  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.
