Implicit associations tied to psychopathology
Implicit associations reflect relatively uncontrollable automatic associations between concepts in memory. Our lab has demonstrated that anxiety and many other forms of psychopathology are characterized by biases in these associations. For instance, we have shown that phobic individuals are more likely than non-phobic individuals to associate their feared object with danger. Moreover, persons with panic disorder associate themselves with panic (vs. calm) relatively more than do non-anxious individuals, and importantly, these associations change following successful treatment and even predict the extent someone will experience a reduction in symptoms. To allow the public to learn about implicit mental health associations, we direct a public web site called Project Implicit Mental Health (www.implicitmentalhealth.com) that allows visitors to try an Implicit Association Test and receive feedback on their score. More than 500,000 tests have been completed!

Sample publications:
•    Glenn, J. J., Werntz, A. J., Slama, S. J. K., Steinman, S. A., Teachman, B. A., & Nock, M. K. (in press). Suicide and self-injury-related implicit cognition: A large-scale examination and replication. Journal of Abnormal Psychology.
•    Werntz, A. J., Steinman, S. A., Glenn, J., Nock, M., & Teachman, B. A. (2016). Characterizing implicit mental health evaluations across clinical domains. Journal of Behavior Therapy and Experimental Psychiatry, 52, 17-28.
•    Teachman, B. A., Joormann, J., Steinman, S. A., & Gotlib, I. H. (2012). Automaticity in anxiety disorders and major depressive disorder. Clinical Psychology Review, 32, 575-603. 


Cognitive bias modification
A primary focus of our recent work has been to examine the causal link between change in cognitive biases and symptom (e.g., anxiety) reduction using cognitive bias modification paradigms. These computer-based training programs are designed to directly alter biased ways of thinking, such as the tendency to make threat interpretations. For instance, we have trained interpretations to be more benign to decrease anxious responding among obsessional, contamination fearful, socially anxious, trait anxious, spider fearful, and anxiety sensitive samples, and even found that symptom changes following interpretation training for acrophobia (height fear) were as large as those achieved by a group receiving the gold-standard exposure therapy. These demonstrations are significant because they permit evaluation of the causal, rather than simply correlational, claims that underlie cognitive models, and because they offer promise for new interventions that are easy to disseminate given they are computer-based and do not require a therapist. We recently launched MindTrails (https://mindtrails.virginia.edu/), a public web site for people to try different online interpretation training programs.

Sample publications:
•    Beadel, J. R., Mathews, A., & Teachman, B. A. (2016). Cognitive Bias Modification to Enhance Resilience to a Panic Challenge. Cognitive Therapy and Research, 40(6), 799-812.
•    Steinman, S. A., & Teachman, B.A. (2014). Reaching new heights: Comparing interpretation bias modification to exposure therapy for extreme height fear. Journal of Consulting and Clinical Psychology, 82, 404-417.
•    Clerkin, E. M., & Teachman, B. A. (2011). Training interpretation biases among individuals with symptoms of obsessive compulsive disorder. Journal of Behavior Therapy and Experimental Psychiatry, 42, 337-343.


Dynamic monitoring of thoughts, feelings, and behaviors
Rather than rely on a static snapshot of biased processing, we seek new ways to more dynamically track anxious and other disorder-related thoughts, feelings, and behaviors. For instance, in collaboration with colleagues in engineering we use active (e.g., ecological momentary assessment) and passive (e.g., GPS, accelerometer, psychophysiology) mobile sensing via smartphones to learn how social anxiety and depressive symptoms tie to communication patterns and emotion regulation efforts in natural environments. In addition, we are developing new paradigms that enable more fine-grained, continuous assessment of biased processing of emotional information (e.g., tracking moment-to-moment changes in responding as positive and negative information is encountered; tracking computer mouse movements in response to feared stimuli to capture avoidance motivation; tracking sensitivity to reward and punishment cues as they shift over time).

Sample publications:
•    Chow, P. I., Fua, K., Huang, Y., Bonelli, W., Xiong, H., Barnes, L. E., & Teachman, B. A. (in press). Using mobile sensing to test clinical models of depression, social anxiety, state affect, and social isolation among college students. Journal of Medical Internet Research.
•    Fua, K., & Teachman, B. A. (in press). Dynamically tracking anxious individuals' affective response to valenced information. Emotion.
•    Chow, P. I., Fua., K., Xiong., H., Bonelli, W., Teachman, B. A., & Barnes, L. E.  (2016). SAD: Social anxiety and depression dynamic monitoring system. In: Computing and Mental Health Workshop, CHI: ACM Conference on Human Factors in Computing Systems. pp. 1-4.


Change in cognitive processing over treatment and across time
To evaluate whether change in cognitive biases predicts later symptom change, we have used repeated measures designs and dynamic modeling approaches. For instance, we have shown that change in the tendency to negatively (mis)interpret ambiguous stimuli tied to panic-related bodily cues predicts later reductions in maladaptive avoidance behaviors, and a myriad of other panic responses. Additionally, given that most cognitive bias measures are not process pure, we have started to look more in depth at just what is changing during treatment (e.g., to what extent changes reflect relatively more automatic vs. strategic components). We also collaborate with Dr. Kristen Lindgren to investigate change in alcohol associations across time.

Sample publications:
•    Lindgren, K. P., Neighbors, C., Teachman, B. A., Baldwin, S. A., Norris, J., Kaysen, D., ... & Wiers, R. W. (2016). Implicit alcohol associations, especially drinking identity, predict drinking over time. Health Psychology, 35(8), 908.
•    Clerkin, E. M., Fisher, C. R., Sherman, J. W., & Teachman, B. A. (2014). Applying the Quadruple Process Model to evaluate change in implicit attitudinal responses during therapy for panic disorder. Behaviour Research and Therapy, 52, 17-25.
•    Teachman, B. A., Marker, C. D., & Clerkin, E. M. (2010). Catastrophic misinterpretations as a predictor of symptom change during treatment for panic disorder. Journal of Consulting and Clinical Psychology, 78, 964-973.


Intrusive, unwanted thought
Intrusive unwanted thinking is tied to numerous forms of psychopathology. We have been investigating individual differences in the nature of intrusive thoughts and methods to control unwanted thinking, such as thought suppression. For example, we have conducted a series of studies to better understand how age-related changes in cognitive processing and emotion regulation alter the ability to suppress the recurrence of intrusive unwanted thoughts, and mitigate the thoughts’ potentially negative affective consequences. Relatedly, we completed a meta-analysis of thought suppression outcomes tied to psychopathology, finding that, while psychopathology is associated with greater rates of intrusive thinking, contrary to many theoretical predictions, there were no overall differences in the recurrence of thoughts following thought suppression between groups with and without psychopathology.

Sample publications:
•    Lambert A. E., Hu, Y., Magee, J. C., Beadel, J. R., & Teachman B. A. (2014). Thought suppression across time: Change in frequency and duration of thought recurrence. Journal of Obsessive-Compulsive and Related Disorders, 3, 21-28.
•    Magee, J. C., Smyth, F. L., & Teachman, B. A. (2014). A web-based examination of responses to intrusive thoughts across the adult lifespan. Aging and Mental Health, 18, 326-339.
•    Magee, J. C., Harden, K. P., & Teachman, B. A. (2012). Psychopathology and thought suppression: A quantitative review. Clinical Psychology Review, 32, 189-201.


Attitudes about mental illness and its treatment
Building from the social cognition literature and our understanding of how to modify negative beliefs and attitudes about stigmatized groups, we are examining how automatic biases affect clinical populations. We seek to better understand stigma toward persons with mental illness and treatment seeking, as well as what factors motivate people to seek care (e.g., beliefs about the value of science in determining a treatment plan; role of biases at the State level).

Sample publications:
•    Kulesza, M., Matsuda, M., Ramirez, J. J., Werntz, A. J., Teachman, B. A., & Lindgren, K. P. (2016). Towards greater understanding of addiction stigma: Intersectionality with race/ethnicity and gender. Drug and Alcohol Dependence, 169, 85-91.
•    Saporito, J., Ryan, C., & Teachman, B. A. (2011). Reducing stigma toward seeking mental health treatment among adolescents. Stigma Research and Action, 1, 9-21.
•    Peris, T. S., Teachman, B. A., & Nosek, B. A. (2008).  Implicit and explicit stigma of mental illness: Links to clinical care. Journal of Nervous and Mental Disease, 196, 752-760.


updated February 2017