Capturing user engagement via psychophysiology: measures and mechanisms for biocybernetic adaptation
Abstract
The concept of task engagement is associated with effortful striving to reach a desired goal. This dimension is fundamental for software designed to elicit high quality performance. This paper will review the concept of task engagement, both in the psychological literature and with respect to affective computing approaches, such as biofeedback and the definition of ‘flow’ states. This paper will briefly describe a series of laboratory experiments designed to explore measures of task engagement based on EEG and cardiovascular measures. These experiments employed a number of manipulations to influence task engagement, e.g. performance feedback, task difficulty and financial incentives. Results demonstrated the sensitivity of EEG measures to cognitive sources of engagement (e.g. mental workload) whilst cardiovascular variables tended to respond to the motivation to achieve. We use these findings to explore how real-time monitoring of engagement may generate adaptive dynamics for software design using a computer game as an exemplar system.
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Psychophysiological measurements have been used to measure users' task-engagement.
“Psychophysiological measurements, ranging from skin conductance and heart rate to facial expression or fMRI, have been used to measure users' task-engagement.39”
From Understanding and Promoting Effective Engagement With Digital Behavior Change Interventions (2016) · cites this paper as [39]
Psychophysiological measures can help identify aspects of the intervention that attract attention or evoke emotional arousal, suggesting mechanisms through which DBCI content or design impact short term engagement.
“Such measures can help identify aspects of the intervention that attract attention or evoke emotional arousal, suggesting mechanisms through which DBCI content or design impact short term engagement.”
From Understanding and Promoting Effective Engagement With Digital Behavior Change Interventions (2016) · cites this paper as [39]
Cites
Flow: the psychology of optimal experience
(1990) · Choice Reviews Online · doi:10.5860/choice.28-0597 · Cited by 12,652 · see citing works
Tosi, H. L. (1991) · Academy of Management Review · doi:10.5465/amr.1991.4278976 · Cited by 4,147 · see citing works
Toward machine emotional intelligence: analysis of affective physiological state
Picard, R. W., Vyzas, E., & Healey, J. (2001) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.954607 · Cited by 2,334 · see citing works
Task-evoked pupillary responses, processing load, and the structure of processing resources.
Beatty, J. (1982) · Psychological Bulletin · doi:10.1037/0033-2909.91.2.276 · Cited by 1,959 · see citing works
Frontal EEG asymmetry as a moderator and mediator of emotion
Coan, J. A., & Allen, J. J. B. (2004) · Biological Psychology · doi:10.1016/j.biopsycho.2004.03.002 · Cited by 1,426 · see citing works
Flow in games (and everything else)
Chen, J. (2007) · Communications of the ACM · doi:10.1145/1232743.1232769 · Cited by 1,021 · see citing works
What does the prefrontal cortex “do” in affect: perspectives on frontal EEG asymmetry research
Davidson, R. J. (2004) · Biological Psychology · doi:10.1016/j.biopsycho.2004.03.008 · Cited by 995 · see citing works
Toward an affect-sensitive multimodal human-computer interaction
Pantic, M., & Rothkrantz, L. J. M. (2003) · Proceedings of the IEEE · doi:10.1109/jproc.2003.817122 · Cited by 791 · see citing works
Larsen, J. T., Norris, C. J., & Cacioppo, J. T. (2003) · Psychophysiology · doi:10.1111/1469-8986.00078 · Cited by 766 · see citing works
Biocybernetic system evaluates indices of operator engagement in automated task
Pope, A., Bogart, E. H., & Bartolome, D. S. (1995) · Biological Psychology · doi:10.1016/0301-0511(95)05116-3 · Cited by 670 · see citing works
Issues and assumptions on the road from raw signals to metrics of frontal EEG asymmetry in emotion
Allen, J. J. B., Coan, J. A., & Nazarian, M. (2004) · Biological Psychology · doi:10.1016/j.biopsycho.2004.03.007 · Cited by 641 · see citing works
Fundamentals of physiological computing
Fairclough, S. H. (2009) · Interacting with Computers · doi:10.1016/j.intcom.2008.10.011 · Cited by 608 · see citing works
Self-citationMatthews, G., Campbell, S. E., Falconer, S., Joyner, L. A., Huggins, J., Gilliland, K., Grier, R. A., & Warm, J. S. (2002) · Emotion · doi:10.1037/1528-3542.2.4.315 · Cited by 596 · see citing works
Automatic prediction of frustration
Kapoor, A., Burleson, W., & Picard, R. W. (2007) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2007.02.003 · Cited by 592 · see citing works
This computer responds to user frustration:
Klein, J., Moon, Y., & Picard, R. W. (2002) · Interacting with Computers · doi:10.1016/s0953-5438(01)00053-4 · Cited by 560 · see citing works
Harmon‐Jones, E., & Allen, J. J. B. (1997) · Journal of Abnormal Psychology · doi:10.1037//0021-843x.106.1.159 · Cited by 544 · see citing works
Monitoring Working Memory Load during Computer-Based Tasks with EEG Pattern Recognition Methods
Gevins, A., Smith, M. E., Leong, H., McEvoy, L., Whitfield, S., Du, R., & Rush, G. (1998) · Human Factors: The Journal of the Human Factors and Ergonomics Society · doi:10.1518/001872098779480578 · Cited by 535 · see citing works
Frontal EEG asymmetry and the behavioral activation and inhibition systems
Coan, J. A., & Allen, J. J. B. (2003) · Psychophysiology · doi:10.1111/1469-8986.00011 · Cited by 528 · see citing works
Brain–computer interface systems: progress and prospects
Allison, B. Z., Wolpaw, E. W., & Wolpaw, J. R. (2007) · Expert Review of Medical Devices · doi:10.1586/17434440.4.4.463 · Cited by 424 · see citing works
Neurophysiological measures of cognitive workload during human-computer interaction
Gevins, A. S., & Smith, M. E. (2003) · Theoretical Issues in Ergonomics Science · doi:10.1080/14639220210159717 · Cited by 416 · see citing works
Flow and immersion in first-person shooters
Nacke, L. E., & Lindley, C. A. (2008) · doi:10.1145/1496984.1496998 · Cited by 328 · see citing works
Frontal Brain Asymmetry and Reward Responsiveness
Pizzagalli, D. A., Sherwood, R., Henriques, J. B., & Davidson, R. J. (2005) · Psychological Science · doi:10.1111/j.1467-9280.2005.01618.x · Cited by 326 · see citing works
Wright, R. A., & Kirby, L. D. (2001) · Advances in Experimental Social Psychology · doi:10.1016/s0065-2601(01)80007-1 · Cited by 313 · see citing works
Task difficulty effects on cardiac activity
Richter, M. M., Friedrich, A., & Gendolla, G. H. E. (2008) · Psychophysiology · doi:10.1111/j.1469-8986.2008.00688.x · Cited by 286 · see citing works
Evaluation of an adaptive automation system using three EEG indices with a visual tracking task
Freeman, F. G., Mikulka, P. J., Prinzel, L. J., & Scerbo, M. W. (1999) · Biological Psychology · doi:10.1016/s0301-0511(99)00002-2 · Cited by 283 · see citing works
Wilson, G. F., & Russell, C. A. (2007) · Human Factors The Journal of the Human Factors and Ergonomics Society · doi:10.1518/001872007x249875 · Cited by 270 · see citing works
Affective Videogames and Modes of Affective Gaming: Assist Me, Challenge Me, Emote Me
Gilleade, K. M., Dix, A. J., & Allanson, J. (2005) · doi:10.26503/dl.v2005i1.229 · Cited by 260 · see citing works
Self-citationBoredom, engagement and anxiety as indicators for adaptation to difficulty in games
Chanel, G., Rebetez, C., Bétrancourt, M., & Pun, T. (2008) · International Academic Mindtrek Conference (Mindtrek) · doi:10.1145/1457199.1457203 · Cited by 233 · see citing works
The Concept and Measurement of Mental Effort
Mulder, G. (1986) · Springer eBooks · doi:10.1007/978-94-009-4448-0_12 · Cited by 218 · see citing works
Using frustration in the design of adaptive videogames
Gilleade, K. M., & Dix, A. J. (2004) · International Conference on Advances in Computer Entertainment Technology (ACE) · doi:10.1145/1067343.1067372 · Cited by 208 · see citing works
Self-citationWright, R. A. (2008) · Social and Personality Psychology Compass · doi:10.1111/j.1751-9004.2008.00093.x · Cited by 182 · see citing works
A research agenda for physiological computing
Allanson, J., & Fairclough, S. H. (2004) · Interacting with Computers · doi:10.1016/j.intcom.2004.08.001 · Cited by 144 · see citing works
Self-citationAffective Agents: Sustaining Motivation to Learn Through Failure and a State of Stuck
Burleson, W., & Picard, R. W. (2004) · Cited by 109 · see citing works
A brain-based system for adaptive automation
Scerbo, M. W., Freeman, F. G., & Mikulka, P. J. (2003) · Theoretical Issues in Ergonomics Science · doi:10.1080/1463922021000020891 · Cited by 100 · see citing works
Miller, A., & Tomarken, A. J. (2001) · Psychophysiology · doi:10.1111/1469-8986.3830500 · Cited by 88 · see citing works
Energetical Issues in Research on Human Information Processing
Hockey, G. R. J., COLES, M. G. H., & Gaillard, A. W. K. (1986) · Springer eBooks · doi:10.1007/978-94-009-4448-0_1 · Cited by 68 · see citing works
Incentive value, unclear task difficulty, and cardiovascular reactivity in active coping
Richter, M. M., & Gendolla, G. H. E. (2006) · International Journal of Psychophysiology · doi:10.1016/j.ijpsycho.2006.12.002 · Cited by 60 · see citing works
Electrophysiologically interactive computer systems
Allanson, J. (2002) · Computer · doi:10.1109/2.989931 · Cited by 37 · see citing works
Towards Emotionally Adapted Games based on User Controlled Emotion Knobs
Saari, T., Ravaja, N., Laarni, J., & Turpeinen, M. (2005) · doi:10.26503/dl.v2005i1.167 · Cited by 12 · see citing works
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Yardley, L., Spring, B. J., Riper, H., Morrison, L. G., Crane, D. H., Curtis, K., Merchant, G. C., Naughton, F., & Blandford, A. (2016) · American Journal of Preventive Medicine · doi:10.1016/j.amepre.2016.06.015 · Cited by 1,118 · see citing works
A review of the use of psychophysiological methods in game research
Kivikangas, J. M., Chanel, G., Cowley, B. U., Ekman, I., Salminen, M., Järvelä, S., & Ravaja, N. (2011) · Journal of Gaming & Virtual Worlds · doi:10.1386/jgvw.3.3.181_1 · Cited by 286 · see citing works
Doherty, K., & Doherty, G. J. (2018) · ACM Computing Surveys · doi:10.1145/3234149 · Cited by 272 · see citing works
A Neuroergonomics Approach to Mental Workload, Engagement and Human Performance
Dehais, F., Lafont, A., Roy, R. N., & Fairclough, S. H. (2020) · Frontiers in Neuroscience · doi:10.3389/fnins.2020.00268 · Cited by 268 · see citing works
Self-citationBaldwin, C. L., & Peñaranda, B. N. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.07.047 · Cited by 159 · see citing works
Dynamic difficulty using brain metrics of workload
Afergan, D., Peck, E. M., Solovey, E. T., Jenkins, A., Hincks, S. W., Brown, E. T., Chang, R., & Jacob, R. J. K. (2014) · Proceedings of the SIGCHI Conference on Human Factors in Computing Systems · doi:10.1145/2556288.2557230 · Cited by 148 · see citing works
Brouwer, A.-M., Zander, T. O., van Erp, J. B. F., Korteling, J. E., & Bronkhorst, A. W. (2015) · Frontiers in Neuroscience · doi:10.3389/fnins.2015.00136 · Cited by 143 · see citing works
Ewing, K., Fairclough, S. H., & Gilleade, K. M. (2016) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2016.00223 · Cited by 111 · see citing works
Self-citationMenezes, M. L. R., Samara, A., Galway, L., Sant’Anna, A., Verikas, A., Alonso‐Fernandez, F., Wang, H., & Bond, R. (2017) · Personal and Ubiquitous Computing · doi:10.1007/s00779-017-1072-7 · Cited by 104 · see citing works
Use of auditory event-related potentials to measure immersion during a computer game
Burns, C. G., & Fairclough, S. H. (2014) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2014.09.002 · Cited by 85 · see citing works
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Bian, D., Wade, J. W., Swanson, A. R., Weitlauf, A. S., Warren, Z. E., & Sarkar, N. (2019) · ACM Transactions on Accessible Computing · doi:10.1145/3301498 · Cited by 55 · see citing works
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Ghergulescu, I., & Muntean, C. H. (2014) · Interacting with Computers · doi:10.1093/iwc/iwu013 · Cited by 53 · see citing works
Prediction of Cognitive States during Flight Simulation using Multimodal Psychophysiological Sensing
Harrivel, A. R., Stephens, C. L., Milletich, R. J., Heinich, C. M., Last, M. C., Napoli, N. J., Abraham, N., Prinzel, L. J., Motter, M. A., & Pope, A. (2017) · AIAA Information Systems-AIAA Infotech @ Aerospace · doi:10.2514/6.2017-1135 · Cited by 53 · see citing works
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Samara, A., Menezes, M. L. R., & Galway, L. (2016) · International Conference on Ubiquitous Computing and Communications (IUCC) · doi:10.1109/iucc-css.2016.027 · Cited by 37 · see citing works
Fairclough, S. H., Stamp, K., Dobbins, C., & Poole, H. M. (2020) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2020.102427 · Cited by 33 · see citing works
Self-citationGhergulescu, I., & Muntean, C. H. (2016) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-016-0111-2 · Cited by 32 · see citing works
Giamos, D., Doucet, O., & Léger, P. (2023) · Journal of Organizational Behavior Management · doi:10.1080/01608061.2023.2238029 · Cited by 46 · see citing works
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Pope, A., Stephens, C. L., & Gilleade, K. M. (2014) · Human-computer interaction series · doi:10.1007/978-1-4471-6392-3_5 · Cited by 27 · see citing works
Self-citationLi, M.-N., Wang, Y.-C., Wu, Y.-M., & Liu, H. (2021) · Enterprise Information Systems · doi:10.1080/17517575.2021.1941272 · Cited by 21 · see citing works
Ayaz, H., & Dehais, F. (2021) · Wiley eBooks · doi:10.1002/9781119636113.ch31 · Cited by 25 · see citing works
Psychophysiological Sensing and State Classification for Attention Management in Commercial Aviation
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Gold, J., & Ciorciari, J. (2021) · Behavioural Brain Research · doi:10.1016/j.bbr.2021.113244 · Cited by 19 · see citing works
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Maia, C. L. B., & Furtado, E. S. (2016) · Brazilian Symposium on Human Factors in Computing Systems (IHC) · doi:10.1145/3033701.3033708 · Cited by 19 · see citing works
An Approach to Analyze User’s Emotion in HCI Experiments Using Psychophysiological Measures
Maia, C. L. B., & Furtado, E. S. (2019) · IEEE Access · doi:10.1109/access.2019.2904977 · Cited by 18 · see citing works
Stephens, C. L., Dehais, F., Roy, R. N., Harrivel, A. R., Last, M. C., Kennedy, K. D., & Pope, A. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-91470-1_9 · Cited by 17 · see citing works
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Galway, L., McCullagh, P. J., Lightbody, G., Brennan, C., & Trainor, D. (2015) · doi:10.1109/cit/iucc/dasc/picom.2015.234 · Cited by 13 · see citing works
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Bae, S., Lee, Y., & Chang, P. (2020) · Brain and Behavior · doi:10.1002/brb3.1788 · Cited by 8 · see citing works
Napoli, N. J., Adams, S., Harrivel, A. R., Stephens, C. L., Kennedy, K. D., Paliwal, M., & Scherer, W. T. (2020) · AIAA SciTech Forum · doi:10.2514/6.2020-1193 · Cited by 8 · see citing works
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Using brain-computer interfaces for implicit input
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Billis, A. S., Pandria, N., Mouratoglou, S., Konstantinidis, E. I., & Bamidis, P. D. (2020) · JMIR Research Protocols · doi:10.2196/17714 · Cited by 6 · see citing works
Boasen, J., Giroux, F., Duchesneau, M.-O., Sénécal, S., Léger, P., & Ménard, J. F. (2020) · Journal of Neural Engineering · doi:10.1088/1741-2552/abaca2 · Cited by 5 · see citing works
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Knierim, M. T., Stano, F., Kurz, F., Heusch, A., & Wilson, M. L. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713512 · Cited by 6 · see citing works
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McCullagh, P. J., Lightbody, G., Galway, L., Brennan, C., & David, T. (2015) · Ambient intelligence and smart environments · doi:10.3233/978-1-61499-530-2-244 · Cited by 4 · see citing works
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Garg, N., & Sharma, K. (2023) · IntechOpen eBooks · doi:10.5772/intechopen.109740 · Cited by 4 · see citing works
Fairclough, S. H., & Burns, C. G. (2013) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/2468356.2468552 · Cited by 3 · see citing works
Self-citationMaharjan, R., Mendu, S., Mariani, M. E., Abdullah, S., & Hansen, J. P. (2024) · Digital Health · doi:10.1177/20552076241302652 · Cited by 2 · see citing works
Uma revisão sistemática sobre medição da experiência do usuário
Maia, C. L. B., & Furtado, E. S. (2014) · Cited by 2 · see citing works
Чемерисова, Е. В., Atanov, M. S., Mikheev, I., & Martynova, O. (2018) · Психология Журнал Высшей школы экономики · doi:10.17323/1813-8918-2018-2-268-278 · Cited by 2 · see citing works
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Scatigna, S., Pecini, C., Pugi, L., Adelucci, E., Cavallo, F., Scrocco, A., Bruni, A., Benigni, M., Mei, S., Lieto, M. C. D., et al. (2026) · International Journal of Social Robotics · doi:10.1007/s12369-026-01398-8 · Cited by 1 · see citing works
Campilii, E., Romano, F., Perpetuini, D., Mancini, L., Paolucci, T., Russo, E. F., Gatta, M. T., Nicola, M. D., Morone, G., Ciancarelli, I., et al. (2026) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/s12984-026-02050-2 · Cited by 1 · see citing works
Collaborative Communications Between A Human And A Resilient Safety Support System
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Hosseini, S. A. (2023) · IntechOpen eBooks · doi:10.5772/intechopen.104074 · Cited by 1 · see citing works
Simulation in the assessment and treatment of violent offenders
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Konstantinidis, E. I., Pandria, N., Billis, A. S., Mouratoglou, S., & Bamidis, P. D. (2020) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-49932-7_42
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Stamp, K., Dobbins, C., & Fairclough, S. H. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-60128-7_55
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The influence of a neuroadaptive game as a distraction from pain: a fNIRS study
Stamp, K., Fairclough, S. H., Dobbins, C., & Poole, H. M. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-821413-8.00011-7
Self-citationCAST SE211 Training for Attention Management Final Report
Harrivel, A. R., Comstock, J. R., Prinzel, L. J., Stephens, C. L., Kennedy, K. D., & Pope, A. (2021) · NASA Technical Reports Server (NASA)
Whitley, P., Clarkson, M. J., & Thompson, S. (2024) · doi:10.2196/preprints.56269
Oh, J. (2026) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2026.2628142
- Authors
- Fairclough, S. H., Gilleade, K., Ewing, K. C., & Roberts, J.
- Affiliations
- Liverpool John Moores University (GB)
- Venue
- International Journal of Autonomous and Adaptive Communications Systems
- Publisher
- Inderscience Publishers
- Published
- 2013
- Volume
- 6
- Issue
- 1
- Pages
- 63
- ISSN
- 1754-8632 (ISSN-L)
- 1754-8640
- OpenAlex
- W1999289753
- Cited by
- 60
- References
- 39
- Citations by year
- 2026: 3 · 2025: 2 · 2024: 4 · 2023: 4 · 2022: 1 · 2021: 7 · 2020: 8 · 2019: 2 · 2018: 5 · 2017: 2 · 2016: 9 · 2015: 3 · 2014: 7 · 2013: 1
- FWCI
- 7.08
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- International Journal of Autonomous and Adaptive Communications Systems (publishedVersion)
- Topics
- Social Sciences › Psychology › Developmental and Educational Psychology › Flow Experience in Various Fields
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Social Sciences › Psychology › Social Psychology › Human-Automation Interaction and Safety
- Keywords
- task engagement, psychophysiology, flow experience, biofeedback, affective computing, EEG, mental workload, cardiovascular parameters, performance feedback, task difficulty, financial incentives, video games, motivation
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