Skip to main content
Share Dataverse

Share this dataverse on your favorite social media networks.

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
Featured Dataverses

In order to use this feature you must have at least one published dataverse.

Publish Dataverse

Are you sure you want to publish your dataverse? Once you do so it must remain published.

Publish Dataverse

This dataverse cannot be published because the dataverse it is in has not been published.

Delete Dataverse

Are you sure you want to delete your dataverse? You cannot undelete this dataverse.

Find Advanced Search

1 to 1 of 1 Result
Mar 18, 2020
Hsu, Kean; Beevers, Christopher, 2020, "Analysis code and supplemental materials", https://doi.org/10.18738/T8/6ENXZW, Texas Data Repository Dataverse, V1
R markdown files, output, and supplemental tables for manuscript currently under invited resubmission
Add Data

Log in to create a dataverse or add a dataset.

Link Dataverse
Reset Modifications

Are you sure you want to reset the selected metadata fields? If you do this, any customizations (hidden, required, optional) you have done will no longer appear.

Contact Texas Data Repository Dataverse TDL Dataverse Support

Texas Data Repository Dataverse TDL Dataverse Support

Please fill this out to prove you are not a robot.

+ =