In AI We Trust: Investigating the Relationship between Biosignals, Trust and Cognitive Load in VR
Abstract
Human trust is a psycho-physiological state that is difficult to measure, yet is becoming increasingly important for the design of human-computer interactions. This paper explores if human trust can be measured using physiological measures when interacting with a computer interface, and how it correlates with cognitive load. In this work, we present a pilot study in Virtual Reality (VR) that uses a multi-sensory approach of Electroencephalography (EEG), galvanic skin response (GSR), and Heart Rate Variability (HRV) to measure trust with a virtual agent and explore the correlation between trust and cognitive load. The goal of this study is twofold; 1) to determine the relationship between biosignals, or physiological signals with trust and cognitive load, and 2) to introduce a pilot study in VR based on cognitive load level to evaluate trust. Even though we could not report any significant main effect or interaction of cognitive load and trust from the physiological signal, we found that in low cognitive load tasks, EEG alpha band power reflects trustworthiness on the agent. Moreover, cognitive load of the user decreases when the agent is accurate regardless of task’s cognitive load. This could be possible because of small sample size, tasks not stressful enough to induce high cognitive load due to lab study and comfortable environment or timestamp synchronisation error due to fusing data from various physiological sensors with different sample rate.
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No study has explored the characterization of human trust in and reliance on AI through eye gaze tracking.
“However, no study has explored the characterization of human trust in and reliance on AI through eye gaze tracking.”
From Understanding User Reliance on AI in Assisted Decision-Making (2022) · cites this paper as [23]
Cites
Rempel, J. K., Holmes, J. G., & Zanna, M. P. (1985) · Journal of Personality and Social Psychology · doi:10.1037/0022-3514.49.1.95 · Cited by 3,146 · cited 1× · see citing works
“This means that it carries cognitive components because it is the calculation of subjective probability given a specific situation, which explains its correlation with cognitive load [Rempel et al. 1985; Samson and Kostyszyn 2015].”
BackgroundTrust in Automation: Integrating Empirical Evidence on Factors That Influence Trust
Hoff, K. A., & Bashir, M. (2015) · Human Factors: The Journal of the Human Factors and Ergonomics Society · doi:10.1177/0018720814547570 · Cited by 2,817 · see citing works
Kubios HRV–heart rate variability analysis software.
Tarvainen, M. P., Niskanen, J.-P., Lipponen, J. A., Ranta-aho, P. O., & Karjalainen, P. A. (2014) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2013.07.024 · Cited by 2,720 · see citing works
A Meta-Analysis of Factors Affecting Trust in Human-Robot Interaction
Hancock, P. A., Billings, D. R., Schaefer, K. E., Chen, J. Y. C., de Visser, E. J., & Parasuraman, R. (2011) · Human Factors: The Journal of the Human Factors and Ergonomics Society · doi:10.1177/0018720811417254 · Cited by 2,051 · cited 1× · see citing works
“…towards robots is a combination of human-related (expertise, competency, experience, demographic, comfort, etc.), robot-related (reliability, predictability, failure rates, personality, etc.), and environmental (membership, culture, communication, complexity, etc.) factors [Hancock et al. 2011].”
Is Anybody out There? Antecedents of Trust in Global Virtual Teams
Jarvenpaa, S. L., Knoll, K., & Leidner, D. E. (1998) · Journal of Management Information Systems · doi:10.1080/07421222.1998.11518185 · Cited by 1,906 · see citing works
Barber, B. (1983) · DigitalGeorgetown (Georgetown University Library) · Cited by 1,801 · see citing works
Foundations for an Empirically Determined Scale of Trust in Automated Systems
Jian, J.-Y., Bisantz, A. M., & Drury, C. G. (2000) · International Journal of Cognitive Ergonomics · doi:10.1207/s15327566ijce0401_04 · Cited by 1,799 · see citing works
Age differences in short-term retention of rapidly changing information.
Kirchner, W. K. (1958) · Journal of Experimental Psychology · doi:10.1037/h0043688 · Cited by 1,798 · see citing works
A continuous measure of phasic electrodermal activity
Benedek, M., & Kaernbach, C. (2010) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2010.04.028 · Cited by 1,689 · see citing works
Frontal theta activity in humans increases with memory load in a working memory task
Jensen, O., & Tesche, C. D. (2002) · European Journal of Neuroscience · doi:10.1046/j.1460-9568.2002.01975.x · Cited by 1,379 · see citing works
Using Electroencephalography to Measure Cognitive Load
Antonenko, P., PAAS, F. G. W. C., Grabner, R. H., & van Gog, T. (2010) · Educational Psychology Review · doi:10.1007/s10648-010-9130-y · Cited by 745 · see citing works
Avatars, People, and Virtual Worlds: Foundations for Research in Metaverses
Davis, A. J., Murphy, J., Owens, D. M., Khazanchi, D., & Zigurs, I. (2009) · Journal of the Association for Information Systems · doi:10.17705/1jais.00183 · Cited by 686 · see citing works
Visual Computing as a Key Enabling Technology for Industrie 4.0 and Industrial Internet
Posada, J., Toro, C., Barandiarán, Í., Oyarzun, D., Stricker, D., Amicis, R. D., Pinto, E. B., Eisert, P., Döllner, J., & Vallarino, I. (2015) · IEEE Computer Graphics and Applications · doi:10.1109/mcg.2015.45 · Cited by 618 · see citing works
Qiu, L., & Benbasat, I. (2005) · International Journal of Human-Computer Interaction · doi:10.1207/s15327590ijhc1901_6 · Cited by 232 · see citing works
Feasibility and pragmatics of classifying working memory load with an electroencephalograph
Grimes, D. B., Tan, D. S., Hudson, S. E., Shenoy, P., & Rao, R. P. N. (2008) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/1357054.1357187 · Cited by 196 · see citing works
A Classification Model for Sensing Human Trust in Machines Using EEG and GSR
Akash, K., Hu, W.-L., Jain, N., & Reid, T. (2018) · ACM Transactions on Interactive Intelligent Systems · doi:10.1145/3132743 · Cited by 159 · see citing works
Cognitive Load Measurement in a Virtual Reality-Based Driving System for Autism Intervention
Zhang, L., Wade, J. W., Bian, D., Fan, J., Swanson, A. R., Weitlauf, A. S., Warren, Z. E., & Sarkar, N. (2016) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2016.2582490 · Cited by 142 · see citing works
The role of post-use trust in the acceptance of a technology: Drivers and consequences
Ortega, B. H. (2011) · Technovation · doi:10.1016/j.technovation.2011.07.001 · Cited by 133 · see citing works
Recent Advancements in Medical Simulation: Patient‐Specific Virtual Reality Simulation
Willaert, W. I. M., Aggarwal, R. K., HERZEELE, I. V., Cheshire, N. J. W., & Vermassen, F. E. G. (2012) · World Journal of Surgery · doi:10.1007/s00268-012-1489-0 · Cited by 131 · see citing works
COGCAM: Contact-free Measurement of Cognitive Stress During Computer Tasks with a Digital Camera
McDuff, D. J., Hernandez, J., Gontarek, S., & Picard, R. W. (2016) · Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems · doi:10.1145/2858036.2858247 · Cited by 113 · see citing works
Hart, S. G. (1986) · NASA Technical Reports Server (NASA) · Cited by 112 · see citing works
Brain measurement for usability testing and adaptive interfaces
Hirshfield, L., Solovey, E., Girouard, A., Kebinger, J., Jacob, R. J. K., Sassaroli, A., & Fantini, S. (2009) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/1518701.1519035 · Cited by 106 · see citing works
Stansfield, S., Shawver, D., Sobel, A. L., Prasad, M., & Tapia, L. (2000) · PRESENCE Virtual and Augmented Reality · doi:10.1162/105474600300040376 · Cited by 100 · see citing works
Exploration of an EEG-Based Cognitively Adaptive Training System in Virtual Reality
Dey, A., Chatburn, A., & Billinghurst, M. (2019) · IEEE Conference on Virtual Reality and 3D User Interfaces (VR) · doi:10.1109/vr.2019.8797840 · Cited by 97 · see citing works
Self-citationTesting the relationship between mimicry, trust and rapport in virtual reality conversations
Hale, J., & Hamilton, A. F. D. C. (2016) · Scientific Reports · doi:10.1038/srep35295 · Cited by 84 · see citing works
Hu, W.-L., Akash, K., Jain, N., & Reid, T. (2016) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2016.12.188 · Cited by 84 · see citing works
Framework for Electroencephalography-based Evaluation of User Experience
Frey, J., Daniel, M., Castet, J., Hachet, M., & Lotte, F. (2016) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/2858036.2858525 · Cited by 82 · see citing works
Effects of Cognitive Load on Trusting Behavior – An Experiment Using the Trust Game
Samson, K., & Kostyszyn, P. (2015) · PLoS ONE · doi:10.1371/journal.pone.0127680 · Cited by 81 · see citing works
Using Galvanic Skin Response (GSR) to Measure Trust and Cognitive Load in the Text-Chat Environment
Khawaji, A., Zhou, J. J., Chen, F., & Marcus, N. (2015) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/2702613.2732766 · Cited by 63 · see citing works
Dynamic modeling of trust in human-machine interactions
Akash, K., Hu, W.-L., Reid, T., & Jain, N. (2017) · Proceedings of the ... American Control Conference/Proceedings of the American Control Conference · doi:10.23919/acc.2017.7963172 · Cited by 56 · see citing works
Lee, J., Yeo, H.-S., Dhuliawala, M., Akano, J., Shimizu, J., Starner, T. E., Quigley, A. J., Woo, W., & Kunze, K. S. (2017) · ACM International Symposium on Wearable Computers (ISWC) · doi:10.1145/3123021.3123060 · Cited by 38 · see citing works
Combining EEG with Pupillometry to Improve Cognitive Workload Detection
Rozado, D., & Dünser, A. (2015) · Computer · doi:10.1109/mc.2015.314 · Cited by 35 · see citing works
Using stroop task to assess cognitive load
Jacek Gwizdka, A. (2010) · European Conference on Cognitive Ergonomics · doi:10.1145/1962300.1962345 · Cited by 28 · see citing works
The Relationship Between Presence and Trust in Virtual Reality
Salanitri, D., Lawson, G., & Waterfield, B. C. (2016) · European Conference on Cognitive Ergonomics · doi:10.1145/2970930.2970947 · Cited by 28 · see citing works
McDonnell, R., & Breidt, M. (2010) · doi:10.1145/1899950.1899991 · Cited by 25 · see citing works
Gerry, L. J., Ens, B. M., Drogemuller, A., Thomas, B. H., & Billinghurst, M. (2018) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3170427.3188479 · Cited by 21 · see citing works
Self-citationEmotion Recognition Using Physiological Signals
Szwoch, W. (2015) · doi:10.1145/2814464.2814479 · Cited by 16 · see citing works
Detecting Human Thermal Discomfort via Physiological Signals
Abouelenien, M., Burzo, M. G., Mihalcea, R. F., Rusinek, K., & Alstine, D. V. (2017) · International Conference on PErvasive Technologies Related to Assistive Environments (PETRA) · doi:10.1145/3056540.3064957 · Cited by 8 · see citing works
Mental State Analysis on Eyewear
Augereau, O., Tag, B., & Kise, K. (2018) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/3267305.3274119 · Cited by 8 · see citing works
Physiological Signal-Driven Virtual Reality in Social Spaces
Pai, Y. S. (2016) · doi:10.1145/2984751.2984787 · Cited by 5 · see citing works
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Trust in AI: progress, challenges, and future directions
Afroogh, S., Akbari, A., Malone, E., Kargar, M., & Alambeigi, H. (2024) · Humanities and Social Sciences Communications · doi:10.1057/s41599-024-04044-8 · Cited by 618 · see citing works
How to Evaluate Trust in AI-Assisted Decision Making? A Survey of Empirical Methodologies
Vereschak, O., Bailly, G., & Caramiaux, B. (2021) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3476068 · Cited by 229 · see citing works
Halbig, A., & Latoschik, M. E. (2021) · Frontiers in Virtual Reality · doi:10.3389/frvir.2021.694567 · Cited by 166 · see citing works
Evaluating privacy, security, and trust perceptions in conversational AI: A systematic review
Leschanowsky, A., Rech, S., Popp, B., & Bäckström, T. (2024) · Computers in Human Behavior · doi:10.1016/j.chb.2024.108344 · Cited by 131 · see citing works
Measuring Human Trust in a Virtual Assistant using Physiological Sensing in Virtual Reality
Gupta, K., Hajika, R., Pai, Y. S., Duenser, A., Lochner, M., & Billinghurst, M. (2020) · IEEE Conference on Virtual Reality and 3D User Interfaces (VR) · doi:10.1109/vr46266.2020.00099 · Cited by 92 · see citing works
Self-citationWhen XR and AI Meet - A Scoping Review on Extended Reality and Artificial Intelligence
Hirzle, T., Müller, F., Draxler, F., Schmitz, M., Knierim, P., & Hornbæk, K. (2023) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3581072 · Cited by 101 · see citing works
Understanding User Reliance on AI in Assisted Decision-Making
Cao, S., & Huang, C.-M. (2022) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3555572 · Cited by 71 · see citing works
Neural Applications Using Immersive Virtual Reality: A Review on EEG Studies
Choi, J. W., Kwon, H., Choi, J.-H., Kaongoen, N., Hwang, C., Kim, M., Kim, B. H., & Jo, S. (2023) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2023.3254551 · Cited by 68 · see citing works
Badia, S. B. I., Silva, P. A., Branco, D., Pinto, A., de Carvalho, C. M. S., Menezes, P. J., Almeida, J., & Pilacinski, A. (2022) · Electronics · doi:10.3390/electronics11111726 · Cited by 50 · see citing works
A comparative study on inter-brain synchrony in real and virtual environments using hyperscanning
Gumilar, I., Sareen, E., Bell, R. E., Stone, A., Hayati, A. F., Mao, J., Barde, A., Gupta, A., Dey, A., Lee, G., et al. (2020) · Computers & Graphics · doi:10.1016/j.cag.2020.10.003 · Cited by 49 · see citing works
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Bernal, G., Hidalgo, N., Russomanno, C., & Maes, P. (2022) · IEEE Conference on Virtual Reality and 3D User Interfaces (VR) · doi:10.1109/vr51125.2022.00024 · Cited by 46 · see citing works
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Zhu, G., Zong, F., Zhang, H., Wei, B., & Liu, F. (2021) · IEEE Access · doi:10.1109/access.2021.3058271 · Cited by 35 · see citing works
Chang, Z., Bai, H., Zhang, L., Gupta, K., He, W., & Billinghurst, M. (2022) · Frontiers in Virtual Reality · doi:10.3389/frvir.2022.995090 · Cited by 35 · see citing works
Self-citationYi, B., Cao, H., Song, X., Wang, J., Guo, W., & Huang, Z. (2023) · Transportation Research Record Journal of the Transportation Research Board · doi:10.1177/03611981231156576 · Cited by 30 · see citing works
Trusting emotional support from generative artificial intelligence: a conceptual review
Volpato, R., DeBruine, L. M., & Stumpf, S. (2025) · Computers in Human Behavior Artificial Humans · doi:10.1016/j.chbah.2025.100195 · Cited by 46 · see citing works
AffectivelyVR: Towards VR Personalized Emotion Recognition
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Self-citationEloy, L., Doherty, E. J., Spencer, C. A., Bobko, P., & Hirshfield, L. (2022) · Frontiers in Neuroergonomics · doi:10.3389/fnrgo.2022.838625 · Cited by 26 · see citing works
Li, H., Liang, M., Niu, K., & Zhang, Y. (2024) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2024.2327188 · Cited by 24 · see citing works
Impact of work instruction difficulty on cognitive load and operational efficiency
Eesee, A. K., Varga, V., Eigner, G., & Ruppert, T. (2025) · Scientific Reports · doi:10.1038/s41598-025-95942-7 · Cited by 22 · see citing works
Colley, M., Speidel, O., Strohbeck, J., Rixen, J. O., Belz, J. H., & Rukzio, E. (2023) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3631408 · Cited by 20 · see citing works
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Bitkina, O. V., Kim, J., Park, J., Park, J., & Kim, H. K. (2021) · IEEE Access · doi:10.1109/access.2021.3117120 · Cited by 17 · see citing works
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Kim, W. B., Jeon, E., Kim, G., Yeo, D., & Kim, S. (2022) · Applied Sciences · doi:10.3390/app12031438 · Cited by 17 · see citing works
Eloy, L., Spencer, C. A., Doherty, E. J., & Hirshfield, L. (2023) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3579598 · Cited by 17 · see citing works
Colley, M., Meinhardt, L.-M., Faßbender, A., Rietzler, M., & Rukzio, E. (2023) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3596249 · Cited by 17 · see citing works
Jing, A., Frederick, M., Sewell, M., Karlson, A. K., Simpson, B., & Smith, M. (2023) · Proceedings - International Symposium on Mixed and Augmented Reality, ISMAR · doi:10.1109/ismar59233.2023.00100 · Cited by 15 · see citing works
Chen, H., Chan, I. Y. S., Dong, Z., & Samuel, T.-A. (2025) · Advanced Engineering Informatics · doi:10.1016/j.aei.2025.103555 · Cited by 14 · see citing works
Huang, Y.-T., Hsu, C.-C., & Wang, T.-H. (2025) · Frontiers in Virtual Reality · doi:10.3389/frvir.2025.1540406 · Cited by 12 · see citing works
Trust in Robot–Robot Scaffolding
Kırtay, M., Hafner, V. V., Asada, M., & Öztop, E. (2023) · IEEE Transactions on Cognitive and Developmental Systems · doi:10.1109/tcds.2023.3235974 · Cited by 11 · see citing works
Gupta, K., Chan, S., Pai, Y. S., Sumich, A. L., Nanayakkara, S. C., & Billinghurst, M. (2021) · International Conference on Human-Computer Interaction with Mobile Devices and Services (MobileHCI) · doi:10.1145/3447527.3474864 · Cited by 10 · see citing works
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Skierś, K., Pai, Y. S., & Minamizawa, K. (2022) · SIGGRAPH Asia XR · doi:10.1145/3550472.3558417 · Cited by 8 · see citing works
Self-citationLi, H., Gai, J., Bu, L., & Gao, C. (2025) · Universal Access in the Information Society · doi:10.1007/s10209-025-01245-z · Cited by 7 · see citing works
Deep Learning Architectures Used In Eeg-Based Estimation Of Cognitive workload: A Review
Zenia, N. Z., & Hu, Y. (2021) · IEEE International Conference on Autonomous Systems (ICAS) · doi:10.1109/icas49788.2021.9551143 · Cited by 7 · see citing works
Moin, S., Belani, M., Singh, P., Phutela, N., & Singh, P. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713807 · Cited by 6 · see citing works
EEG, Pupil Dilations, and Other Physiological Measures of Working Memory Load in the Sternberg Task
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Self-citationThe uncertain advisor: trust, accuracy, and self-correction in an automated decision support system
Lochner, M., & Smilek, D. (2022) · Cognitive Processing · doi:10.1007/s10339-022-01113-1 · Cited by 5 · see citing works
Self-citationEffects of 6DoF Motion on Cybersickness in Interactive Virtual Reality
Chatterjee, J., Spruyt, L., Pirson, N., & Vega, M. T. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-71713-0_2 · Cited by 4 · see citing works
Using Context and Physiological Cues to Improve Emotion Recognition in Virtual Reality
Gupta, K. (2021) · IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct) · doi:10.1109/ismar-adjunct54149.2021.00105 · Cited by 4 · see citing works
Self-citation[DC] Exploration of Context and Physiological Cues for Personalized Emotion-Adaptive Virtual Reality
Gupta, K. (2022) · IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW) · doi:10.1109/vrw55335.2022.00331 · Cited by 4 · see citing works
Self-citationA Human–Computer-Interaction Exploration of Working Memory Using EEG
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Transcendental Avatar: Experiencing Bioresponsive Avatar of the Self for Improved Cognition
Skierś, K., Pai, Y. S., & Minamizawa, K. (2022) · SIGGRAPH Asia Posters · doi:10.1145/3550082.3564210 · Cited by 3 · see citing works
Self-citationDarejeh, A., Marcus, N., Mohammadi, G., & Sweller, J. (2026) · Human Factors The Journal of the Human Factors and Ergonomics Society · doi:10.1177/00187208261427867 · Cited by 2 · see citing works
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Darmasti, A. H. (2023) · IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct) · doi:10.1109/ismar-adjunct60411.2023.00172
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Facino, M., VLADESCU, A., Lutteroth, C., Hunter, A. J., Roberts, A. P. J., & Dugelay, S. (2024) · International Conference on Underwater Acoustics (ICUA) · doi:10.25144/22247
Kang, S., Lee, Y., Lee, G., Yang, H.-J., Kim, S.-H., Shin, J.-E., & Kim, S. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3790674
Zhang, Z., Yi, X., Dai, Z., Yu, X., Zhang, S., Liu, B., Cao, J., & Zhao, H. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3791926
Darmasti, A. H., Zender, R., Sianipar, A., & Niels Pinkwart, C. (2026) · Frontiers in Virtual Reality · doi:10.3389/frvir.2026.1870193
Trust and interaction design in AI-enabled systems: a systematic literature review
Abramson, S. L., Sora-Cardenas, J., Nandakumar, P., Bruce, J. E., Gopinath, N., Zangari, L. G., & Feijóo-García, P. G. (2026) · Frontiers in Psychology · doi:10.3389/fpsyg.2026.1932655
- Authors
- Gupta, K., Hajika, R., Pai, Y. S., Duenser, A., Lochner, M., & Billinghurst, M.
- Affiliations
- University of Auckland (NZ)
- Commonwealth Scientific and Industrial Research Organisation (AU)
- Venue
- 25th ACM Symposium on Virtual Reality Software and Technology
- Publisher
- ACM
- Published
- 2019-11-12
- Pages
- 1-10
- OpenAlex
- W2983152572
- Cited by
- 59
- References
- 40
- Citations by year
- 2026: 6 · 2025: 9 · 2024: 10 · 2023: 12 · 2022: 11 · 2021: 8 · 2020: 3
- FWCI
- 4.45
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- ACM Symposium on Virtual Reality Software and Technology (VRST) (publishedVersion)
- Adelaide Research & Scholarship (AR&S) (University of Adelaide) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Human-Computer Interaction › Virtual Reality Applications and Impacts
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Social Sciences › Psychology › Social Psychology › Human-Automation Interaction and Safety
- Keywords
- cognitive load, physiological signals, virtual reality, EEG, skin conductance, heart rate variability, trustworthiness, human-computer interaction
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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