from disengaging regulation shape, over time, how strongly and quickly stress becomes linked to disengaging behavior in ways that explain psychopathology? Finally, we leverage individual participant data meta analysis of EMA studies of emotion regulation to better characterize the strength and variability of the relationships between emotion regulation processes and momentary affective outcomes.

**Linguistic and physiological markers of emotion processing**

We are developing a lab-wide pipeline that processes audio and text data from three sources: open-text descriptions of daily situations from EMA, speech recorded during standardized laboratory stressors and emotion regulation conversations, and interview recording of minority stress experiences and emotion regulation from sexual and gender minority young adults. From extracted acoustic and linguistic features, we ask whether these features can be used to understand information processing bias (e.g., self-normative situational discrepancies), schema, physiological stress reactivity, and emotion regulation. For instance, a lab study combining virtual reality, eye tracking, and cardiac psychophysiology examines how people shift attention across evaluators with different attitudes and how this shapes beliefs, emotion, and physiology. A BBRF-funded study tests autonomic mechanisms of emotion regulation flexibility in depression, and a preregistered meta-analysis examines contextual influences on autonomic physiology.

**Culture and emotion regulation**

We study how culture shapes which strategies people use, how well they work, and how closely strategy choice corresponds with the situation people are in. Current projects include cross-cultural EMA studies in the United States and China, spatial meta-analyses examining how culture shapes emotion regulation across development as well as resilience prevalence and mechanisms. In addition, through an ongoing collaboration we are mapping local emotion norms as a form of regional culture to test whether norms shape emotional expression and regulation.

**Digital technology, emotion learning and regulation, and psychopathology**

We have recently begun to study how social media and generative AI are changing how young people learn about and regulate emotion. In a lab-wide project, we are using accelerated longitudinal measurement-burst design with wearables and digital phenotyping to follow how people manage their feelings in everyday life on their own, with others, or via AI companions. The goal is to illustrate the short- and long-term consequences toward a more contextualized understanding of the risk and opportunity of using AI for emotion regulation.





 

 

 

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