Time perception research has advanced significantly over the years. However, some areas remain largely unexplored. This study addresses two such under-explored areas in timing research: (1) A quantitative analysis of time perception at an individual level, and (2) Time perception in an ecological setting. In this context, we trained a machine learning model to predict the direction of change in an individual's time production. The model's training data was collected using an ecologically valid setup. We moved closer to an ecological setting by conducting an online experiment with 995 participants performing a time production task that used naturalistic videos (no audio) as stimuli. The model achieved an accuracy of 61%. This was 10 percentage points higher than the baseline models derived from cognitive theories of timing. The model performed equally well on new data from a second experiment, providing evidence of its generalization capabilities. The model's output analysis revealed that it also contained information about the magnitude of change in time production. The predictions were further analysed at both population and individual level. It was found that a participant's previous timing performance played a significant role in determining the direction of change in time production. By integrating attentional-gate theories from timing research with feature importance techniques from machine learning, we explained model predictions using cognitive theories of timing. The model and findings from this study have potential applications in systems involving human-computer interactions where understanding and predicting changes in user's time perception can enable better user experience and task performance.

翻译：时间感知研究近年来取得了显著进展，但某些领域仍存在较大探索空间。本研究针对时间研究领域中两个尚未充分探索的方向：(1) 个体层面时间感知的量化分析；(2)生态情境下的时间感知。在此背景下，我们训练了一个机器学习模型来预测个体时间生成的变化方向。模型的训练数据通过生态效度良好的实验设置收集。我们采用在线实验形式，让995名参与者以自然主义视频（无音频）作为刺激材料执行时间生成任务，从而更贴近生态情境。该模型达到了61%的预测准确率，较基于时间认知理论的基线模型高出10个百分点。模型在第二次实验的新数据上表现出同等性能，证明了其泛化能力。模型输出分析表明，该模型还包含时间生成变化幅度的信息。我们进一步在群体和个体层面分析了预测结果，发现参与者先前的时间表现对决定时间生成变化方向具有重要作用。通过将时间研究中的注意门控理论与机器学习特征重要性技术相结合，我们运用时间认知理论解释了模型的预测机制。本研究提出的模型及发现可应用于人机交互系统，通过理解和预测用户时间感知的变化，有助于提升用户体验和任务表现。