five

An Implicit Measurement of Alexithymia

收藏
PsychArchives2025-04-09 更新2026-04-25 收录
下载链接:
https://hdl.handle.net/20.500.12034/11620
下载链接
链接失效反馈
官方服务:
资源简介:
This study aims to develop an implicit measure of alexithymia using reaction times to emotional stimuli. Participants will be presented with facial expression images representing neutral, positive, and negative emotions. Participants will be asked to press a button as soon as they know how the image makes them feel. They do not need to indicate any emotion, simply press a button when they know. We will record the reaction time (RT) it takes for participants to report experiencing an emotional response to each image. To assess the validity of this implicit measure, participants’ reaction times will be correlated with their scores on two established self-report measures: the 20-item Toronto Alexithymia Scale (TAS-20) and the Depression, Anxiety, and Stress Scales (DASS-21). Methodology: A cross-sectional design will be employed with an expected sample size of 200 adult participants (aged 18+), recruited via Prolific. All participants will receive monetary compensation for their participation. There will be appr 100 trials, a third of them will be neutral expressions, a third of them will be positive expressions and a third will be negative expressions. We will first conduct Pearson correlations to explore associations between TAS-20 scores, DASS-21 depression subscale scores, and reaction times for neutral, positive, and negative stimuli. Subsequently, three linear regression analyses will be performed—one for each stimulus type—with TAS-20 and depression scores as predictors and reaction time as the dependent variable. This will help determine whether reaction times are more strongly associated with alexithymia or depressive symptoms and whether this relationship might be stronger for a particular type of emotional expression. unknown other
提供机构:
PsychArchives
创建时间:
2025-04-09
5,000+
优质数据集
54 个
任务类型
进入经典数据集
二维码
社区交流群

面向社区/商业的数据集话题

二维码
科研交流群

面向高校/科研机构的开源数据集话题

数据驱动未来

携手共赢发展

商业合作