Affective impact and electrocortical correlates of a psychotherapeutic microintervention: An ERP study of cognitive restructuring
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2014-01-01
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Objective: Psychotherapy for depression emphasizes techniques that can help individuals regulate their moods. The present study investigated the affective impact and electrocortical correlates of cognitive restructuring, delivered as a 90-minute psychotherapeutic microintervention in a dysphoric sample. Method: Participants (N = 92) who reported either low or high levels of dysphoric symptoms were randomly assigned to the restructuring microintervention, a control intervention or a no-intervention condition. We obtained recordings of event-related potentials (ERPs) as well as mood self-ratings during an experimental session immediately after the psychotherapeutic microintervention and the control intervention in which a set of negatively valenced pictures (IAPS) was presented with different instructions. Results: Whereas the restructuring intervention group and the control intervention group reported both increases in positive and decreases in negative affect from pre- to post-intervention, the three groups differed significantly on ERP measures. Conclusions: Findings provide support for current models of mechanisms of action in cognitive therapies. © 2013 © 2013 Society for Psychotherapy Research.
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Zaunmüller, Luisa, Wolfgang Lutz and Timothy J Strauman (2014). Affective impact and electrocortical correlates of a psychotherapeutic microintervention: An ERP study of cognitive restructuring. Psychotherapy Research, 24(5). pp. 550–564. 10.1080/10503307.2013.847986 Retrieved from https://hdl.handle.net/10161/13843.
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Timothy J. Strauman
Professor Strauman’s work is grounded in the premise that mental health and well-being are fundamentally shaped by self-regulation—how individuals pursue goals, respond to challenges, and adapt over time. His research integrates clinical psychology, affective neuroscience, and behavioral science to characterize the psychological and neurobiological systems that support self-regulation, and to understand how disruptions in these systems contribute to vulnerability to depression and related conditions.
Across a program of experimental, clinical, and neuroimaging research, his work has examined self-regulation as a multi-level system, including its cognitive and motivational mechanisms, its development through socialization, and its links to affective and immunological processes. This work has also informed the development and evaluation of novel interventions targeting self-regulatory dysfunction.
More recently, his work has focused on translating this science of self-regulation into scalable, AI-informed approaches to intervention and prevention. This includes the development of new models of treatment that target regulatory processes across disorders, as well as efforts to extend effective self-regulation skills beyond traditional clinical settings and into everyday contexts. This translational focus reflects a broader aim of building integrated, system-level approaches to mental health that can improve outcomes at population scale, using the capabilities of AI responsibly and creating the most effective synergy of human professional augmented by machine-based intelligence.
Unless otherwise indicated, scholarly articles published by Duke faculty members are made available here with a CC-BY-NC (Creative Commons Attribution Non-Commercial) license, as enabled by the Duke Open Access Policy. If you wish to use the materials in ways not already permitted under CC-BY-NC, please consult the copyright owner. Other materials are made available here through the author’s grant of a non-exclusive license to make their work openly accessible.