Dataset supporting the paper: High trait anxiety enhances optimal integration of auditory and visual threat cues

This dataset includes data on behavioural outcomes for the audiovisual emotion recognition tasks used in the publication, "High Trait Anxiety Enhances Optimal Integration of Auditory and Visual Threat Cues". In this study the authors investigated perception of happy, sad and angry emotions within unimodal (audio- and visual-only) and audiovisual displays in adults with low vs. high levels of trait anxiety. The data is organised to facilitate replication of the analyses carried out in the aforementioned study, which includes two model-based analyses to elucidate how multisensory integration of emotional information operates in high trait anxiety. This was done by comparing performance in the audiovisual condition for both high and low trait anxiety groups to performance predicted by the Maximum Likelihood Estimation (MLE) model (Ernst & Banks, 2002; Rohde et al., 2016) and Miller’s Race Model (Miller, 1982; Ulrich et al., 2007). Data included in this dataset has already been pre-processed (i.e., univariate outliers have already been identified and dealt with).

本数据集涵盖了用于出版物《高特质焦虑强化了对听觉和视觉威胁线索的最佳整合》中的视听情感识别任务的行为结果数据。在该研究中，作者们探讨了低特质焦虑与高特质焦虑的成年人在单模态（仅听觉和视觉）以及视听显示中对于快乐、悲伤和愤怒情绪的感知。数据组织旨在便于复现上述研究中进行的分析，该分析包括两个基于模型的统计分析，旨在阐明多感官情感信息的整合在高特质焦虑者中的运作机制。这通过比较高特质焦虑与低特质焦虑组在视听条件下的表现与最大似然估计（MLE）模型（Ernst & Banks, 2002；Rohde 等人，2016）及 Miller 的赛跑模型（Miller, 1982；Ulrich 等人，2007）的预测性能来完成。数据集中的数据已进行预处理（即，已识别并处理了单变量异常值）。