The authors are incredibly grateful to Mr. Li Yan for his help in recruiting pilot participants and acknowledge Ms. Bu Lingyun for her work on drawing pictures. The research was supported by the National Natural Science Foundation of China (grant number T2192931, 72071185), the National Brain Project (grant number&#160;STI2030-Major Projects2022ZD0208500), the National Key Laboratory Project of Human Factors Engineering (grant number SYFD062003), the National Key Laboratory Project of Human Factors Engineering (grant number 6142222210201), and year 2022 Major Projects of Military Logistic Research Grant and Key Project of Air Force Equipment Comprehensive Research&#160;(grant number KJ2022A000415).

All methods described here have been approved by the Institutional Review Board (IRB) of Tsinghua University, and informed consent was obtained from all participants. After completion, all participants were paid $12 (or a gift of equal value).
1. Participant selection
<ol>
	<li>Recruit participants according to a prior study of power analysis using G*Power software35 (see Table of Materials) to ensure the participant number meets the expected sample size given by G*Power, which equals 21. 
	NOTE: The expected sample size given by G*Power refers to the estimated number of participants needed in a study to achieve a desired level of statistical power based on the specified effect size, significance level, and statistical test used. The expected sample size is an important consideration in research planning. It helps researchers determine the feasibility and cost-effectiveness of their study design and ensures that the study has sufficient statistical power to detect meaningful effects.</li>
	<li>Ensure none participants have a history of epilepsy, heart or brain disease, recent endocrine or psychiatric medication, or severe skin allergies.</li>
	<li>Use the Snellen eye chart (use the metric system 6/6)36 to confirm that participants&#39; vision is normal or corrected-to-normal and that they don&#39;t have any visual impairments like color blindness or color weakness.</li>
	<li>Ensure that none of the participants have consumed alcohol or drugs within the previous 24 h that may affect their ability to fly.</li>
	<li>Ensure the participants have had no less than 6 h of sleep and are in good mental condition before the experiment.</li>
</ol>
2. Flight simulator hardware
<ol>
	<li>Check that all of the flight simulator&#39;s hardware is complete. According to its function, this hardware is organized into three modules (Table 1) (see Table of Materials). 
	NOTE: Researchers need to touch a metal rod before touching the equipment to avoid the risk posed by electrostatic induction.
	<ol>
		<li>Check the components of the VR, HMD (head-mounted display), and eye-tracking module with the help of Table 2.</li>
		<li>Ensure that all the flight simulator PC module components meet the following minimum requirements: 3.6G Hz processor, 4G internal memory, 64-bit operating system, and graphics card. The system will support all flight controllers.</li>
		<li>Check the components of the flight control module and ensure the device&#39;s parameter configuration is consistent with Table 3.</li>
	</ol>
	</li>
	<li>Install the hardware of the flight simulator according to the layout in Figure 1. Figure 2 shows how the hardware is connected.
	<ol>
		<li>Join the throttle and control panel physically and treat them as a unit.</li>
		<li>Connect the throttle, joystick, and pedal to the flight simulator PC module via USB.</li>
		<li>Connect the HMD to the flight simulator PC module via the link box.</li>
		<li>Connect the base stations and VR controllers to the HMD via the VR software on the PC.</li>
	</ol>
	</li>
</ol>
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td rowspan="2">VR head-mounted display(HMD) and eye tracking module</td>
			<td>1. Base Station</td>
		</tr>
		<tr>
			<td>2. VR HMD</td>
		</tr>
		<tr>
			<td>Flight Simulator PC module</td>
			<td>3. Flight Simulator PC</td>
		</tr>
		<tr>
			<td rowspan="3">Flight control module</td>
			<td>4. Flight Throttle</td>
		</tr>
		<tr>
			<td>5. Flight Joystick</td>
		</tr>
		<tr>
			<td>6. Flight Pedal</td>
		</tr>
	</tbody>
</table>
Table 1: Components of the three modules of flight simulator hardware.
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Main component</td>
			<td>Accessories</td>
		</tr>
		<tr>
			<td rowspan="4">VR HMD</td>
			<td>Headset cable (attached)</td>
		</tr>
		<tr>
			<td>Face cushion (attached)</td>
		</tr>
		<tr>
			<td>Cleaning cloth</td>
		</tr>
		<tr>
			<td>Earphone hole cap &#215; 2&#160;</td>
		</tr>
		<tr>
			<td rowspan="4">Link box</td>
			<td>&#160;Power adapter</td>
		</tr>
		<tr>
			<td>DisplayPort cable</td>
		</tr>
		<tr>
			<td>USB 3.0 cable</td>
		</tr>
		<tr>
			<td>Mounting pad</td>
		</tr>
		<tr>
			<td rowspan="3">Controllers (2018) &#215; 2</td>
			<td>Power adapters &#215; 2&#160;</td>
		</tr>
		<tr>
			<td>Lanyards &#215; 2&#160;</td>
		</tr>
		<tr>
			<td>Micro-USB cables &#215; 2&#160;</td>
		</tr>
		<tr>
			<td rowspan="2">Base Station 2.0 &#215; 2</td>
			<td>Power adapters &#215; 2&#160;</td>
		</tr>
		<tr>
			<td>Mounting Kit (2 mounts, 4 screws, and 4 wall anchors)</td>
		</tr>
	</tbody>
</table>
Table 2: List of components of the VR HMD and eye-tracking module.
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Device</td>
			<td>Parameter configuration</td>
		</tr>
		<tr>
			<td rowspan="5">Flight Joystick</td>
			<td>Nineteen action buttons</td>
		</tr>
		<tr>
			<td>One 8-way &#34;point of view&#34; hat</td>
		</tr>
		<tr>
			<td>Several 3D magnetic sensors</td>
		</tr>
		<tr>
			<td>One 5-coil spring system</td>
		</tr>
		<tr>
			<td>One 16-bit resolution (65536 x 65536 values).</td>
		</tr>
		<tr>
			<td rowspan="3">Flight Control Panel</td>
			<td>Fifteen action buttons</td>
		</tr>
		<tr>
			<td>One TRIM wheel</td>
		</tr>
		<tr>
			<td>Five programmable LEDs</td>
		</tr>
		<tr>
			<td rowspan="5">Flight Throttle</td>
			<td>Seventeen action buttons</td>
		</tr>
		<tr>
			<td>One mouse hat with a push button</td>
		</tr>
		<tr>
			<td>One 8-way &#34;point of view&#34; hat</td>
		</tr>
		<tr>
			<td>Several 3D magnetic sensors</td>
		</tr>
		<tr>
			<td>Two 14-bit resolution</td>
		</tr>
		<tr>
			<td rowspan="2">Flight Pedal</td>
			<td>Tension between 2.5 kg and 5 kg</td>
		</tr>
		<tr>
			<td>Angle between 35&#176; and 75&#176;</td>
		</tr>
	</tbody>
</table>
Table 3: The parameter configuration of the devices of the flight control module.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/65170/65170fig01.jpg" /> 
Figure 1: The layout of the VR flight simulator hardware. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/65170/65170fig01large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/65170/65170fig02.jpg" /> 
Figure 2: The connection of flight simulator hardware. (A) Flight control module. The throttle and control panel are physically joined and treated as a unit. If the term &#34;throttle&#34; is used in this study, it refers to both the throttle and the control panel. (B) Flight simulator PC module. A computer that meets the requirements outlined in step 2.2. (C) HMD and eye-tracking module. The software development kits (SDKs) for eye tracking and the 3D engine are kept in sync when installed on the same computer. Therefore, the eye-tracking functions and the operating system interact and work together. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/65170/65170fig02large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
3. Flight simulator software
<ol>
	<li>Ensure all the software (see Table of Materials) has been installed before the experiment begins. Information about all software used in the experiment is shown in Table 4.</li>
</ol>
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Name</td>
			<td colspan="4">Description</td>
		</tr>
		<tr>
			<td>VR software</td>
			<td colspan="4">A widely used tool for experiencing VR content on the hardware.</td>
		</tr>
		<tr>
			<td>VR app store</td>
			<td colspan="4">The app store for virtual reality where customers can explore, create, connect, and experience the content they love and need.</td>
		</tr>
		<tr>
			<td>Eye-tracking software</td>
			<td colspan="4">Eye-tracking software developed by the research team via Eye-tracking and 3D engine SDKs.</td>
		</tr>
		<tr>
			<td>FlySimulator</td>
			<td colspan="4">The main program of the flight simulator software, developed by the research team.</td>
		</tr>
		<tr>
			<td>Screen recording software</td>
			<td colspan="4">A free and open source software for video recording and live streaming.</td>
		</tr>
	</tbody>
</table>
Table 4: Information about all software used in the experiment.
4. Preparation before launching the flight simulator
NOTE: If this is one&#39;s first time running the eye-tracking program, perform the additional steps according to Figure 3. The eye-tracking program will activate automatically after the initial run.
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/65170/65170fig03.jpg" /> 
Figure 3: The additional steps when running the eye-tracking program for the first time. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/65170/65170fig03large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<ol>
	<li>Ensure that the HMD is turned on and connected to the computer.</li>
	<li>Place the base station diagonally to ensure the HMD can always be inside the base station&#39;s monitoring range. Keep the base station fixed to keep a stable VR environment.</li>
	<li>Set up a standing-only play area according to the prompts given by the VR software on the computer. 
	NOTE: The prompt allows a room-scale area or sets up a standing-only play area. Standing-only mode is generally used to experience VR scenes that do not require walking and can be selected when the user has limited space to move around. Therefore, in this experiment, set up a standing-only play area.</li>
	<li>Set the eye-tracking calibration. The eye-tracking system must be recalibrated every time the participant is switched.
	<ol>
		<li>Confirm that the participants are not using contact lenses, which causes eye tracking to malfunction.</li>
		<li>Open the eye-tracking calibration program using the VR controllers (see Table of Materials).</li>
		<li>Adjust the device height, interpupillary distance (IDP), and gaze point as the system directs.</li>
		<li>Get the participant&#39;s eyes to light up each spot for 2 s in a clockwise direction to verify the effectiveness of the eye-tracking calibration.</li>
	</ol>
	</li>
	<li>Connect the tuned flight control module and large screen display (i.e., at least a 27 inch monitor) to the same computer as the VR HMD. The screen allows the experimenter to view what is happening in the VR HMD simultaneously.</li>
</ol>
5. Experimental procedure
NOTE: The experiment is divided into four steps: &#34;collect information,&#34; &#34;introduce the task and operation,&#34; &#34;practice before the experiment,&#34; and &#34;conduct a formal experiment.&#34; The experimental process is summarized in Figure 4.
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/65170/65170fig04.jpg" /> 
Figure 4: The flowchart of the experiment. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/65170/65170fig04large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<ol>
	<li>Collect information from participants (15 min).
	<ol>
		<li>Fill out the payment details form with the payment information of the participants.</li>
		<li>Inform the participants to read the informed consent document and sign it.</li>
	</ol>
	</li>
	<li>Introduce the task and simulator operation to the participants (5 min).
	<ol>
		<li>Explain to the participants the traffic pattern and map (Figure 5).</li>
		<li>Instruct participants on how to use the VR flight simulator.</li>
		<li>Inform the participants that they can leave the experiment if they experience discomfort while simulating a flight.</li>
	</ol>
	</li>
	<li>Ask the participants to practice using the VR flight simulator (15 min).
	<ol>
		<li>Fit the VR HMD to the participants and calibrate the eye movements. Adjust the device height, interpupillary distance (IDP), and gaze point as instructed by the system.</li>
		<li>Open the screen recording software.</li>
		<li>Launch the FlySimulator program to assist participants with flight training.</li>
		<li>Instruct the participants to use the flight pedal, joystick, and throttle in concert to steer the aircraft as closely as possible to the reference line of flight (Figure 5).</li>
		<li>Reset the flight throttle and engine switch buttons after exiting the FlySimulator program, and stop recording the screen after the subject has completed the practice.</li>
	</ol>
	</li>
	<li>Conduct formal flight experiments (20 min).
	<ol>
		<li>Put on the VR HMD for the participants, and run OBS Studio to start recording the screen.</li>
		<li>Start the FlySimulator program, choose a fit perspective, start the engine, let go of the parking brake, and put the flight throttle to full.</li>
		<li>Exit the FlySimulator program, then reset the flight throttle and engine switch buttons. Stop recording the screen.</li>
	</ol>
	</li>
</ol>
<img alt="Figure 5" class="xfigimg" src="/files/ftp_upload/65170/65170fig05.jpg" /> 
Figure 5: The traffic pattern for the VR flight simulator. The pitch angle is the rotation angle around the left and right axes of the aircraft, and the reference line (or the center reference line) is right in the middle of the red and green lines. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/65170/65170fig05large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
6. Data analysis
<ol>
	<li>Analyze eye movement data.
	<ol>
		<li>Divide five area of interest (AOI) zones in this study; for more information about the calculation of AOIs, see Figure 6, based on the actual panel of the flight cockpit corresponding to the flight cockpit instrument (Figure 7).</li>
		<li>Calculate the AOI difference test using the percentage of dwell time in the AOI zone37, given the equipment feature of the VR oculomotor used in this study. 
		NOTE: The average percentage of dwell time is the cumulative percentage spent looking within an AOI divided by the total fixation time and averaged across participants.</li>
	</ol>
	</li>
	<li>Analyze flight performance data with custom-developed programming scripts using Python 3.10. The core algorithm for performance metrics was adapted from the QAR analysis method, inspired by the 35 BAE-146 aircraft QAR data provided by the National Aeronautics and Space Administration (NASA)38. 
	NOTE: Flight performance indicators for this study: the total flight time (the total length of time from takeoff to landing for each participant, in seconds), pitch angle 1 s before landing (pitch angle of the aircraft 1 s before landing, obtained from raw data, in degrees), mean distance to reference line (mean error of the spatial distance between the aircraft and the reference line during flight, in meters), and standard deviation of distance to reference line (standard deviation of the spatial distance between the plane and the reference line during flight, in meters).</li>
	<li>Conduct the statistical analyses.
	<ol>
		<li>Use the Shapiro-Wilk test39 to confirm the data&#39;s normality.</li>
		<li>Use statistical software (see Table of Materials) for descriptive statistics, Mann-Whitney U test, and Student&#39;s t-test.</li>
		<li>Depict the violin plots to indicate better the shape of the data distribution40. 
		NOTE: The explanation for the effect size measured using Cohen&#39;s d is as follows: 0.1 is very small, 0.2 is small, 0.5 is medium, 0.8 is large, 1.2 is very large, and 2.0 is huge41,42. The significance level was set at p &#60; 0.05.</li>
	</ol>
	</li>
</ol>
<img alt="Figure 6" class="xfigimg" src="/files/ftp_upload/65170/65170fig06.jpg" /> 
Figure 6: AOI preprocessing and calculating flow process. Sections 1 to 4 describe how the present study processed the pilots&#39; eye movement data up to the independent-sample t-test. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/65170/65170fig06large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 7" class="xfigimg" src="/files/ftp_upload/65170/65170fig07.jpg" /> 
Figure 7: Schematic diagram of AOI division of flight instrument. The function of instruments: (A) The airspeed indicator indicates &#65279;the aircraft&#39;s speed relative to the air. (B) The altitude indicator shows the aircraft&#39;s pitch and roll altitude. (C) The vertical speed indicator indicates the aircraft&#39;s ascent or descent speed. (D) The altitude indicator indicates the aircraft&#39;s barometric altitude. (E) The engine speed indicator indicates the speed of the aircraft engine. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/65170/65170fig07large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

&#65279;The authors declared no financial disclosure or conflicts of interest.

&#65279;The current study assessed if participants with flight experience had better flight performance than those without flight experience in a VR-based flight simulator. More importantly, it evaluated whether a more optimized eye movement pattern could be found in these participants with better flight performance. The results have significant differences between participants with and without flight experience in three key flying QAR indicators: pitch angle 1 s before landing, the mean distance to the reference line, and the standard deviation of the distance to the reference line (Figure 9B-D). These results show that participants with flight experience have less flying deviation and more stable flight performance. At the same time, the results also have significant differences between participants with and without flight experience in three key instrument AOIs: airspeed indicator, vertical speed indicator, and altitude indicator (Figure 8A-C). Further, participants with flight experience spend more time looking at critical flying instruments than those without flight experience.
In summary, flight expertise has an important impact on flight performance; experts&#39; flight performance data shows their flight track is more concentrated around the center reference line, which means they have better flight performance. In contrast, novices&#39; flight track deviates significantly from the center reference line. Furthermore, different flight expertise corresponds to different eye-scanning patterns; experts depend more on key flying instruments (i.e., airspeed indicator, vertical speed indicator, etc.) and have more gaze time on the devices. In other words, experts show a more structural and efficient eye-scanning pattern compared with novices. These results are consistent with Wickens&#39; research, which shows that expert pilots have a better visual mode26.
As mentioned earlier, the VR flight simulator can measure flight performance. Still, some critical steps need more attention. First, during the demonstration flight session, the mission should be introduced along with the flight map. Second, the propeller aircraft is biased to the right in the process of flying, so the left flight pedal should be pressed when starting, and the flight joystick should be kept on the left during the flight to offset the right bias keep the aircraft flying smoothly. Third, the flight throttle in the takeoff phase should be pushed to the maximum, then held at two-thirds throttle in the level flight phase. Fourth, participants should be reminded to control the aircraft by matching the flight pedal, joystick, and throttle to make it fly as close to the guideline of the center reference line as possible. Fifth, whenever one exits the FlySimulator program, the experimenter must reset the flight launch switch and throttle.
Given researchers&#39; diverse experimental needs, the protocol can be modified accordingly. First, the flight map can be easily changed as a practical scenario by configuring the script file (Supplementary File 1). Second, except for eye-movement data, electroencephalogram (EEG) data can be collected by integrating an EEG with dry electrodes in the HMD, as can heart rate data by a heart rate belt. Third, some subjective measures can be applied to the participants&#39; psychology before and after the experiment by adding some scales and questionnaires, such as the proactive personality scale43, the measurement of situation awareness (SA)44,45, the VR sickness questionnaire (VRSQ)46, and the NASA task load index (TLX)47, etc. In addition, when the flight simulation program starts computing the dynamics model data in real-time, sometimes the aircraft body altitude is not in the correct place, and the experimenter needs to press the grey button in the upper right corner of the flight joystick, thus resetting it. A situation can also occur where the aircraft loses control, and the experimenter needs to exit for a reset by pressing Alt + F4 on the keyboard.
The method also has its limitations. A prominent issue is that participants get motion sickness when coping with a complex flight task in an immersive VR scenario, thus possibly withdrawing from the experiment. In addition, compared with a traditional flight simulator with motion bases4, the current study&#39;s VR flight simulator is not equipped with a motion device. It thus cannot produce the actual sensation of movement. Finally, the flight performance records various flight parameters, which can be used to evaluate the flight performance29; only three key indicators are selected in the current research.
In any case, the VR flight simulator has unique advantages concerning existing methods. Above all, considering conventional flight simulation relies on bulky and expensive instruments to create a virtual world9, the current flight simulator based on VR has a lower cost. Besides, VR technology enables keeping a person in a fully immersive virtual environment1, so the VR flight simulator provides more realistic immersion than other methods. More importantly, this method builds a new platform based on VR technology for pilot selection in the future, integrating eye-tracking and flight dynamics technology, and thus can provide more comprehensive screening metrics compared with current methods.
The VR flight simulator shows excellent potential as a pilot selection platform. It is well known that current pilot selection is expensive and overly dependent on expert opinion. The current VR flight simulator can be used in pilot selection, significantly reducing costs and providing more objective data for selection. In addition, the VR simulator can be used as a human factor engineering tool that enables ergonomic assessment of the flight cockpit in the early stages of the design process2. Finally, researchers can use this virtual simulator platform to study hand-eye coordination, fatigue, distraction, mind wandering37, etc., by designing different experiments.

The European Aviation Safety Agency (2012) categorizes flight simulators as training facilities, flight and navigation program trainers, flight training equipment, and complete flight simulators1. To date, a range of flight simulators is available for training, from low-level tabletop systems to highly complicated motion-based full flight simulators2. The traditional simulator includes a flight dynamics model, a system simulation, a hardware cockpit, an external visualization, and an optional motion simulation3.
These traditional flight simulators have some advantages as effective flight training equipment. However, their cost is high and environmentally unfriendly, as the drive of each system requires substantial electrical energy, especially a full flight simulator, which requires high temperature and high-pressure fluid or air pressure, consumes much power and generates a lot of noise4.
However, a simple desktop simulator system is flexible and low-cost, with lower immersion and fewer interactions than a full flight simulator2. Therefore, it is essential to develop new flight simulators that combine the advantages of desktop systems and full flight simulators (in other words, the flexibility of a tabletop simulation and the immersion and interaction level close to a full flight simulator).
With the development of computer technology, especially virtual reality (VR) technology, a new type of flight simulator based on emerging VR technology is becoming a reality. The VR-based flight simulator is flexible, portable, low-cost, and has fewer space requirements than conventional flight simulators5. Researchers have created flight simulators based on VR technology over the past 20 years6,7,8,9,10,11; however, these VR flight simulators are mainly for flight training, and there are few for pilot selection. Still, with cost reduction and technology enhancement, VR-based simulators are changing and becoming feasible for personal selection. Some studies have used VR-based simulators for personal selection in different domains: Schijven et al.12 selected surgical trainees using a virtual reality simulator. Huang et al.13 developed a psychology selection instrument based on virtual reality technology for air force pilot recruitment. Wojciechowski and Wojtowicz14 assessed a candidate&#39;s capabilities as an unmanned aerial vehicle (UAV) pilot based on VR technology. Given that pilot selection is critical for the aviation industry, it is pressing to develop a new VR-based flight simulator focusing on pilot selection, as large-scale pilot selection is susceptible to the cost of the simulator and demands in the portability simulator system.
Eye movements provide cues for a pilot&#39;s performance. Different studies have found that the eye-scanning mode distinguishes the performance between expert and novice pilots. By comparing the scanning pattern between experts and novices, experts&#39; efficient and structural eye movement behavior and the inadequate scanning methods of beginners could be differentiated. Several aviation studies have found that pilots&#39; eye-scanning strategy highly relates to the level of expertise15,16,17,18,19,20,21,22,23,24. According to Bellenkes et al.25, the duration of experts&#39; fixations is shorter, and the frequency of their fixations on instruments is higher than that of novices. Almost the same conclusion was drawn by Kasarskis et al.26, who discovered that expert pilots have more fixations combined with shorter durations than novices, suggested that expert pilots have a better visual mode than novices. In another study, Lorenz et al.27 found that experts spend more time looking outside the cockpit than novices. These results have great practical value in the selection of newcomers.
Flight performance assessment is another critical factor for pilot selection. However, the following problems exist in pilot flight performance evaluation: conflicting expert opinions, more selection norms, and a unified selection theory. In the driving field, Horrey et al.28 compared the absolute value of lane departure from the centerline for different experimental conditions to assess driving performance. Back to the aviation domain, the flight quick access recorder (QAR) records all sorts of pilot manipulation parameters, aircraft parameters, environments, and warning information during flight29. More specifically, as the QAR indicators, the pitch angle is the rotation angle around the left and right axes of the aircraft30, and the reference line (or the center reference line) is right in the middle of the red and green lines28; these two flight parameters are used to evaluate the flight performance of participants with or without experience in the current study. These QAR data can be used to evaluate flight performance, yet to our best knowledge, they have seldom been used for personal training and selection in scientific research31,32.
Measurements of eye movement patterns can be used to assess and predict flight performance and guide pilot training and selection. Gerathewohl33 stated that the eye is the most important sensory organ of the pilot, processing 80% of the flight information. Pilots must acquire visual information from instruments in the cockpit and integrate it into a coherent image to manage the flight22. Further, optimal scanning behavior is essential to accomplish better flight performance15. However, no affordable flight simulator currently integrates an eye tracker to facilitate quantitative studies of the relationship between eye movements and flight performance.
The current study developed a new VR flight simulator to assess if participants with flight experience had better flight performance than those without flight experience. The VR flight simulator integrates eye tracking and a flight dynamics system allowing eye-movement pattern analysis and flight performance evaluation. In particular, it is worth mentioning that the VR flight simulator uses a VR eye tracker34, not a glass-like or desktop eye tracker, to analyze the area of interest (AOI)-based eye movement without time-consuming frame counting.
Finally, the present work can lead to an omnibus measurement for pilot selection in the future, from eye scanning path to objective flying performance data. With the help of the virtual flight simulator, the cost of flight selection will be significantly reduced, and the norm of pilots can be formed based on extensive data gathering. The work fills a gap between conventional and desktop simulators for flight selection needs.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>3D engine SDK</td>
			<td>Epic Games</td>
			<td>Unreal Engine 4 
			GameAnalytics Unreal SDK</td>
			<td>This SDK is a powerful yet flexible free analytics tool designed for games.</td>
		</tr>
		<tr>
			<td>CPU</td>
			<td>Intel</td>
			<td>IntelCore i9</td>
			<td>One of the most powerful CPU on the mainstream market.</td>
		</tr>
		<tr>
			<td>Eye tracking SDK</td>
			<td>Tobii</td>
			<td>Tobii XR SDK</td>
			<td>This SDK provide device agnostic access to eye tracking data to allow development for headsets from many different hardware vendors and is not limited to devices using Tobii Eye Tracking hardware.</td>
		</tr>
		<tr>
			<td>Eye tracking software</td>
			<td>Developed by the research team</td>
			<td></td>
			<td>A program that tracks the movement of a person&#39;s eyes while they are using a virtual reality HMD.</td>
		</tr>
		<tr>
			<td>FlySimulator program</td>
			<td>Developed by the research team</td>
			<td></td>
			<td>A software that simulates flying experiences in a virtual environment, using VR HMD and hand-held controllers.</td>
		</tr>
		<tr>
			<td>Graphics card</td>
			<td>NVIDIA</td>
			<td>GeForce RTX 3090 
			10496 NVIDIA CUDA Cores 
			1.70 GHz Boost Clock&#160;&#160; 
			24 GB Memory Size 
			GDDR6X Memory Type</td>
			<td>One of the most powerful graphics card on the mainstream market.</td>
		</tr>
		<tr>
			<td>Operating system (OS)</td>
			<td>Microsoft</td>
			<td>Windows XP</td>
			<td>An operating system (OS) developed and exclusively distributed by Microsoft Corporation</td>
		</tr>
		<tr>
			<td>Replica control panel</td>
			<td>THRUSTMASTER</td>
			<td>2960720 2971004 2962072 2960748 2960769</td>
			<td>U.S. Air Force A-10C attack aircraft HOTAS</td>
		</tr>
		<tr>
			<td>Replica joystick</td>
			<td>THRUSTMASTER</td>
			<td>2960720</td>
			<td>U.S. Air Force A-10C attack aircraft HOTAS</td>
		</tr>
		<tr>
			<td>Replica pedal</td>
			<td>THRUSTMASTER</td>
			<td></td>
			<td>TPR pendular rudder</td>
		</tr>
		<tr>
			<td>Replica throttle</td>
			<td>THRUSTMASTER</td>
			<td></td>
			<td>U.S. Air Force A-10C attack aircraft HOTAS</td>
		</tr>
		<tr>
			<td>Screen connected to PC</td>
			<td>Redmi</td>
			<td>RMMNT27NF, 27-inch, 1920 X 1080 resolution ratio</td>
			<td>Screen allows the experimenter to simultaneously view what is happening in the VR HMD</td>
		</tr>
		<tr>
			<td>Screen recording software</td>
			<td>OBS Project</td>
			<td>OBS Studio Version 28.0</td>
			<td>A free and open source software for video recording and live streaming</td>
		</tr>
		<tr>
			<td>Statistical power analysis software</td>
			<td>Open-Source</td>
			<td>G*power Version 3.1.9.6</td>
			<td>A free and user-friendly tool for estimating statistical power and sample size.</td>
		</tr>
		<tr>
			<td>Statistical software</td>
			<td>IBM</td>
			<td>SPSS Version 24.0</td>
			<td>A powerful statistical software platform</td>
		</tr>
		<tr>
			<td>Versatile statistics tool</td>
			<td>GraphPad Software</td>
			<td>GraphPad Prism Version 9.4.0</td>
			<td>A versatile statistics tool purpose-built for scientists-not statisticians</td>
		</tr>
		<tr>
			<td>VR app store</td>
			<td>HTC Corporation</td>
			<td>VIVE Software 2.0.17.6 / 2.1.17.6</td>
			<td>An app store for virtual reality where customers can explore, create, connect, and experience the content they love and need.</td>
		</tr>
		<tr>
			<td>VR head-mounted display (HMD)</td>
			<td>HTC Corporation</td>
			<td>VIVE Pro Eye</td>
			<td>A VR headset with precision eye tracking</td>
		</tr>
		<tr>
			<td>VR software</td>
			<td>Steam</td>
			<td>Steam VR Version 1.23</td>
			<td>A tool for experiencing VR content on the hardware</td>
		</tr>
	</tbody>
</table>

evaluating flight performance and eye movement patterns using virtual reality flight simulator

Efficient and economical performance evaluation of pilots has become critical to the aviation industry. With the development of virtual reality (VR) and the combination of eye-tracking technology, solutions to meet these needs are becoming a reality. Previous studies have explored VR-based flight simulators, focusing mainly on technology validation and flight training. The current study developed a new VR flight simulator to evaluate pilots' flight performance based on eye movement and flight indicators in a 3D immersive scene. During the experiment, 46 participants were recruited: 23 professional pilots and 23 college students without flight experience. The experiment results showed significant differences in flight performance between participants with and without flight experience, the former being higher than the latter. In contrast, those with flight experience showed more structured and efficient eye-movement patterns. These results of the differentiation of flight performance demonstrate the validity of the current VR flight simulator as a flight performance assessment method. The different eye-movement patterns with flight experience provide the basis for future flight selection. However, this VR-based flight simulator has shortcomings like motion feedback compared to traditional flight simulators. This flight simulator platform is highly flexible except for the apparent low cost. It can meet the diverse needs of researchers (e.g., measuring situation awareness, VR sickness, and workload by adding relevant scales).

For the current experiment, 23 experts with flight experience and 23 novices without flight experience were chosen. The participants were between 25 and 58 years of age (experts: M = 32.52 years, SD = 7.28 years; novices: M = 29.57 years, SD = 5.74 years). The gender of all participants was male. All the novices were recruited from Tsinghua University (students or faculty), and all the experts were from China Eastern Airlines.
Eye movement 
The eye movement data of the instrument AOIs, airspeed indicator, vertical speed indicator, altitude indicator, and engine speed indicator are not normally distributed, according to the Shapiro-Wilk test (all p-values &#60;0.05 ). Thus, the Mann-Whitney U test was used. The results showed that experts (M = 60.79%, SD = 7.72) paid more time looking at the flying instruments than novices (M = 53.31%, SD = 12.89; U = 141, p &#60; 0.01). More precisely, experts looked more at the airspeed indicator (M = 8.56%, SD = 5.45 for experts; M = 5.32%, SD = 4.98 for novices; U = 145.5, p &#60; 0.01), the vertical speed indicator (M = 13.11%, SD = 8.84 for experts; M = 5.33%, SD = 4.11 for novices; U = 127, p &#60; 0.01), and the altitude indicator (M = 2.83%, SD = 2.29 for experts; M = 1.34%, SD = 2.22 for novices; U = 159.5, p &#60; 0.05) than novices. The difference in the gaze time at the engine speed indicator was insignificant between experts (M = 0.31%, SD = 0.56) and novices (M = 0.47%, SD = 1.16, p &#62; 0.05 ). An independent-sample t-test was performed on the altitude indicator, and the result was marginally significant [t(44) = 1.843, p = 0.072, Cohen&#39;s d = 0.541]; novices (M = 31.03%, SD = 12.2) spent more time looking at the altitude indicator than experts (M = 25.15%, SD = 9.23).
As shown in Figure 8, compared with the novices, the experts&#39; eye movement data were more evenly distributed around the mean value in the airspeed indicator (Figure 8A), the altitude indicator (Figure 8B), the vertical speed indicator (Figure 8C), and the altitude indicator (Figure 8D).
<img alt="Figure 8" class="xfigimg" src="/files/ftp_upload/65170/65170fig08.jpg" /> 
Figure 8: Means and&#160;data distribution of percent dwell time. (A)&#160;Airspeed indicator.&#160;(B)&#160;Altitude indicator. (C)&#160;Vertical speed indicator. (D)&#160;Altitude indicator. *p &#60; 0.05, **p &#60; 0.01, and ***p &#60; 0.001. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/65170/65170fig08large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Flight performance 
The data of the pitch angle 1 s before landing, the mean distance to the reference line, and the standard deviation of the distance to the reference line are not normally distributed, according to the Shapiro-Wilk test (all p-values &#60;0.05 ). Thus, the Mann-Whitney U test was used. The pitch angle 1 s before landing for novices (M = -12.54&#176;, SD = 29.03) was significantly smaller than that of experts (M = 3.97&#176;, SD = 24.43; U = 130, p &#60; 0.01), which indicated a more stable approach and better landing posture control for experts. The mean distance to the reference line for experts (M = 176.67 m, SD = 205.52) was significantly smaller than that of novices (M = 873.89 m, SD = 818.43;&#160;U = 439, p &#60; 0.001), which represents a minor flying deviation. The standard deviation of the distance to the reference line for experts (M&#160;= 211.52 m, SD = 225.76) was also significantly smaller than that of novices (M&#160;= 675.78 m, SD = 589.07;&#160;U = 420, p &#60; 0.01), which represents a more stable and reliable flight performance. These results showed that experts had a better flying performance than novices. An independent-sample t-test was performed on the total flight time, and the result showed a marginally significant difference [t(44) = 1.835, p = 0.076, Cohen&#39;s d = 0.541]; experts (M = 759.06 s, SD = 163.58) had a shorter total flight time than novices (M = 902.32 s, SD = 336.73).
As shown in Figure 9, compared with the novices, the experts&#39; flight performance data was mainly concentrated around the mean value in the total flight time (Figure 9A), the mean distance to the reference line (Figure 9B), the pitch angle 1 s before landing (Figure 9C), and the standard deviation of the distance to the reference line (Figure 9D).
<img alt="Figure 9" class="xfigimg" src="/files/ftp_upload/65170/65170fig09.jpg" /> 
Figure 9: Means and data distribution. (A)&#160;The total flight time. (B)&#160;Mean distance to reference line. (C)&#160;Pitch angle 1 s before landing. (D)&#160;The standard deviation of the distance to the reference line. *p &#60; 0.05, **p &#60; 0.01. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/65170/65170fig09large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Supplementary File 1: The script file of airports&#39; configuration. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/65170/Supplemantary File 1.pdf" target="_blank">Please click here to download this File.</a>

Watch this Scientific Journal Video about Evaluating Flight Performance and Eye Movement Patterns Using Virtual Reality Flight Simulator at JoVE.com

Evaluating Flight Performance and Eye Movement Patterns Using Virtual Reality Flight Simulator

A new virtual reality flight simulator was built, which enables efficient and low-cost evaluation of flight performance and eye movement patterns. It also provides a high-potential research tool for ergonomics and other research.

Efficient and economical performance evaluation of pilots has become critical to the aviation industry. With the development of virtual reality (VR) and ...

evaluating-flight-performance-eye-movement-patterns-using-virtual

Nanyang Technological University

Research

JoVE Journal

Behavior

807 Views.  Tsinghua University. A new virtual reality flight simulator was built, which enables efficient and low-cost evaluation of flight performance and eye movement patterns. It also provides a high-potential research tool for ergonomics and other research.

Video: Evaluating Flight Performance and Eye Movement Patterns Using Virtual Reality Flight Simulator

Two-photon Calcium Imaging in Mice Navigating a Virtual Reality Environment

In recent years, two-photon imaging has become an invaluable tool in neuroscience, as it allows for chronic measurement of the activity of genetically identified cells during behavior1-6. Here we describe methods to perform two-photon imaging in mouse cortex while the animal navigates a virtual reality environment. We focus on the aspects of the experimental procedures that are key to imaging in a behaving animal in a brightly lit virtual environment. The key problems that arise in this experimental setup that we here address are: minimizing brain motion related artifacts, minimizing light leak from the virtual reality projection system, and minimizing laser induced tissue damage. We also provide sample software to control the virtual reality environment and to do pupil tracking. With these procedures and resources it should be possible to convert a conventional two-photon microscope for use in behaving mice.

Here we describe the experimental procedures involved in two-photon imaging of mouse cortex during behavior in a virtual reality environment.

In recent years, two-photon imaging has become an invaluable tool in neuroscience, as it allows for chronic measurement of the activity of genetically ...

Early Metamorphic Insertion Technology for Insect Flight Behavior Monitoring

Early Metamorphosis Insertion Technology (EMIT) is a novel methodology for integrating microfabricated neuromuscular recording and actuation platforms on insects during their metamorphic development. Here, the implants are fused within the structure and function of the neuromuscular system as a result of metamorphic tissue remaking. The implants emerge with the insect where the development of tissue around the electronics during pupal development results in a bioelectrically and biomechanically enhanced tissue interface. This relatively more reliable and stable interface would be beneficial for many researchers exploring the neural basis of the insect locomotion with alleviated traumatic effects caused during adult stage insertions. In this article, we implant our electrodes into the indirect flight muscles of Manduca sexta. Located in the dorsal-thorax, these main flight powering dorsoventral and dorsolongitudinal muscles actuate the wings and supply the mechanical power for up and down strokes. Relative contraction of these two muscle groups has been under investigation to explore how the yaw maneuver is neurophysiologically coordinated. To characterize the flight dynamics, insects are often tethered with wires and their flight is recorded with digital cameras. We also developed a novel way to tether Manduca sexta on a magnetically levitating frame where the insect is connected to a commercially available wireless neural amplifier. This set up can be used to limit the degree of freedom to yawing &#8220;only&#8221; while transmitting the related electromyography signals from dorsoventral and dorsolongitudinal muscle groups.

We present a novel surgical procedure to implant electrodes in Manduca sexta during its early metamorphic stages. This technique allows mechanically stable and electrically reliable coupling with the neuromuscular tissue to study flight neurophysiology dynamics. We also present a novel magnetic levitation platform for tethered studies of insect yaw.

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Development of a Virtual Reality Assessment of Everyday Living Skills

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A challenge for proving treatment efficacy for cognitive impairments in schizophrenia is finding the optimizing measurement of skills related to everyday functioning.&#160; The Virtual Reality Functional Capacity Assessment Tool (VRFCAT) is an interactive gaming based computerized measure aimed at skills associated with everyday functioning, including baseline impairments and treatment related changes.

Cognitive impairments affect the majority of patients with schizophrenia and these impairments predict poor long term psychosocial outcomes.&#160; ...

Multifunctional Setup for Studying Human Motor Control Using Transcranial Magnetic Stimulation, Electromyography, Motion Capture, and Virtual Reality

The study of neuromuscular control of movement in humans is accomplished with numerous technologies. Non-invasive methods for investigating neuromuscular function include transcranial magnetic stimulation, electromyography, and three-dimensional motion capture. The advent of readily available and cost-effective virtual reality solutions has expanded the capabilities of researchers in recreating &#8220;real-world&#8221; environments and movements in a laboratory setting. Naturalistic movement analysis will not only garner a greater understanding of motor control in healthy individuals, but also permit the design of experiments and rehabilitation strategies that target specific motor impairments (e.g. stroke). The combined use of these tools will lead to increasingly deeper understanding of neural mechanisms of motor control. A key requirement when combining these data acquisition systems is fine temporal correspondence between the various data streams. This protocol describes a multifunctional system&#8217;s overall connectivity, intersystem signaling, and the temporal synchronization of recorded data. Synchronization of the component systems is primarily accomplished through the use of a customizable circuit, readily made with off the shelf components and minimal electronics assembly skills.

Transcranial magnetic stimulation, electromyography, and 3D motion capture are commonly used non-invasive techniques for investigating neuromuscular function in humans. In this paper, we describe a protocol that synchronously samples data generated by all three of these tools along with the unique addition of virtual reality stimulus presentation and feedback.

The study of neuromuscular control of movement in humans is accomplished with numerous technologies. Non-invasive methods for investigating neuromuscular ...

Using Virtual Reality to Transfer Motor Skill Knowledge from One Hand to Another

As far as acquiring motor skills is concerned, training by voluntary physical movement is superior to all other forms of training (e.g. training by observation or passive movement of trainee&#39;s hands by a robotic device). This obviously presents a major challenge in the rehabilitation of a paretic limb since voluntary control of physical movement is limited. Here, we describe a novel training scheme we have developed that has the potential to circumvent this major challenge. We exploited the voluntary control of one hand and provided real-time movement-based manipulated sensory feedback as if the other hand is moving. Visual manipulation through virtual reality (VR) was combined with a device that yokes left-hand fingers to passively follow right-hand voluntary finger movements. In healthy subjects, we demonstrate enhanced within-session performance gains of a limb in the absence of voluntary physical training. Results in healthy subjects suggest that training with the unique VR setup might also be beneficial for patients with upper limb hemiparesis by exploiting the voluntary control of their healthy hand to improve rehabilitation of their affected hand.

We describe a novel virtual-reality based setup which exploits voluntary control of one hand to improve motor-skill performance in the other (non-trained) hand. This is achieved by providing real-time movement-based sensory feedback as if the non-trained hand is moving. This new approach may be used to enhance rehabilitation of patients with unilateral hemiparesis.

As far as acquiring motor skills is concerned, training by voluntary physical movement is superior to all other forms of training (e.g. training by ...

Mobile Game-based Virtual Reality Program for Upper Extremity Stroke Rehabilitation

Stroke rehabilitation requires repetitive, intensive, goal-oriented therapy. Virtual reality (VR) has the potential to satisfy these requirements. Game-based therapy can promote patients&#39; engagement in rehabilitation therapy as a more interesting and a motivating tool. Mobile devices such as smartphones and tablet PCs can provide personalized home-based therapy with interactive communication between patients and clinicians. In this study, a mobile VR upper extremity rehabilitation program using game applications was developed. The findings from the study show that the mobile game-based VR program effectively promotes upper extremity recovery in patients with stroke. In addition, patients completed two weeks of treatment using the program without adverse effects and were generally satisfied with the program. This mobile game-based VR upper extremity rehabilitation program can substitute for some parts of the conventional therapy that are delivered one-on-one by an occupational therapist. This time-efficient, easy to implement, and clinically effective program would be a good candidate tool for tele-rehabilitation for upper extremity recovery in patients with stroke. Patients and therapists can collaborate remotely through these e-health rehabilitation programs while reducing economic and social costs.

Here, we present a protocol to develop and apply a mobile game-based virtual reality program for the recovery of upper limb dysfunction in patients with stroke. The present study shows that the mobile program is feasible and effectively promotes upper limb recovery in stroke patients.

Stroke rehabilitation requires repetitive, intensive, goal-oriented therapy. Virtual reality (VR) has the potential to satisfy these requirements. ...

Measuring the Kinematics of Daily Living Movements with Motion Capture Systems in Virtual Reality

The inability to complete instrumental activities of daily living (IADL) is a precursor to various neuropsychological diseases. Questionnaire-based assessments of IADL are easy to use but prone to subjective bias. Here, we describe a novel virtual reality (VR) test to assess two complex IADL tasks: handling financial transactions and using public transportation. While a participant performs the tasks in a VR setting, a motion capture system traces the position and orientation of the dominant hand and head in a three-dimensional Cartesian coordinate system. Kinematic raw data are collected and converted into &#39;kinematic performance measures,&#39; i.e., motion trajectory, moving distance, and time to completion. Motion trajectory is the path of a particular body part (e.g., dominant hand or head) in space. Moving distance refers to the total distance of the trajectory, and time to completion is how long it took to complete an IADL task. These kinematic measures could discriminate patients with cognitive impairment from healthy controls. The development of this kinematic measuring protocol allows detection of early IADL-related cognitive impairments.

We designed a virtual reality test to assess instrumental activities of daily living (IADL) with a motion capture system. We propose a detailed kinematic analysis to interpret the participant&#39;s various movements, including trajectory, moving distance, and time to completion to evaluate IADL capabilities.

The inability to complete instrumental activities of daily living (IADL) is a precursor to various neuropsychological diseases. Questionnaire-based ...

A Networked Desktop Virtual Reality Setup for Decision Science and Navigation Experiments with Multiple Participants

Investigating the interactions among multiple participants is a challenge for researchers from various disciplines, including the decision sciences and spatial cognition. With a local area network and dedicated software platform, experimenters can efficiently monitor the behavior of the participants that are simultaneously immersed in a desktop virtual environment and digitalize the collected data. These capabilities allow for experimental designs in spatial cognition and navigation research that would be difficult (if not impossible) to conduct in the real world. Possible experimental variations include stress during an evacuation, cooperative and competitive search tasks, and other contextual factors that may influence emergent crowd behavior. However, such a laboratory requires maintenance and strict protocols for data collection in a controlled setting. While the external validity of laboratory studies with human participants is sometimes questioned, a number of recent papers suggest that the correspondence between real and virtual environments may be sufficient for studying social behavior in terms of trajectories, hesitations, and spatial decisions. In this article, we describe a method for conducting experiments on decision-making and navigation with up to 36 participants in a networked desktop virtual reality setup (i.e., the Decision Science Laboratory or DeSciL). This experiment protocol can be adapted and applied by other researchers in order to set up a networked desktop virtual reality laboratory.

This paper describes a method for conducting multi-user experiments on decision-making and navigation using a networked computer laboratory.

Investigating the interactions among multiple participants is a challenge for researchers from various disciplines, including the decision sciences and ...

Virtual Reality Experiments with Physiological Measures

Virtual reality (VR) experiments are increasingly employed because of their internal and external validity compared to real-world observation and laboratory experiments, respectively. VR is especially useful for geographic visualizations and investigations of spatial behavior. In spatial behavior research, VR provides a platform for studying the relationship between navigation and physiological measures (e.g., skin conductance, heart rate, blood pressure). Specifically, physiological measures allow researchers to address novel questions and constrain previous theories of spatial abilities, strategies, and performance. For example, individual differences in navigation performance may be explained by the extent to which changes in arousal mediate the effects of task difficulty. However, the complexities in the design and implementation of VR experiments can distract experimenters from their primary research goals and introduce irregularities in data collection and analysis. To address these challenges, the Experiments in Virtual Environments (EVE) framework includes standardized modules such as participant training with the control interface, data collection using questionnaires, the synchronization of physiological measurements, and data storage. EVE also provides the necessary infrastructure for data management, visualization, and evaluation. The present paper describes a protocol that employs the EVE framework to conduct navigation experiments in VR with physiological sensors. The protocol lists the steps necessary for recruiting participants, attaching the physiological sensors, administering the experiment using EVE, and assessing the collected data with EVE evaluation tools. Overall, this protocol will facilitate future research by streamlining the design and implementation of VR experiments with physiological sensors.

Virtual reality (VR) experiments can be difficult to implement and require meticulous planning. This protocol describes a method for the design and implementation of VR experiments that collect physiological data from human participants. The Experiments in Virtual Environments (EVE) framework is employed to accelerate this process.

Virtual reality (VR) experiments are increasingly employed because of their internal and external validity compared to real-world observation and ...

Using Flight Mills to Measure Flight Propensity and Performance of Western Corn Rootworm, Diabrotica virgifera virgifera (LeConte)

The western corn rootworm, Diabrotica virgifera virgifera (LeConte) (Coleoptera: Chrysomelidae), is an economically important pest of corn in the northern United States. Some populations have developed resistance to management strategies including transgenic corn that produces insecticidal toxins derived from the bacterium Bacillus thuringiensis (Bt). Knowledge of western corn rootworm dispersal is of critical importance for models of resistance evolution, spread, and mitigation. Flight behavior of an insect, especially over a long distance, is inherently difficult to observe and characterize. Flight mills provide a means to directly test developmental and physiological impacts and consequences of flight in the laboratory that cannot be obtained in field studies. In this study, flight mills were used to measure the timing of flight activity, total number of flights, and the distance, duration, and speed of flights taken by female rootworms during a 22-h test period. Sixteen flight mills were housed in an environmental chamber with programmable lighting, temperature, and humidity control. The flight mill described is of a typical design, where a flight arm is free to rotate about a central pivot. Rotation is caused by flight of an insect tethered to one end of the flight arm, and each rotation is recorded by a sensor with a time-stamp. Raw data are compiled by software, which are subsequently processed to provide summary statistics for flight parameters of interest. The most difficult task for any flight mill study is attachment of the tether to the insect with an adhesive, and the method used must be tailored to each species. The attachment must be strong enough to hold the insect in a rigid orientation and to prevent detachment during movement, while not interfering with natural wing motion during flight. The attachment process requires dexterity, finesse, and speed, making video footage of the process for rootworms of value.

Using Flight Mills to Measure Flight Propensity and Performance of Western Corn Rootworm, Diabrotica virgifera virgifera LeConte

Flight mills are important tools for comparing how age, sex, mating status, temperature, or various other factors may influence an insect&#8217;s flight behavior. Here we describe protocols to tether and measure the flight propensity and performance of western corn rootworm under different treatments.

The western corn rootworm, Diabrotica virgifera virgifera (LeConte) (Coleoptera: Chrysomelidae), is an economically important pest of corn in the northern ...