Present Brain-Computer Interfacing (BCI) technology allows inference and detection of cognitive and affective states, but fairly little has been done to study scenarios in which such information can facilitate new applications that rely on modeling human cognition. One state that can be quantified from various physiological signals is attention. Estimates of human attention can be used to reveal preferences and novel dimensions of user experience. Previous approaches have tackled these incredibly challenging tasks using a variety of behavioral signals, from dwell-time to click-through data, and computational models of visual correspondence to these behavioral signals. However, behavioral signals are only rough estimations of the real underlying attention and affective preferences of the users. Indeed, users may attend to some content simply because it is salient, but not because it is really interesting, or simply because it is outrageous. With this paper, we put forward a research agenda and example work using BCI to infer users' preferences, their attentional correlates towards visual content, and their associations with affective experience. Subsequently, we link these to relevant applications, such as information retrieval, personalized steering of generative models, and crowdsourcing population estimates of affective experiences.

翻译：当前的脑机接口技术能够推断和检测认知与情感状态，但利用此类信息促进依赖人类认知建模的新应用场景的研究尚不充分。注意力是一种可从多种生理信号中量化的状态。对人类注意力的评估可用于揭示用户偏好及用户体验的新维度。先前研究通过多种行为信号（从注视时间到点击数据）以及与此类行为信号对应的视觉计算模型来处理这些极具挑战性的任务。然而，行为信号仅是对用户真实潜在注意力与情感偏好的粗略估计。实际上，用户可能仅因内容显著（而非真正有趣）或因其令人震惊而关注某些内容。本文提出了一项研究议程及示例工作，利用脑机接口推断用户偏好、其对视觉内容的注意力关联及其与情感体验的关联。随后，我们将这些关联与相关应用联系起来，例如信息检索、生成模型的个性化引导以及情感体验的群体众包评估。