In early June, an article I wrote with co-authors about the Self-Referent Encoding Task (SRET) was published in the journal Psychological Assessment (Dainer-Best, Lee, Shumake, Yeager, & Beevers, in press). The article is entitled “Determining optimal parameters of the Self Referent Encoding Task: A large-scale examination of self-referent cognition and depression”, and you can find it on the publisher’s website by following that link. (If you are unable to access the publisher’s copy, an updated post-print ((The online database SHERPA defines a post-print as “The final version of an academic article or other publication—after it has been peer-reviewed and revised into its final form by the author.” As a general rule, this means the version of the manuscript that is to be published—but not in the journal’s style. As such, the link there is to a version of the manuscript that I created in LaTeX. )) is available on the Open Science Framework, here.) I’m very pleased to see that this article has been published.

I think the conclusions we reach are worthwhile. This article is primarily about methodology in studying depression, and we took this opportunity to investigate a commonly-used task on a larger-than-normal scale and across three samples (572 college students, 293 adults on Amazon Mechanical Turk, and 270 adolescents). We were interested in answering this question: what is the best way to link how people describe themselves (what we call “self-referential processing”) to how elevated they are in terms of depressive symptoms? Researchers often use the task mentioned above (the Self-Referent Encoding Task, or SRET) to measure self-referential processing. Here, we collected data on that behavioral task, and measured depressive symptoms with a questionnaire. The SRET has a number of outcomes, from simple endorsements (do you describe yourself using positive and negative words?) to computational outcomes (what I elsewhere call “the rate of accumulation of information needed to make the decision about whether each word was self-referential”). By using a recursive validation procedure, we were able to make a pretty good argument for which outcomes from the SRET should be the focus for future work: the number of positive and negative words that individuals endorse as being self-referential, and the processed responses which mark “accumulation of information”—but not the reaction times or recall of words.

Additionally, this article gave me the opportunity to begin expressing a long-held interest in open science. I published our data and code on the Texas Data Repository, published the pre-print (when I submitted the article—updated to the post-print, above), and laid out several of our analyses in supplemental websites on GitHub. I also created a Shiny app which allows you to visualize the correlations between variables.

I’m pleased to note that two new journal articles were published in the past few months. They’re based on research that I worked on over the past year (or, actually, 2-3 years).

One is entitled “Sustained attentional engagement is associated with increased negative self-referent decision-making in major depressive disorder” (Dainer-Best, Trujillo, Schnyer, & Beevers, 2017). In this work, we helped solidify the relationship between depression and negative information processing; finding that people who were depressed responded to a task about self-referential stimuli differently behaviorally and in EEG.

The second paper is called “Specificity and overlap of attention and memory biases in depression” (Marchetti, Everaert, Dainer-Best, Loeys, Beevers, & Koster, 2018). Dr. Igor Marchetti was the lead on this project. Here, we used a commonality analysis to begin to tease apart the relationship between measures of depression symptoms and two types of cognitive biases: attention bias and memory bias. In this study, we found that the memory bias in mood-relevant stimuli was reliably related to depressive symptoms but not anxiety symptoms—it was specific here.