Ocular Physiological Phenomena

To allow for a broader and more direct use of the Tobii EyeX device in scientific research, we have implemented a software Toolkit in Matlab which interfaces with the eye tracker controller. The Matlab Toolkit consists of four parts: 1) a client UDP (User Datagram Protocol) interface to connect Matlab with the Tobii server, 2) a set of basic connection functions for data transmission and reception, 3) a set of routines for standard use of the device, and 4) sample code provided to exemplify the usage of each function of the Toolkit in simple experiments in which we measure saccade, smooth pursuit, vergence and fixational eye movements. The graphical interface of the Toolkit has been implemented exploiting the Psychophysics Toolbox Version 3 (Kleiner et al. 2007 ; Brainard 1997 (link); Pelli 1997 (link)). The client UDP interface has been developed via the Tobii Gaze SDK, thus allowing the Toolkit to be compatible with other eye tracking devices produced by Tobii such as the Tobii Rex and Tobii Steelseries Sentry.
The quality of eye tracking data in scientific experiments may be affected by both the subject and the operator (Nyström et al. 2013 (link)). Level of compliance to task instructions, variable environment illumination, glasses or contact lenses, makeup and eye physiology are all relevant factors with regards to the eye tracking data quality. To allow for a rapid online evaluation of gaze data quality, the Toolkit implements routines that resemble and extend the functionalities provided by the Tobii EyeX Engine. In particular, we provide routines to: 1) correctly position the user with respect to the screen; 2) calibrate both binocular and monocular gaze measurements, 3) visually check the outcome of the calibration. Moreover, we also release the code used to evaluate accuracy, precision and sample frequency of Tobii EyeX, which can be easily adapted to other eye tracking devices. A detailed description of the Matlab Tookit is provided in Appendix A.

After obtaining the accurately corrected OCT images, in order to fit the real structure of mice eyes, we used MATLAB software to calculate the three-dimensional fitting surfaces of each refractive interface (cornea, lens, vitreous, retina). Among the 256 B-scan images, seven equally spaced images with clear boundaries (serial numbers as 97, 107, 177, 127, 137, 147, and 157) were selected to extract the three-dimensional coordinates of each refractive fitting curve. After the calculation using the Curve Fitting Tool of MATLAB, the extended polynomial coefficients of the three-dimensional surface of each biological structure can be obtained. Figure 4 shows schematic diagrams of the fitting results of the anterior segment of cornea (Figure 4A), stereograms of the whole eye before correction (Figure 4B), and the result of correction (Figure 4C). The coordinate system in the Figure 4A is a three-dimensional coordinate system with the corneal vertex as the origin and the z-axis direction as the visual axis direction.
Combining the biological parameters of each mice eye and the coefficients of the extended polynomial of the fitting surface with the refractive index data of mice eye tissue, the optical reflective system of mice eyes was imported into the ZEMAX software. The refractive index data used by the model was consistent with the correction process. The ray-tracing model established by ZEMAX is shown in Figure 5, where the incident wavelengths were set to the visible light series wavelengths preset by the software (0.655 and 0.450). Six different colors of light shown in Figure 5 represent six different fields of view angles of incident light (0°, 5°, 10°, 15°, 20°, and 25°). The optical system parameters such as aberration, wavefront diagram, and point spread function of the mice eye model can be analyzed by ZEMAX. In order to analyze the correlation between the data to investigate the mechanisms of myopia, the root mean square (RMS) wavefront aberration of the model calculated from ZEMAX was used.

The study is performed in a quiet eye-tracking laboratory with room temperature constantly maintained at 25°C. Each experiment takes about 40 min, the details are as follows (Figure 5).
First of all, the participants are asked to wear a VR eye tracker of head-mounted which calibrates eye movements by repeated measurements of their eyes by infrared light reflection. After calibration, they turn to watch a 60 s test video to adapt to the VR viewing mode, during which researchers introduce the experiment procedures and precautions. Subsequently, 18 virtual reality videos are randomly presented on the display screen of the VR eye tracker, each target video is about 20 s, and the blank video was spaced 3 s. The participants are free to move their eyes and head to watch the videos during the experiment, and they could withdraw from the experiment at any time if they feel uncomfortable. This part of the experiment mainly obtains the visual physiological data of the participants when viewing different VR forest videos, especially the average pupil diameter (Figure 6). Average pupil diameter records the aver-age pupil size change of participants in each VR forest video. In previous studies, it has often been regarded as a predictor of emotional arousal and autonomic activation (53 (link)–55 (link)). In fact, a dilator stimulation or a constrictor inhibition can cause pupillary dilation. Its average size in standard light conditions is about 3 mm (56 (link)). And in dim light or darkness, the pupil can enlarge to an average size of about 7 mm (57 (link)). Cognitively driven changes are usually smaller and rarely exceed 0.5 mm (58 ).
Eye-tracking data can measure participants' visual physiological indicators but is unable to distinguish specific visual psychological characteristics. Therefore, after the eye-tracking experiment, the participants are asked to fill in demographic information (gender, age, educational level) and forest visit information (visit frequency, visit duration), and then display 18 virtual reality videos randomly again for evaluation. The relationship between the physical environment and visual preferences has been demonstrated in previous studies (59 –61 (link)), and we select indicators of visual attractiveness and perceived safety to collect their visual psychological data. Between them, visual attraction is used to assess the perceived fascinating of visual environments (59 ), and perceived safety is often used to assess the perceived threat of the environment (8 (link), 60 (link)). The primary sources of threat are social incivilities (e.g., physical or sexual assault), whereas in natural areas there are additional threats (e.g., predators, snakes, spiders). Each item is assessed by a 5-point Likert scale (“not at all” to “very much”).