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Data analysis code, resulting output (generated by R markdown), and raw data for manuscript under invited resubmission.

Abstract: Individual differences in reward-related processes, such as reward responsivity and approach motivation, appear to play a role in the nature and course of depression. Prior work suggests that cognitive biases for valenced information may contribute to these reward processes. Yet there is little work examining how biased attention, processing, and memory for positively- and negatively-valenced information may be associated with reward-related processes in samples with depression symptoms. We consequently used a data-driven, machine-learning (elastic net) approach to identify the best predictors of self-reported reward-related processes using multiple tasks of attention, processing, and memory for valenced information measured across behavioral, eye tracking, psychophysiological, and computational modeling approaches (N = 202). Participants were adults (ages 18 - 35) who ranged in depression symptom severity from mild to severe. Models predicted between 5.0-12.2% and 9.7-28.0% of held-out test sample variance in approach motivation and reward responsivity, respectively. Low self-referential processing of positively-valenced information was the most robust, albeit modest, predictor of low approach motivation and reward responsivity. Experiments are now needed to clarify the causal relationship between self-referential processing of positively-valenced information and reward processes in depression.

Keywords: behavioral activation system; attentional bias; cognitive processing; memory; depression; machine learning
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Mar 18, 2020
Hsu, Kean; Beevers, Christopher, 2020, "Analysis code and supplemental materials",, Texas Data Repository, V1
R markdown files, output, and supplemental tables for manuscript currently under invited resubmission
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