Site Search
People Search
Upcoming Colloquia
Affiliated Departments
Syndication
CNBC Connect
News Archvies
Siegle, Greg J.
Ph.D., San Diego State University /University of California, San Diego
Research Interests
My research is devoted to understanding interactions of emotion and cognition in healthy individuals as well as individuals with affective psychopathology, particularly unipolar depression. Ultimate goals of this research include better understanding individual differences in emotional style as well as the nature of affective disorders, and the development of interventions tailored to account for individual differences in information processing styles. Three constraints help to make this research rigorous and interpretable. First, the theoretical models I adopt must be physiologically motivated, so as to allow integration of psychological and physiological perspectives. Second, implementing analogs of the models as computational neural networks helps to specify theories in an internally consistent manner, i.e. so theoretical conclusions follow from theoretical assumptions and so variables relevant to a rigorous characterization of a theory have not been left out. Finally, model predictions, generated by computational analogs, must be empirically supported. This constraint leads to empirical advances in understanding psychopathology and the use of valid models in developing novel interventions. This process leads to a research cycle of model specification, hypothesis generation, empirical testing, and model refinement.
Recently, I have used computational neural network models of emotional information processing to understand how negative life events and disruptions in brain connectivity could interact to lead depressed individuals to focus on negative information. Model behaviors predicted that some depressed individuals would display sustained processing of negative personally relevant information long after they were exposed to it, as a function of increased feedback between limbic and cortical structures. Assessment of pupil dilation (a correlate of cognitive load) and functional magnetic resonance imaging (fMRI) suggested that depressed individuals demonstrated such sustained processing across a variety of emotional information processing tasks, and that this pattern was related to self-reported rumination. fMRI data, in particular, suggest that depressed individuals display sustained amygdala activity in response to negative information. This work has lead to further research using physiological assessment (pupil dilation, heart rate, event related potentials (ERPs), and electromyography (EMG)) and fMRI assessment to understand brain processes associated with regulating emotional information processing, and the development of an intervention to increase their functioning in depressed individuals through cognitive exercises. Future plans involve continuing to develop such neurally motivated behavioral interventions, relating physiological profiles to self-reported individual difference variables, and examining whether traditional interventions, and those tailored to disruptions in information processing are associated with changes in information processing.
Recent Publications
|











![[Picture of Greg J. Siegle]](http://www.cnbc.cmu.edu/images/faculty/siegle.jpg)