Can placebos prevent intrusive memories?

After traumatic experiences, intrusive memories can flash back and evoke significant distress. Here, we investigated whether the occurrence of intrusions can be prevented by placebo. After the exposure to an experimental psychopathology model of psychological trauma, healthy participants (N=112) were randomised to deceptive placebo (DP), open-label placebo (OLP), or a no-treatment control group. The results show that one week later, the groups did not differ in the frequency of intrusive memories. However, participants receiving OLP reported the lowest intensity of intrusions. Participants receiving DP reported the lowest burden of intrusions. Across groups, the expectation that intrusions will be intense and hardly controllable was associated with a higher frequency of intrusions, higher distress, higher burden, and more negative appraisal. The results suggest that expectations play a crucial role in the emergence of intrusive memories and that at least some of the disabling aspects of intrusive memories can be reduced by placebo.

Making the Leap: from Experimental Psychopathology to Clinical Trials

One important aim of experimental psychopathology research is to inform development of new interventions derived from basic science. However, testing whether a newly developed intervention is in fact effective requires moving from experimental studies to clinical trials, and this transition can pose many problems. These problems stem not only from the inherent complexity of even the simplest clinical trial, but also from differences between experimental psychopathology and clinical trial research that may not always be obvious to researchers immersed in only one of these specialist areas. In this paper we explore some of these complexities, and discuss when a clinical trial may, or may not be, the best next step in the translational process. We then consider some of the ins and outs of clinical trials methodology, from design and planning through to reporting, with the aim of providing a guide for experimental psychopathology researchers thinking of making the leap from their experimental studies of mechanisms to clinical trials of novel interventions. We hope that this can help increase the chance of successful clinical translation and novel treatment development from basic science.

Applying Hierarchical Bayesian Modeling to Experimental Psychopathology Data: An Introduction and Tutorial

Over the past two decades Bayesian methods have been gaining popularity in many scientific disciplines. However, to this date, they are rarely part of formal graduate statistical training in clinical science. Although Bayesian methods can be an attractive alternative to classical methods for answering certain research questions, they involve a heavy “overhead” (e.g., advanced mathematical methods, complex computations), which pose significant barriers to researchers interested in adding Bayesian methods to their statistical toolbox. To increase the accessibility of Bayesian methods for psychopathology researchers, this paper presents a gentle introduction of the Bayesian inference framework and a tutorial on implementation. We first provide a primer on the key concepts of Bayesian inference and major implementation considerations related to Bayesian estimation. We then demonstrate how to apply hierarchical Bayesian modeling (HBM) to experimental psychopathology data. Using a real dataset collected from two clinical groups (schizophrenia and bipolar disorder) and a healthy comparison sample on a psychophysical gaze perception task, we illustrate how to model individual responses and group differences with probability functions respectful of the presumed underlying data-generating process and the hierarchical nature of the data. We provide the code with explanations and the data used to generate and visualize the results to facilitate learning. Finally, we discuss interpretation of the results in terms of posterior probabilities and compare the results with those obtained using a traditional method.