AffectTracker: Real-time continuous rating of affective experience in immersive virtual reality.

Subjective experience is key to understanding affective states, characterized by valence and arousal. Traditional experiments using post-stimulus summary ratings do not resemble natural behavior. Fluctuations of affective states can be explored with dynamic stimuli, such as videos. Continuous ratings can capture moment-to-moment affective experience, however the rating or the feedback can be interfering. We designed, empirically evaluated, and openly share AffectTracker, a tool to collect continuous ratings of two-dimensional affective experience (valence and arousal) during dynamic stimulation, such as 360-degree videos in immersive virtual reality. AffectTracker comprises three customizable feedback options: a simplified affect grid (Grid), an abstract pulsating variant (Flubber), and no visual feedback. Two studies with healthy adults were conducted, each at two sites (Berlin, Germany, and Torino, Italy). In Study 1 (Selection: n = 51), both Grid and Flubber demonstrated high user experience and low interference in repeated 1-min 360-degree videos. Study 2 (Evaluation: n = 82) confirmed these findings for Flubber with a longer (23-min), more varied immersive experience, maintaining high user experience and low interference. Continuous ratings collected with AffectTracker effectively captured valence and arousal variability. For shorter, less eventful stimuli, their correlation with post-stimulus summary ratings demonstrated the tool’s validity; for longer, more eventful stimuli, it showed the tool’s benefits of capturing additional variance. Our findings suggest that AffectTracker provides a reliable, minimally interfering method to gather moment-to-moment affective experience also in immersive environments, offering new research opportunities to link affective states and physiological dynamics.

主观体验是理解以效价（valence）和唤醒度（arousal）为核心特征的情感状态的关键所在。传统的后刺激总结评分实验与自然行为模式存在显著差异。借助动态刺激（如视频），可对情感状态的波动变化进行探究。连续评分能够捕捉逐时刻的情感体验，但评分流程或反馈机制可能会引入干扰。我们设计并实证评估了AffectTracker，并将其开源共享——该工具可在动态刺激（如沉浸式虚拟现实中的360度视频）场景下，采集二维情感体验（效价与唤醒度）的连续评分数据。AffectTracker提供三种可自定义的反馈模式：简化版情感量表（Grid）、抽象脉动式变体（Flubber），以及无视觉反馈模式。我们针对健康成年人开展了两项研究，实验分别在德国柏林与意大利都灵两个站点完成。在研究1（选型实验，样本量n=51）中，Grid与Flubber两种模式在重复播放的1分钟360度视频刺激下，均展现出优异的用户体验与极低的干扰性。研究2（评估实验，样本量n=82）针对时长更长（23分钟）、内容更丰富的沉浸式体验场景，验证了Flubber模式的上述优势，其仍能维持较高的用户体验与较低的干扰水平。通过AffectTracker采集的连续评分数据，可有效捕捉效价与唤醒度的变化波动。对于时长较短、事件密度较低的刺激素材，连续评分与后刺激总结评分的相关性验证了该工具的有效性；而对于时长更长、事件更丰富的刺激素材，则体现出该工具能够捕捉额外变异信息的优势。本研究结果表明，AffectTracker可为沉浸式环境下的逐时刻情感体验采集提供一种可靠、低干扰的方法，为关联情感状态与生理动态的研究开辟了全新的方向。