Predicting and Interpreting Patterns of Responding on the IRAP in the Context of Facial Emotions and Depression

Renato Bortoloti, Ana Paula Rubert Alves de Azevedo, Colin Harte, Dermot Barnes-Holmes
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Abstract

This study aimed to build on existing research on the implicit relational assessment procedure (IRAP), which has identified different patterns of IRAP effects associated with responses to happy and angry facial expressions. In particular, the study aimed to (1) replicate the previously observed happiness superiority effect, but with a far larger participant group than previous studies and (2) explore whether the IRAP effect would be influenced by self-reported levels of depression. One hundred twenty-two participants completed an IRAP that presented pairs of emotional human faces in a 2x2 crossover design yielding four trial-types: happy face–happy face, happy face–angry face, angry face–happy face and angry face–angry face. The results showed a clear happiness superiority effect, with the IRAP effect for trial-type 1 (happy face–happy face) larger than the IRAP effect for trial-type 4 (angry face–angry face). Self-reported depression appeared to moderate responding on the trial-type 1 alone, with low depressed individuals producing a larger D-IRAP score on the trial-type 1 relative to high depressed individuals. The findings support recent arguments that the stimulus function properties of all elements within an IRAP should be taken into account when predicting and interpreting behavioral patterns produced on the procedure. Considerations for use of the IRAP to predict behaviors in the natural environment are discussed.

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在面部情绪和抑郁的背景下预测和解释 IRAP 的反应模式
本研究旨在以现有的内隐关系评估程序(IRAP)研究为基础,该程序发现了与快乐和愤怒面部表情反应相关的不同IRAP效应模式。特别是,本研究旨在:(1)复制之前观察到的快乐优势效应,但参与者群体要比之前的研究大得多;(2)探索 IRAP 效应是否会受到自我报告的抑郁水平的影响。122 名参与者完成了 IRAP,在 2x2 交叉设计中呈现了成对的情绪化人脸,产生了四种试验类型:开心脸-开心脸、开心脸-生气脸、生气脸-开心脸和生气脸-生气脸。结果显示了明显的快乐优势效应,试验类型 1(快乐脸-快乐脸)的 IRAP 效应大于试验类型 4(愤怒脸-愤怒脸)的 IRAP 效应。自我报告的抑郁情绪似乎仅对试验类型 1 的反应有调节作用,相对于抑郁情绪较高的人,抑郁情绪较低的人在试验类型 1 上的 D-IRAP 得分更高。这些研究结果支持最近的观点,即在预测和解释程序中产生的行为模式时,应考虑到 IRAP 中所有元素的刺激功能特性。研究还讨论了使用 IRAP 预测自然环境中行为的注意事项。
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