IntroductionMicrosurgery is increasingly being adopted across various surgical fields. However, the acquisition and transfer of microsurgical skills largely depend on experience. Their opportunities to acquire skills are limited compared to athletes1. Appropriate education and training are essential for acquiring skills in microsurgery, as with other surgical procedures. Microsurgical techniques, which are widely used to reattach amputated limbs and various skin flaps, involve vascular anastomoses and nerve sutures in the extremities; these are often less than 1 mm in diameter and require the use of an operating microscope. In general, learning surgical techniques involves apprenticeship. However, in cases of finger amputation and severe limb trauma—where microsurgery is essential—the target tissue can be soft and deformed, making it difficult to quantify. Additionally, the timing for these surgeries is often urgent, precluding the use of various simulation technologies and intraoperative navigation that are becoming more common in other surgical training contexts.Recently, various simulation and augmented reality (AR) technologies have been enhancing the efficiency of learning common surgical procedures. In Europe, microscopes that incorporate these technologies are available, although they are not yet widespread.In daily activities like walking or reaching, it is possible to respond unconsciously to slight environmental changes or uncertainties. A seminal experiment highlighting this unconscious adaptation involves the treadmill walking test in decerebrated cats2. These animals were shown to maintain locomotion on a treadmill and adjust their gait patterns according to the treadmill’s speed, illustrating an implicit adaptive response to environmental dynamics. Furthermore, the anticipatory nature of motion control plays a pivotal role in unconscious adaptation. Experimental3,4 and theoretical5 evidence suggests that human movements are fine-tuned to align with environmental conditions prior to the conscious recognition of such changes. This principle of unconscious movement adaptation has been applied to the control of arm prostheses, enhancing their effective use in real-world settings6. Surgical experience involves the unconscious processing of surgical procedures. And surgeons must be acutely aware of their fingertips, the tips of their instruments, and even the surgical site itself. “Responsiveness” to unquantifiable uncertainties, which involves unconscious processing, is essential. This research was initiated based on the premise that it might be possible to visualize this phenomenon.The gaze analysis technique used in this study is non-invasive. It has started to be applied across various fields because it can reflect subtle unconscious changes in brain activity through the analysis of eye movement patterns, their distribution, and pupil diameter changes. There are reports suggesting that gaze analysis is beneficial for understanding decision-making processes in the manufacturing and distribution sectors7. In the medical field, numerous studies have utilized this method in laparoscopic surgery8,9, indicating its reliability as quantitative and objective data. This method may also enhance surgical training to improve performance10. There is a common saying that “the eyes are as expressive as the mouth,” suggesting that by observing eye movements, one can understand latent cognitive behaviors not expressed in words. Eye movements are indicative of brain function, and it has been reported that saccade movements can help detect diseases11.As for electromyograms, the use of surface electromyograms allows non-invasive and continuous data collection. The experimental results demonstrated a positive correlation between manipulation performance and maneuvering experience12. Additionally, this method helps to depict unconscious cognitive behavioral models13.ResultsGroups E and N required average suture times of 15 min and 17 min, respectively. Four patients in group N failed to suture six stitches within 20 min. Furthermore, the mean times for a single-stitch suture were 2.5 min for group E and 4.1 min for group N; this difference was not statistically significant (P > 0.05).A heat map of the distribution of eye gazing is presented in Fig. 1.Fig. 1Gaze heatmap.Full size imageThe suture work area of group E on the 3D display can be seen as a concentrated red area in the gaze heatmap (see the right column in Fig. 2). The gaze of group N (left column, Fig. 2) was broadly distributed, possibly because the participants spent time looking at their hands. A comparison of this heatmap with color-weighted averages revealed that the area of the N group (p