Dataset for 'Self-processing in relation to emotion and reward processing in depression'

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Self_Emotion_Reward_Processing_Depression_readme.txt

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Self_Emotion_Reward_Processing_Depression_Analysis_210813.Rmd

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data archive 9007 disk0/00/00/09/24 2021-09-13 10:08:33 2024-07-15 10:59:34 2021-09-13 10:08:33 data_collection show Hobbs Katie C.Hobbs@bath.ac.uk 0000-0002-2324-5882 University of Bath TRUE Button Katherine K.S.Button@bath.ac.uk 0000-0003-4332-8789 University of Bath TRUE Supervisor Sui Jie jie.sui@abdn.ac.uk 0000-0002-4031-4456 University of Aberdeen Supervisor Munafo Marcus R. marcus.munafo@bristol.ac.uk 0000-0002-4049-993X University of Bristol Supervisor Kessler David david.kessler@bristol.ac.uk 0000-0001-5333-132X University of Bristol Dataset for 'Self-processing in relation to emotion and reward processing in depression' JN0010 dept_psy depression, self, emotion, reward, social cognition A data dictionary is provided outlining the organisation of data and describing individual variables. This dataset is for a study examining the role of self processing in relation to emotion and reward processing in depression. Participants (n = 144) with varying levels of depression symptoms completed cognitive tasks measuring self processing independently and in combination with emotion and reward processing over two session approximately one week apart. This dataset contains the raw trial level and cleaned aggregate data used for analysis for each of these cognitive tasks (Associative Learning, Go/No-Go Self-Esteem, Social Evaluation Learning), self-report questionnaire data for mood, demographics and output from the clinical interview schedule-revised (.csv, .xlsx and .RDA files). Code used for cleaning and analysis is also provided in the form of R Notebook files. 2021-09-07 University of Bath public RightsHolder University of Bath Medical Research Council https://doi.org/10.13039/501100000265 MR/N0137941/1 GW4 BioMed MRC Doctoral Training Partnership We recruited participants aged 18 to 65, fluent in English, with normal or corrected to normal vision, through campus advertising at the University of Bath. As depression severity is positively skewed within the general population (Tomitaka et al., 2015), to ensure balanced levels of depression we screened participants using the Patient Health Questionnaire (PHQ-9; Kroenke et al., 2001). We recruited an equal number of participants with no depression (PHQ-9 less than or equal to 4), mild depression (PHQ-9 5-9) and moderate to severe depression (PHQ-9 greater than or equal to 10). Participants completed self-report depression measures (PHQ-9, BDI-II) across two sessions approximately one week apart. We measured self, emotion, and reward processing, separately and in combination, using three cognitive tasks. This included simple associative learning task, a self-esteem go/no-go task, and a social evaluation learning task. Data was anonymised prior to cleaning, through random re-assignment of unique IDs and removal of potentially identifying variables (e.g. exact date of sessions). Aside from the anonymisation changes, the raw data is provided as well as the code used to clean data to produce the aggregate data used for analysis. For clarity the code used for anonymisation is provided in the R scripts for cleaning raw data in comments. The data was cleaned and analysed using R version 3.6. Data is provided in .CSV, .XLSX and .RDA files which can be opened with a variety of software. 2018-10-01 2019-05-22 10 West, Department of Psychology, University of Bath, BA2 7AY en 1 10.15125/BATH-00924 https://osf.io/gz9xj/ website https://doi.org/10.1017/S0033291721003597 pub https://researchportal.bath.ac.uk/en/studentTheses/2bb066e2-d1b4-4c45-8f9f-d0f9441b130b thesis open