Decomposing Pupil Dynamics to Reveal Attention-Dependent Processing of Emotional Faces

The aim of the dataset is to investigate how attention to emotional faces influences pupillary dynamics. The dataset includes both behavioural and pupillometry data collected from 35 participants who performed an emotion detection task involving happy, angry, and neutral facial expressions under different experimental conditions. In addition to the main task, a control condition was included to preserve visual input while minimizing emotional relevance. Trial-wise behavioural performance is provided in the file "Master_Accuracy_Trials.csv", which includes participant identifiers, emotion category, experimental condition, and response accuracy coded as 0 (incorrect) and 1 (correct).The file "Master_Pupil_Features.csv" contains detailed trial-level pupil data along with reaction times and six decomposed features capturing distinct phases of pupil dynamics. These features include constriction onset, constriction amplitude, latency of contriction amplitude, pupil value during dilation, constriction rate, and dilation rate. In addition, the file includes the full pupil time course for each trial, represented by columns "Pupil_Data_1" to "Pupil_Data_275", sampled at 8 ms intervals and preprocessed prior to analysis.The pupillary response was decomposed into biologically meaningful components to capture the temporal dynamics underlying autonomic and cognitive influences. All pupil measurements are baseline-corrected and reported in millimetres (mm), with temporal measures in milliseconds (ms) and rate-based measures expressed as change in pupil size over time (mm/ms). The code used for preprocessing and feature extraction is publicly available at: https://github.com/Sangramjit/Decomposing-Pupil-Dynamics-to-Reveal-Attention-Dependent-Processing-本数据集旨在探究对情绪面孔的注意如何影响瞳孔动力学（pupillary dynamics）。数据集包含来自35名参与者的行为学数据与瞳孔测量法（pupillometry）数据，所有参与者在不同实验条件下完成了涵盖快乐、愤怒与中性面部表情的情绪检测任务。除主任务外，本实验还设置了控制条件，在保留视觉输入的同时尽可能降低情绪相关性。逐试次行为学表现存储于文件"Master_Accuracy_Trials.csv"中，该文件包含参与者标识号、情绪类别、实验条件以及以0（错误）和1（正确）编码的反应准确率。文件"Master_Pupil_Features.csv"包含详细的试次级瞳孔数据，以及反应时与六个分解得到的、用于捕捉瞳孔动力学不同阶段的特征。这些特征分别为：瞳孔收缩起始、收缩幅度、收缩幅度潜伏期、扩张期瞳孔值、收缩速率与扩张速率。此外，该文件还包含每一试次的完整瞳孔时间序列，以"Pupil_Data_1"至"Pupil_Data_275"列表示，采样间隔为8毫秒，并在分析前完成了预处理。本研究将瞳孔响应分解为具有生物学意义的组分，以捕捉自主神经与认知影响背后的时间动力学特征。所有瞳孔测量值均经过基线校正，单位为毫米（mm），时间测量单位为毫秒（ms），速率类测量值以单位时间内瞳孔大小变化量表示（mm/ms）。用于预处理与特征提取的代码已公开发布于：https://github.com/Sangramjit/Decomposing-Pupil-Dynamics-to-Reveal-Attention-Dependent-Processing-