Towards inferring cognitive state changes from pupil size variations in real world conditions

Abstract

The ability to infer cognitive state from pupil size provides an opportunity to reduce friction in human-computer interaction. For example, the computer could automatically turn off notifications when it detects, using pupil size, that the user is deeply focused on a task. However, our ability to do so has been limited. A principal reason for this is that pupil size varies with multiple factors (e.g., luminance and vergence), so isolating variations due to cognitive processes is challenging. In particular, rigorous benchmarks to detect cognitively-driven pupillary event from continuous stream of data in real-world settings have not been well-established. Motivated by these challenges, we first performed visual search experiments at room scale, with natural indoor conditions with real stimuli where the timing of the detection event was user-controlled. In spite of the natural experimental conditions, we found that the mean pupil dilation response to a cognitive state change (i.e., search target detected) was qualitatively similar and consistent with more controlled laboratory studies. Next, to address the challenge of detecting state changes from continuous data, we fit discriminant models using Support Vector Machine (SVM) computed on short epochs of 1-2 seconds extracted using rolling windows. We tested three different features (descriptive statistics, baseline corrected pupil size, and local Z-score) with our models. We obtained best performance using local Z-score as a feature (mean Area under the Curve (AUC) of 0.6). Our naturalistic experiments and modeling results provide a baseline for future research aimed at leveraging pupillometry for real-world applications.