Suppr超能文献

一项使用新型虚拟现实范式研究情感预测偏差的初步研究。

A pilot study investigating affective forecasting biases with a novel virtual reality-based paradigm.

机构信息

Univ. Lille, Inserm, CHU Lille, U1172 - LilNCog - Lille Neuroscience & Cognition, 59000, Lille, France.

Department of Psychiatry, CHU Lille, 59000, Lille, France.

出版信息

Sci Rep. 2023 Jun 8;13(1):9321. doi: 10.1038/s41598-023-36346-3.

Abstract

A body of research indicates that people are prone to overestimate the affective impact of future events. Here, we developed a novel experimental paradigm to study these affective forecasting biases under laboratory conditions using subjective (arousal and valence) and autonomic measures (skin conductance responses, SCRs, and heart rate). Thirty participants predicted their emotional responses to 15 unpleasant, 15 neutral, and 15 pleasant scenarios (affective forecasting phase) to which they were then exposed in virtual reality (emotional experience phase). Results showed that participants anticipated more extreme arousal and valence scores than they actually experienced for unpleasant and pleasant scenarios. The emotional experience phase was characterized by classic autonomic patterns, i.e., higher SCRs for emotionally arousing scenarios and greater peak cardiac acceleration for pleasant scenarios. During the affective forecasting phase, we found only a moderate association between arousal scores and SCRs and no valence-dependent modulation of cardiac activity. This paradigm opens up new perspectives for investigating affective forecasting abilities under lab-controlled conditions, notably in psychiatric disorders with anxious anticipations.

摘要

大量研究表明,人们倾向于高估未来事件的情感影响。在这里,我们开发了一种新的实验范式,使用主观(唤醒和效价)和自主测量(皮肤电反应、SCR 和心率)在实验室条件下研究这些情感预测偏差。30 名参与者预测了他们对 15 个不愉快、15 个中性和 15 个愉快场景的情绪反应,然后在虚拟现实中体验这些场景(情感体验阶段)。结果表明,参与者预计不愉快和愉快的场景会产生更极端的唤醒和效价得分,而实际上他们体验到的得分并不那么极端。情感体验阶段的特点是经典的自主模式,即情绪唤起的场景会产生更高的 SCR,愉快的场景会产生更大的峰值心脏加速度。在情感预测阶段,我们只发现唤醒得分与 SCR 之间存在中度关联,而心脏活动没有效价依赖的调制。这种范式为在实验室控制条件下研究情感预测能力开辟了新的视角,特别是在焦虑预期的精神障碍中。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/303d/10250404/7e1bc276eccd/41598_2023_36346_Fig1_HTML.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验