Modeling User Preferences via Brain-Computer Interfacing

TL;DR

This paper explores using brain-computer interfaces to directly infer user preferences and attention, aiming to improve personalized applications by capturing more accurate cognitive and affective states than traditional behavioral signals.

Contribution

It introduces a research framework and initial work on leveraging BCI data to model user preferences, attention, and emotions for enhanced personalized experiences.

Findings

BCI can infer attention and preferences more accurately than behavioral signals.

Potential applications include improved information retrieval and personalized content generation.

Framework links neural data to user experience and affective states.

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.