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, <i>N</i> = 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, <i>N</i> = 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, <i>N</i> = 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.

本研究首先构建了一种全新的实验范式，可同时考察自下而上的刺激特征与自上而下的注意调控成分（研究1，第2章，样本量<i>N</i>=168）。第2章对应的数据集包含行为学数据与计算建模数据（分层漂移扩散模型（hierarchical drift diffusion model, HDDM））。该数据集涵盖1项主实验与2项对照实验的行为学数据，同时附带用于数据分析的MATLAB及Python脚本文件夹。HDDM数据包含3种模型的采样轨迹。
随后，我们将上述已开发并验证的实验范式应用于内化型精神病理（internalising psychopathology, IP）临床组与健康对照（healthy control, HC）组被试（研究2，第3章，样本量<i>N</i>=137）。第3章对应的数据集包含行为学数据与计算建模数据（HDDM）。该数据集涵盖1项主实验的行为学数据，同时附带用于数据分析的MATLAB及Python脚本文件夹。针对HDDM分析，本数据集包含第2章中确定的最优模型的采样轨迹。
研究3聚焦于另一类与情绪相关的知觉决策任务，旨在考察IP组与HC组在暴露于大量情绪性刺激证据时的潜在差异（研究3，第4章，样本量<i>N</i>=122）。本研究采用了面向人脸集群的多元素实验范式。第4章对应的数据集包含行为学数据与计算建模数据（隐马尔可夫模型眼动分析（Eye movement analysis with hidden Markov models, EMHMM））。该数据集涵盖1项主实验的行为学数据，同时附带用于数据分析的MATLAB及Python脚本代码。
精神病理相关数据包含经匿名化处理的《DSM-5研究版结构化临床访谈》（Structured Clinical Interview for DSM-5, Research Version, SCID-5-RV）评分表，以及包含人口统计学信息与维度化症状严重程度评估的问卷结果。