Events
International Lecture Series
September 30th, 9:30-11 am: Michelle Craske: "Targeting Threat & Reward Processes for Depression, Anxiety and Anhedonia: efficacy, mechanisms and community implementation"
I will present our latest evidence regarding targeted psychological treatments for anxiety, depression, and anhedonia, drawing from findings from the research laboratory, research clinic and community implementation. Efficacy data demonstrate that not all psychological treatments are the same, as inhibitory retrieval models outperform standard habituation models of exposure therapy, and positive affect treatment outperforms conventional cognitive behavioral therapy. Mechanism data demonstrate specificity, as extinction prediction error learning explains treatment outcome from exposure therapy whereas fear habituation does not, and reward processes are significantly more strengthened by positive affect treatment than conventional cognitive behavioral therapy. Emerging data will be presented to suggest that personalized precision can be achieved by matching therapy to mechanism (rather than to symptoms). These mechanistically targeted treatments have been incorporated into scalable and personalized community implementation through our STAND (Screening, Tracking and Treating Anxiety and Depression) system of care. Evidence for STAND efficacy and enhancement by predictive decision-making algorithms will be presented, and directions of future research will conclude the presentation.
Location: ESA W 221
October 1st, 9:00-10:30 am: Stefano Palminteri: "How to provoke, fight and win (?) scientific battles: lessons from 10 years of research on reinforcement learning biases"
Since its inception in 2017, one of our main research goals has been to propose, computationally formalize, and behaviorally validate the existence of biases in the way humans learn from rewards and punishments. In particular, we have identified two key processes: relative outcome encoding and a positivity/confirmation bias in learning. We have tested these ideas across a wide range of experimental designs, which has helped refine our computational understanding of these biases. Their empirical validation has systematically relied on what we consider methodological gold standards in computational cognitive modeling (coupling parsimony with falsification). However, fueled by strict open data practices and the broad interest generated by these questions, many laboratories have sought to challenge our conclusions over the years, sometimes proposing radically different computational interpretations of the behavioral effects that are otherwise widely replicated. In this talk, I will recount the story of these challenges and our responses. I will argue that, taken together, this process provides unusually strong evidence that the reinforcement learning biases we have identified withstand a level of scrutiny that is both rare and highly valuable for the field.
Location: ESA W 221
October 14th, 4:15-5:45 pm: Tobias Hauser: "Deciding when to decide: neural mechanisms underlying biased information gathering in mental health” - Joint Talk with GRK2753
Online via Zoom
October 28th, 4:15-5:45 pm: Miriam Klein-Flügge - Topic TBA
Please e-mail to klinische.psych"AT"uni-hamburg.de for all requests related to the lecture series, including requests for the Zoom link to online lectures.