Supporting data for "Examining Emotion-related Perceptual Decision Making in Internalising Psychopathology"

We first established a novel paradigm which allowed the examination of both, bottom-up stimulus features and top-down attention-guided components (Study 1, Chapter 2, N = 168). The dataset for Chapter 2 contains behavioural data and computational modeling data (HDDM = hierarchical drift diffusion model). Behavioural data is included for one main experiment and two control experiments. Script folders (run in MATLAB and python) for analysis purposes are included. HDDM data contains traces for three models.Next, we applied the introduced and tested paradigm among a clinical (internalising psychopathology = IP) and healthy control (HC) sample (Study 2, Chapter 3, N = 137). The dataset for Chapter 3 contains behavioural data and computational modelling data (HDDM). Behavioural data is included for one main experiment and script folders (run in MATLAB and python) for analysis purposes. For HDDM analysis, traces for the winning model established in Chapter 2 are included.Study 3 addressed a different type of emotion-related perceptual decision making. We investigated potential differences among IP and HC under exposure to myriad pieces of emotional evidence (Study 3, Chapter 4, N = 122). Here, we employed a multi-element paradigm under exposure to face crowds. The dataset for Chapter 4 includes behavioural data and computational modelling data (EMHMM = Eye movement analysis with hidden Markov models). Behavioural data is included for one main experiment and script folders contain MATLAB and python codes for analysis purposes.Psychopathology data includes anonymised scoresheets of the Structured Clinical Interview for DSM-5, Research Version (SCID-5-RV) as well as questionnaire outputs with demographic information and dimensionally assessed symptom severities.

本研究首先确立了一种新颖的范式，该范式允许对自下而上的刺激特征与自上而下的注意力引导成分进行考察（第1章，第二章，样本量N=168）。第二章的数据集包含行为数据与计算建模数据（HDDM = 层次漂移扩散模型）。行为数据包括一个主要实验和两个控制实验。分析用的脚本文件夹（在MATLAB和Python中运行）亦包含在内。HDDM数据包含三个模型的轨迹。随后，我们将所引入并验证的范式应用于临床样本（内部化心理病理学 = IP）和健康对照组（HC）（第2章，第三章，样本量N=137）。第三章的数据集包含行为数据与计算建模数据（HDDM）。行为数据包括一个主要实验，分析用的脚本文件夹（在MATLAB和Python中运行）亦包含在内。针对HDDM分析，包含第二章中建立的获胜模型的轨迹。第3章针对一种不同类型的与情绪相关的感知决策问题进行研究。我们探讨了在众多情绪证据暴露下，内部化心理病理学样本和健康对照组之间可能存在的差异（第3章，第四章，样本量N=122）。在此，我们采用了一种在面部群体暴露下的多元素范式。第四章的数据集包括行为数据与计算建模数据（EMHMM = 隐藏马尔可夫模型的眼动分析）。行为数据包括一个主要实验，包含MATLAB和Python分析代码的脚本文件夹亦包含在内。心理病理学数据包括结构化临床访谈DSM-5研究版（SCID-5-RV）的去匿名评分表，以及包含人口统计信息和症状严重程度维度评估的调查问卷输出。