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.

翻译：结合眼追踪数据收集的主观自我报告能够反映疲劳度、认知负荷与任务难度等感知状态。然而，此类报告在纵向研究中不仅采集成本高昂，且难以保持解释的一致性。本研究旨在探究客观注视动态特征是否能够可靠预测眼动数据集中多次重复记录轮次的主观报告。我们将主观报告预测构建为监督回归问题，并提出一种基于DenseNet的深度学习回归器，该模型能够从注视速度信号中学习预测性表征。我们通过两项互补实验阐明研究目标：首先，跨轮次泛化实验检验基于前期轮次训练的模型能否迁移至后期轮次，以此评估模型捕捉纵向变化的能力；其次，跨被试泛化实验通过预测新个体的主观结果来检验模型的鲁棒性。这些实验旨在降低对手工特征设计的依赖，并明确哪些主观体验状态会随时间推移系统性地体现在眼动行为中。