Eye Feel You: A DenseNet-driven User State Prediction Approach

TL;DR

This paper introduces a DenseNet-based deep learning model to predict subjective states like fatigue and effort from eye-tracking gaze dynamics, aiming to improve longitudinal and cross-subject generalization.

Contribution

It presents a novel DenseNet-driven regression approach for predicting subjective reports from gaze velocity signals, reducing reliance on handcrafted features.

Findings

Model trained on earlier rounds generalizes to later rounds.

Model predicts subjective states for new individuals.

Deep learning captures systematic oculomotor patterns related to subjective states.

Abstract

Subjective self-reports, collected with eye-tracking data, reveal perceived states like fatigue, effort, and task difficulty. However, these reports are costly to collect and challenging to interpret consistently in longitudinal studies. In this work, we focus on determining whether objective gaze dynamics can reliably predict subjective reports across repeated recording rounds in the eye-tracking dataset. We formulate subjective-report prediction as a supervised regression problem and propose a DenseNet-based deep learning regressor that learns predictive representations from gaze velocity signals. We conduct two complementary experiments to clarify our aims. First, the cross-round generalization experiment tests whether models trained on earlier rounds transfer to later rounds, evaluating the models' ability to capture longitudinal changes. Second, cross-subject generalization tests models' robustness by predicting subjective outcomes for new individuals. These experiments aim to reduce reliance on hand-crafted feature designs and clarify which states of subjective experience systematically appear in oculomotor behavior over time.