Gazepoint Citations

We are frequently asked if we can provide examples of research papers in which the Gazepoint eye-tracking technologies are used. We are very happy to provide a shortlist of publications that we have found to date. If you are interested in using our best eye-tracking software for marketers in your research and don’t have the software yet, shop now or contact us to get started!

If you have published your research from your neuromarketing study that uses the Gazepoint system, please let us know and we will add a link to your work here! Our suggested reference to cite Gazepoint in your research is: Gazepoint (2021). GP3 Eye-Tracker. Retrieved from https://www.gazept.com

Sims, J. P., Haynes, A., & Lanius, C. (2023). Exploring the utility of eye tracking for sociological research on race. The British Journal of Sociology, n/a(n/a). https://doi.org/10.1111/1468-4446.13054
Aslan, M., Baykara, M., & Alakuş, T. B. (2023). LSTMNCP: lie detection from EEG signals with novel hybrid deep learning method. Multimedia Tools and Applications. https://doi.org/10.1007/s11042-023-16847-z
Yao, J., Su, S., & Liu, S. (2023). The effect of key audit matters reviewing on loan approval decisions? Finance Research Letters, 104467. https://doi.org/10.1016/j.frl.2023.104467
Hatzithomas, L., Theodorakioglou, F., Margariti, K., & Boutsouki, C. (2023). Cross-media advertising strategies and brand attitude: the role of cognitive load. International Journal of Advertising, 0(0), 1–33. https://doi.org/10.1080/02650487.2023.2249342
Prahm, C., Konieczny, J., Bressler, M., Heinzel, J., Daigeler, A., Kolbenschlag, J., & Lauer, H. (2023). Influence of colored face masks on judgments of facial attractiveness and gaze patterns. Acta Psychologica, 239, 103994. https://doi.org/10.1016/j.actpsy.2023.103994
Jaśkowiec, M., & Kowalska-Chrzanowska, M. (2023). The Use of Games in Citizen Science Based on Findings from the EyeWire User Study. Games and Culture, 15554120231196260. https://doi.org/10.1177/15554120231196260
Huang, J., Raja, J., Cantor, C., Marx, W., Galgano, S., Zarzour, J., Caridi, T., Gunn, A., Morgan, D., & Smith, A. (2023). Eye Motion Tracking for Medical Image Interpretation Training. Current Problems in Diagnostic Radiology. https://doi.org/10.1067/j.cpradiol.2023.08.013
Abeysinghe, Y., Mahanama, B., Jayawardena, G., Sunkara, M., Ashok, V., & Jayarathna, S. (2023). Gaze Analytics Dashboard for Distributed Eye Tracking. 2023 IEEE 24th International Conference on Information Reuse and Integration for Data Science (IRI), 140–145. https://doi.org/10.1109/IRI58017.2023.00031
(PDF) Eye Tracking as a Research and Training Tool for Ensuring Quality Education. (2023, July 23). ResearchGate. https://doi.org/10.1007/978-3-031-30498-9_28
Han, E. (2023). Comparing the Perception of In-Person and Digital Monitor Viewing of Paintings. Empirical Studies of the Arts, 41(2), 465–496. https://doi.org/10.1177/02762374231158520
Koutsogiorgi, C. C., & Michaelides, M. P. (2023). Response Tendencies to Positively and Negatively Worded Items of the      Rosenberg Self-Esteem Scale With Eye-Tracking Methodology. European Journal of Psychological Assessment, 39(4), 307–315. https://doi.org/10.1027/1015-5759/a000772
Pillai, P., Balasingam, B., & Biondi, F. N. (2023). Model-Based Estimation of Mental Workload in Drivers Using Pupil Size Measurements. 2023 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM), 815–821. https://doi.org/10.1109/AIM46323.2023.10196230
Hahn, A., Riedelsheimer, J., Royer, Z., Frederick, J., Kee, R., Crimmins, R., Huber, B., Harris, D., & Jantzen, K. (2023). Effects of Cleft Lip on Visual Scanning and Neural Processing of Infant Faces [Preprint]. Preprints. https://doi.org/10.22541/au.168455102.24287447/v1
Lobodenko, L., Cheredniakova, A., Shesterkina, L., & Kharitonova, O. (2023). Eye-Tracking Technologies in the Analysis of Environmental Advertising and Journalistic Texts Perception by Youth. 2023 Communication Strategies in Digital Society Seminar (ComSDS), 78–85. https://doi.org/10.1109/ComSDS58064.2023.10130433
Mahanama, B., Sunkara, M., Ashok, V., & Jayarathna, S. (2023). DisETrac: Distributed Eye-Tracking for Online Collaboration. Proceedings of the 2023 Conference on Human Information Interaction and Retrieval, 427–431. https://doi.org/10.1145/3576840.3578292
Cui, Y., Liu, X., & Cheng, Y. (2023). Attention-consuming or attention-saving: an eye tracking study on punctuation in Chinese subtitling of English trailers. Multilingua. https://doi.org/10.1515/multi-2022-0138
Mani, R., Asper, L., Arunachalam, V., & Khuu, S. K. (2023). The impact of traumatic brain injury on inhibitory control processes assessed using a delayed antisaccade task. Neuroscience Letters, 797, 137081. https://doi.org/10.1016/j.neulet.2023.137081
Santos, S. M. P., Fernandes, N. L., & Pandeirada, J. N. S. (2023). Same but different: The influence of context framing on subjective disgust, eye movements and pupillary responses. Consciousness and Cognition, 108, 103462. https://doi.org/10.1016/j.concog.2022.103462
Calle, A., Ortega, P., Argudo-Vásconez, A., Cobos, M., & Alvarado, O. (2023). Exploring the Role of Visual Attention in Aggressive Behavior: Evidence from Eye-Tracking Measurements. https://doi.org/10.54941/ahfe1003024
Yaneva, V., Ha, L. A., Eraslan, S., Yesilada, Y., & Mitkov, R. (2023). Chapter 3 - Reading differences in eye-tracking data as a marker of high-functioning autism in adults and comparison to results from web-related tasks. In A. S. El-Baz & J. S. Suri (Eds.), Neural Engineering Techniques for Autism Spectrum Disorder (pp. 63–79). Academic Press. https://doi.org/10.1016/B978-0-12-824421-0.00011-4
Cui, Y., Liu, X., & Cheng, Y. (2023). A Comparative Study on the Effort of Human Translation and Post-Editing in Relation to Text Types: An Eye-Tracking and Key-Logging Experiment. SAGE Open, 13(1), 21582440231155850. https://doi.org/10.1177/21582440231155849
Katona, J. (2023). An Eye Movement Study in Unconventional Usage of Different Software Tools. Sensors, 23(8), 3823. https://doi.org/10.3390/s23083823
Arnaud, C. (2023). A Design-Based Approach to Studying Algorithmic Practices: A Case Study on the Explicit Controllability of a Recommender System. https://dial.uclouvain.be/pr/boreal/object/boreal:278314
Collins, A., Pillai, P., Balasingam, B., & Jaekel, A. (2023). Machine Learning Technique for Data Fusion and Cognitive Load Classification Using an Eye Tracker. In K. Daimi & A. Al Sadoon (Eds.), Proceedings of the 2023 International Conference on Advances in Computing Research (ACR’23) (pp. 86–95). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-33743-7_7
Pacheco-González, D., Argudo-Vasconez, A., Ortega-Chasi, P., Cobos-Cali, M., & Alvarado-Cando, O. (2023). Fixation Analysis of Affective Picture Processing in Aggressive Adolescent. Physical Ergonomics and Human Factors.
Eyes are the Windows to AI Reliance: Toward Real-Time Human-AI Reliance Assessment. (2023).
Xu, J., Guo, K., Zhang, X., & Sun, P. Z. H. (2023). Left Gaze Bias between LHT and RHT: A Recommendation Strategy to Mitigate Human Errors in Left- and Right-Hand Driving. IEEE Transactions on Intelligent Vehicles, 1–12. https://doi.org/10.1109/TIV.2023.3298481
Kim, S. K., Liersch, J., & Kirchner, E. A. (2023). Classification of Error-Related Potentials Evoked During Observation of Human Motion Sequences. In D. D. Schmorrow & C. M. Fidopiastis (Eds.), Augmented Cognition (pp. 142–152). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-35017-7_10
Rodrigo, M. M. T., & Tablatin, C. L. S. (2023). How Do Programming Students Read and Act upon Compiler Error Messages? In D. D. Schmorrow & C. M. Fidopiastis (Eds.), Augmented Cognition (pp. 153–168). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-35017-7_11
Taha, B., Seha, S. N. A., Hwang, D. Y., & Hatzinakos, D. (2023). EyeDrive: A Deep Learning Model for Continuous Driver Authentication. IEEE Journal of Selected Topics in Signal Processing, 1–11. https://doi.org/10.1109/JSTSP.2023.3235302
Mora-Salinas, R. J., Perez-Rojas, D., & De La Trinidad-Rendon, J. S. (2023). Real-Time Sensory Adaptive Learning for Engineering Students. In M. E. Auer, W. Pachatz, & T. Rüütmann (Eds.), Learning in the Age of Digital and Green Transition (pp. 820–831). Springer International Publishing. https://doi.org/10.1007/978-3-031-26876-2_78
Chmal, J., Ptasińska, M., & Skublewska-Paszkowska, M. (2022). Analysis of the ergonomics of e-commerce websites. Journal of Computer Sciences Institute, 25, 330–336. https://doi.org/10.35784/jcsi.3016
Sethi, T., & Ziat, M. (2022). Dark mode vogue: Do light-on-dark displays have measurable benefits to users? Ergonomics, 0(0), 1–15. https://doi.org/10.1080/00140139.2022.2160879
Salminen, J., Jung, S., Nielsen, L., Şengün, S., & Jansen, B. J. (2022). How does varying the number of personas affect user perceptions and behavior? Challenging the ‘small personas’ hypothesis! International Journal of Human-Computer Studies, 168, 102915. https://doi.org/10.1016/j.ijhcs.2022.102915
Cui, Z., Tang, Y.-Y., & Kim, M.-K. (2022). The Effects of Different Kinds of Smooth Pursuit Exercises on Center of Pressure and Muscle Activities during One Leg Standing. Healthcare, 10(12), 2498. https://doi.org/10.3390/healthcare10122498
Murphy, T. I., Abel, L. A., Armitage, J. A., & Douglass, A. G. (2022). Effects of tracker location on the accuracy and precision of the Gazepoint GP3 HD for spectacle wearers. Behavior Research Methods. https://doi.org/10.3758/s13428-022-02023-y
Contemori, G., Oletto, C. M., Cessa, R., Marini, E., Ronconi, L., Battaglini, L., & Bertamini, M. (2022). Investigating the role of the foveal cortex in peripheral object discrimination. Scientific Reports, 12(1), 19952. https://doi.org/10.1038/s41598-022-23720-w
Menzel, T., Teubner, T., Adam, M. T. P., & Toreini, P. (2022). Home is where your Gaze is – Evaluating effects of embedding regional cues in user interfaces. Computers in Human Behavior, 136, 107369. https://doi.org/10.1016/j.chb.2022.107369
Steffens, J., & Himmelein, H. (2022). Induced cognitive load influences unpleasantness judgments of modulated noise.
Pillai, P., Balasingam, B., & Biondi, F. (2022). USING SIGNAL-TO-NOISE RATIO TO EXPLORE THE COGNITIVE COST OF THE DETECTION RESPONSE TASK. https://doi.org/10.1177/1071181322661481
Antoine, M., Abdessalem, H. B., & Frasson, C. (2022). Cognitive Workload Assessment of Aircraft Pilots. Journal of Behavioral and Brain Science, 12(10), 474–484. https://doi.org/10.4236/jbbs.2022.1210027
Zhou, H., Doggett, E. V., Qi, K., Tang, B., Wolak, A., Nahavandi, S., & Nguyen, D. T. (2022). Image Saliency Prediction in Novel Production Scenarios. 2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 3367–3372. https://doi.org/10.1109/SMC53654.2022.9945490
Souza, A., & Freitas, D. (2022). Towards the Improvement of the Cognitive Process of the Synthesized Speech of Mathematical Expression in MathML: An Eye-Tracking. 2022 International Conference on Interactive Media, Smart Systems and Emerging Technologies (IMET), 1–8. https://doi.org/10.1109/IMET54801.2022.9929541
Zyrianov, V., Peterson, C. S., Guarnera, D. T., Behler, J., Weston, P., Sharif, B., & Maletic, J. I. (2022). Deja Vu: semantics-aware recording and replay of high-speed eye tracking and interaction data to support cognitive studies of software engineering tasks—methodology and analyses. Empirical Software Engineering, 27(7), 168. https://doi.org/10.1007/s10664-022-10209-3
Lewandowska, A., Dziśko, M., & Jankowski, J. (2022). Investigation the role of contrast on habituation and sensitisation effects in peripheral areas of graphical user interfaces. Scientific Reports, 12(1), 15281. https://doi.org/10.1038/s41598-022-16284-2
Gawade, V., Bifulco, C., & (Grace) Guo, W. (2022). Lessons Learned to Effectively Teach and Evaluate Undergraduate Engineers in Work Design and Ergonomics Laboratory from a World Before, During, and After COVID-19. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 66(1), 756–760. https://doi.org/10.1177/1071181322661505
Gallant, S. N., Kennedy, B. L., Bachman, S. L., Huang, R., Cho, C., Lee, T.-H., & Mather, M. (2022). Behavioral and fMRI evidence that arousal enhances bottom-up selectivity in young but not older adults. Neurobiology of Aging. https://doi.org/10.1016/j.neurobiolaging.2022.08.006
Veerabhadrappa, R., Hettiarachchi, I. T., Hanoun, S., Jia, D., Hosking, S. G., & Bhatti, A. (2022). Evaluating Operator Training Performance Using Recurrence Quantification Analysis of Autocorrelation Transformed Eye Gaze Data. Human Factors, 00187208221116953. https://doi.org/10.1177/00187208221116953
Robison, M. K., Coyne, J. T., Sibley, C., Brown, N. L., Neilson, B., & Foroughi, C. (2022). An examination of relations between baseline pupil measures and cognitive abilities. Psychophysiology, n/a(n/a), e14124. https://doi.org/10.1111/psyp.14124
Spitzer, L., & Mueller, S. (2022). Using a test battery to compare three remote, video-based eye-trackers. 2022 Symposium on Eye Tracking Research and Applications, 1–7. https://doi.org/10.1145/3517031.3529644