Modeling User Preferences via Brain-Computer Interfacing

TL;DR

The paper tackles how brain-computer interface signals can reveal attention and affective Preferences during naturalistic content exposure. It surveys instrumentation choices (EEG and fNIRS), argues that informative downstream tasks can benefit from—even when upstream classifications are imperfect—through approaches like information retrieval, affective similarity, and steering of generative models. It introduces the concept of brainsourcing to aggregate implicit signals across users for crowd-level affective estimates and discusses the importance of open data and reproducible practices. It concludes by emphasizing ethical considerations and policy measures to protect privacy as neurophysiological data become integrated into user experiences.

Abstract

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.