Virtual Reality (VR) creates a highly realistic and controllable simulation environment that can easily manipulate users' perception of space and time. However, while the sensation of “losing track of time” is often associated with enjoyable experiences, both the relationship between time perception and user experience in VR, and the underlying mechanisms of time perception itself, remain largely unexplored. In this study, we first investigated how different zeitgebers—such as light color, music tempo, and VR task—affect time perception. We then introduced the Relative Subjective Time Change (RSTC) method to explore the link between time perception and user experience quantitatively. Furthermore, to uncover the mechanisms underlying time perception in VR, we propose a computational model based on CNN and Transformer, named the Time Perception Modeling Network (TPM-Net), which leverages multimodal physiological data to infer users' time perception states in VR. In a between-subject experiment with 56 participants, our results indicate that the VR task factor significantly influences time perception, with red light and slow-tempo music contributing to an underestimation of time. The RSTC method effectively demonstrates that a relative underestimation of time in VR is strongly associated with enhanced user experience, presence, and engagement. Moreover, the TPM-Net shows great potential in modeling time perception, enabling further inference of relative changes in both time perception and user experience. Our study comprehensively elucidates the mechanisms of time perception in VR. It provides valuable insights and promising methodologies for exploring the relationship between time perception and user experience. Modeling time perception through physiological data marks a first step toward objectively assessing users' temporal perception states, offering a promising tool for VR-based therapy and training systems that require precise temporal awareness.

BACKGROUND: To explore the value of multiparametric MRI markers for preoperative prediction of Ki-67 expression among patients with rectal cancer. METHODS: Data from 259 patients with postoperative pathological confirmation of rectal adenocarcinoma who had received enhanced MRI and Ki-67 detection was divided into 4 cohorts: training (139 cases), internal validation (in-valid, 60 cases), and external validation (ex-valid, 60 cases) cohorts. The patients were divided into low and high Ki-67 expression groups. In the training cohort, DWI, T2WI, and contrast enhancement T1WI (CE-T1) sequence radiomics features were extracted from MRI images. Radiomics marker scores and regression coefficient were then calculated for data fitting to construct a radscore model. Subsequently, clinical features with statistical significance were selected to construct a combined model for preoperative individualized prediction of rectal cancer Ki-67 expression. The models were internally and externally validated, and the AUC of each model was calculated. Calibration and decision curves were used to evaluate the clinical practicality of nomograms. RESULTS: Three models for predicting rectal cancer Ki-67 expression were constructed. The AUC and Delong test results revealed that the combined model had better prediction performance than other models in three chohrts. A decision curve analysis revealed that the nomogram based on the combined model had relatively good clinical performance, which can be an intuitive prediction tool for clinicians. CONCLUSION: The multiparametric MRI radiomics model can provide a noninvasive and accurate auxiliary tool for preoperative evaluation of Ki-67 expression in patients with rectal cancer and can support clinical decision-making.