Determining optimal parameters of the self-referent encoding task: A large-scale examination of self-referent...

[Correction Notice: An Erratum for this article was reported online in Psychological Assessment on Aug 2 2018 (see record 2018-38659-001). In this article, there was an error in how exclusions for one of the three samples were reported, which resulted in inaccurate reporting of how many participants did not have complete data. This error did not change the primary results of the article or the conclusions. However, in the second paragraph of the Participant Attrition and Data Filtering section, the number of exclusions for the adolescent sample should be 301, not 163. As a result, n=408 should read n=270 in the abstract; in paragraph 3 of the Method section; and in the Figure 1 legend. In addition, the correct values for the Adolescents sample reported in Tables 1 and 2 are provided in the erratum.] Although the self-referent encoding task (SRET) is commonly used to measure self-referent cognition in depression, many different SRET metrics can be obtained. The current study used best subsets regression with cross-validation and independent test samples to identify the SRET metrics most reliably associated with depression symptoms in three large samples: a college student sample (n = 572), a sample of adults from Amazon Mechanical Turk (n = 293), and an adolescent sample from a school field study (n = 408). Across all 3 samples, SRET metrics associated most strongly with depression severity included number of words endorsed as self-descriptive and rate of accumulation of information required to decide whether adjectives were self-descriptive (i.e., drift rate). These metrics had strong intratask and split-half reliability and high test-retest reliability across a 1-week period. Recall of SRET stimuli and traditional reaction time (RT) metrics were not robustly associated with depression severity. (PsycINFO Database Record (c) 2018 APA, all rights reserved).