Justin Dainer-Best is a clinical psychology intern (2017-2018) in the Connecting Cultures and NESTT service at Vermont Psychological Services, and a clinical psychology intern at the Vermont Center for Children, Youth and Families. He will receive his Ph.D. in clinical psychology from the University of Texas at Austin, where he worked in the Mood Disorders Lab with Dr. Christopher Beevers
Justin's primary research goal is to advance understanding of Major Depressive Disorder and the impact of depressed mood on thinking—what makes people who are depressed think the way that they do? What mechanisms lead to depression, and which mechanisms keep people depressed? How can we think of these mechanisms as mutable, allowing us to devise more effective treatments? And what techniques are most useful in terms of exploring these questions?
Justin's research has a theoretical emphasis on the genesis and maintenance of depressed mood. He is interested in dysregulation of self-schema, and in engagement with and disengagement from negative information. He has been working on exploring the self-referent encoding task (SRET), a measure of negative self-schema, from a psychometric and validity perspective. Justin is also a proponent of open science and open data sharing; he has created online Shiny app data supplements for some of the above-mentioned work. (Read more about Shiny.)
Justin has worked extensively with EEG and ERPs, publishing on worrying and stress. More recently, Justin published a first-authored paper using ERPs to link major depression and disordered self-reference.
In terms of research interests, Justin is also directed towards statistical learning. He has worked with a colleague on a meta-analytic commonality analysis that identified patterns in attention and memory biases, and submitted a paper that uses best subsets procedures to build reliable statistical models linking self-reference and depression. Justin has also used generalized linear mixed-effects models to test hypotheses in an intervention trial aiming to modify some of the biases discussed above; this paper will be submitted in late 2017.