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Dutcher, J.

Publications and source records attributed to Dutcher, J..

2 recordsLinked to original sources

Frontostriatal Functional Connectivity Underlies Self-Enhancement During Social Evaluation

Self-enhancement, the tendency to view oneself positively, is a pervasive social motive widely investigated in social and personality psychology. Despite research on the topic over the past several decades, relatively little is known about the neurocognitive mechanisms underlying this motive, specifically in social evaluative situations. To investigate whether positive emotion regulation circuitry, neural circuitry involved in modulating positive affect, relates to the operation of the self-enhancement motive in social contexts, we conducted an fMRI study in a sample of healthy young adults. We hypothesized that self-enhancement indices (state, trait self-esteem) relate to greater positive functional connectivity between right ventrolateral prefrontal cortex (RVLPFC), a neural region implicated in emotion regulation, and the ventral striatum (VS), a region associated with reward-related affect, during a social feedback task. Following social-evaluative feedback, participants maintained stable self-esteem or experienced a drop in state self-esteem. We found stable state self-esteem and higher trait self-esteem related to greater positive connectivity in RVLFPC-VS circuitry during receipt of positive (vs. neutral) feedback. These findings implicate the neurocognitive mechanisms underlying emotion regulation in the functioning of the self-enhancement motive and highlight a pathway through which self-enhancement may restore feelings of self-worth during threatening circumstances.

neuroscience↗

CoRhythMo: A Computational Framework for Modeling Biobehavioral Rhythms from Mobile and Wearable Data Streams

This paper presents CoRhythMo, the first computational framework for modeling biobehavioral rhythms - the repeating cycles of physiological, psychological, social, and environmental events - from mobile and wearable data streams. The framework incorporates four main components: mobile data processing, rhythm discovery, rhythm modeling, and machine learning. We use a dataset of smartphone and Fitbit data collected from 138 college students over a semester to evaluate the frameworks ability to 1) model biobehavioral rhythms of students, 2) measure the stability of their rhythms over the course of the semester, 3) model differences between rhythms of students with different health status, and 4) predict the mental health status in students using the model of their biobehavioral rhythms. Our evaluation provides evidence for the feasibility of using CoRhythMo for modeling and discovering human rhythms and using them to assess and predict different life and health outcomes.

bioinformatics↗