Research at the MDL generally examines factors that contribute vulnerability to depression, maintain depression, or ameliorate depression. Research tends to focus on cognitive factors in depression–biases in information processing (attention, interpretation, recall), rumination, and dysfunctional beliefs. We are particularly interested in how these biases influence everyday behavior using EMA and mobile sensing. More recently, we have been using machine learning and other data science techniques (e.g., time series feature extraction) to identify important predictors of depression-related phenomena. MDL research has been generously supported by funding from the National Institute of Mental Health (NIMH), Department of Defense (DoD), and The University of Texas at Austin.

One area of broad interest in the Cognitive Bias Modification (CBM). CBM refers to procedures used to directly change biases in cognitive processes, such as biased attention toward mood-congruent stimuli and interpreting neutral information in a negative manner. For an overview of this work, please see this AI-generated podcast that we created using NotebookLM.

Examples of current studies include:

Development of Attention Bias Modification for Depression
The overall goal of this project is to continue the development of an attention bias modification (ABM) intervention that targets and reduces negative attention bias among adults with elevated symptoms of depression. Our prior work indicates that attention bias for negative information is associated with the maintenance of depression and that neural circuitry within frontal-parietal brain networks supports biased attention for negative information, thus allowing us to develop specific and targeted interventions that directly alter the neurobiology of negative attention bias (for examples, see Hsu et al and Beevers et al).

Perceptual and Decisional Processes Underlying Face Perception Bias in Clinical Depression
In this study, our goal is to examine how the brain processes emotion and facial identification. Prior research suggests that the ability to process important face dimensions (identity) independently from emotion might be impaired in depression. We aim to use state-of-the-art computational and psychophysical approaches to identify the precise locus of the impairments in face emotion processing in depression (for examples, see Soto et al and Soto & Beevers).