Abstract
Two-dimensional (2D) desktop and three-dimensional (3D) Virtual-Reality (VR) play a significant role in providing military personnel with training environments to hone their decision-making skills. The nature of the environment (2D versus 3D) and the order of task difficulty (novice to expert or expert to novice) may influence human performance in these environments. However, an empirical evaluation of these environments and their interaction with the order of task difficulty has been less explored. The primary objective of this research was to address this gap and explore the influence of different environments (2D desktop or 3D VR) and order of task difficulty (novice to expert or expert to novice) on human performance. In a lab-based experiment, a total of 60 healthy subjects executed scenarios with novice or expert difficulty levels across both 2D desktop environments (N = 30) and 3D VR environments (N = 30). Within each environment, 15 participants executed the novice scenario first and expert scenario second, and 15 participants executed the expert scenario first and novice scenario second. Results revealed that the participants performed better in the 3D VR environment compared to the 2D desktop environment. Participants performed better due to both expert training (performance in novice second better compared to novice first) and novice training (performance in expert second better compared to expert first). The combination of a 3D VR environment with expert training first and novice training second maximized performance. We expect to use these conclusions for creating effective training environments using VR technology.
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Notes
- 1.
For the analysis of the interaction effect, the performance/cognitive measures were averaged across both variations of order in the task difficulty, i.e., [Novice First (N1) + Expert Second (E2)]/2 and [Expert First (E1) → Novice Second (N2)]/2.
References
Jenkins, D.P., Stanton, N.A., Salmon, P.M., Walker, G.H.: A formative approach to developing synthetic environment fidelity requirements for decision-making training. Appl. Ergon. 42(5), 757–769 (2011)
Gonzalez, C., Fakhari, P., Busemeyer, J.: Dynamic decision making: learning processes and new research directions. Hum. Factors 59(5), 713–721 (2017)
Dreyfus, H., Dreyfus, S.E., Athanasiou, T.: Mind over machine. Simon and Schuster (2000)
Donovan, S.L., Triggs, T.: Investigating the effects of display design on unmanned underwater vehicle pilot performance (No. DSTO-TR-1931). Defense Science and Technology Organization, Maritime Platforms Division, Victoria, Australia (2006)
Donovan, S., Wharington, J., Gaylor, K., Henley, P.: Enhancing Situation Awareness for UUV Operators. ADFA, Canberra (2004)
McCarley, J.S., Wickens, C.D.: Human factors implications of UAVs in the national airspace (2005)
ter Haar, R.: Virtual reality in the military: present and future. In: 3rd Twente Student Conference IT (2005)
Adhikarla, V.K., Wozniak, P., Barsi, A., Singhal, D., Kovács, P.T., Balogh, T.: Freehand interaction with large-scale 3D map data. In: 2014 3DTV-Conference: The True Vision-Capture, Transmission and Display of 3D Video (3DTV-CON), pp. 1–4. IEEE, July 2014
Rao, Akash K., Chahal, J.S., Chandra, S., Dutt, V.: Virtual-Reality Training Under Varying Degrees of Task Difficulty in a Complex Search-and-Shoot Scenario. In: Tiwary, U.S., Chaudhury, S. (eds.) IHCI 2019. LNCS, vol. 11886, pp. 248–258. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-44689-5_22
Gonzalez, C., Dutt, V.: Instance-based learning: Integrating sampling and repeated decisions from experience. Psychol. Rev. 118(4), 523 (2011)
Bjork, R.A.: Memory and metamemory considerations in the, p. 185. Knowing about knowing, Metacognition (1994)
Srivastava, P., Rimzhim, A., Vijay, P., Singh, S., Chandra, S.: Desktop VR is better than nonambulatory HMD VR for spatial learning. Front. Robot. AI 6, 50 (2019)
Parmar, D., et al.: A comparative evaluation of viewing metaphors on psychophysical skills education in an interactive virtual environment. Virt. Real. 20(3), 141–157 (2016). https://doi.org/10.1007/s10055-016-0287-7
Murcia-López, M., Steed, A.: The effect of environmental features, self-avatar, and immersion on object location memory in virtual environments. Front. ICT 3, 24 (2016)
Rao, A.K., Pramod, B.S., Chandra, S., Dutt, V.: Influence of indirect vision and virtual reality training under varying manned/unmanned interfaces in a complex search-and-shoot simulation. In: Cassenti, D.N. (ed.) AHFE 2018. AISC, vol. 780, pp. 225–235. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-94223-0_21
Schneider, V.I., Healy, A.F., Bourne Jr., L.E.: What is learned under difficult conditions is hard to forget: contextual interference effects in foreign vocabulary acquisition, retention, and transfer. J. Mem. Lang. 46(2), 419–440 (2002)
Pyc, M.A., Rawson, K.A.: Testing the retrieval effort hypothesis: Does greater difficulty correctly recalling information lead to higher levels of memory? J. Mem. Lang. 60(4), 437–447 (2009)
For Building Fun: Groovy Little Games Quickly. Packt Publishing Ltd., Birmingham (2010)
Roosendaal, T., Selleri, S. (eds.): The Official Blender 2.3 Guide: Free 3D Creation Suite for Modeling, Animation, and Rendering, vol. 3. No Starch Press, San Francisco (2004)
Rao, A., Satyarthi, C., Dhankar, U., Chandra, S., Dutt, V.: Indirect visual displays: Influence of field-of-views and target-distractor base-rates on decision-making in a search-and-shoot task. In: 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 4326–4332. IEEE, October 2018
Hart, S.G., Staveland, L.E.: Development of NASA-TLX (Task Load Index): results of empirical and theoretical research. In: Advances in Psychology, vol. 52, pp. 139–183. North-Holland, Amsterdam (1988)
Acknowledgments
This research was supported by a grant from Defence Research and Development Organization (DRDO) titled “Development of a human performance modeling framework for visual cognitive enhancement in IVD, VR and AR paradigms” (IITM/DRDO-CARS/VD/110) to Prof. Varun Dutt.
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Rao, A.K., Chandra, S., Dutt, V. (2020). Desktop and Virtual-Reality Training Under Varying Degrees of Task Difficulty in a Complex Search-and-Shoot Scenario. In: Stephanidis, C., Chen, J.Y.C., Fragomeni, G. (eds) HCI International 2020 – Late Breaking Papers: Virtual and Augmented Reality. HCII 2020. Lecture Notes in Computer Science(), vol 12428. Springer, Cham. https://doi.org/10.1007/978-3-030-59990-4_31
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