Augmenting Traditional Performance Analyses with Eye Tracking Metrics

  • Ciara SibleyEmail author
  • Cyrus Foroughi
  • Noelle Brown
  • Sabrina Drollinger
  • Henry Phillips
  • Joseph Coyne
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1201)


One hundred and nineteen military aviators were tasked with developing route plans for directing multiple Unmanned Aerial Vehicles to search for targets with different values and probabilities of being found. Target icon visualizations were manipulated on the planning display’s map, such that half the trials included either enhanced or simple icons. Plan quality performance was not impacted by icon type, but participants developed plans 11% faster with enhanced icons. Additionally, neither maximum pupil dilation nor mean pupil dilation, measured during planning, were statistically different between icon types, signifying similar levels of mental effort when interacting with either icon. Fixation analyses revealed that enhanced icons increased the proportion of time spent looking at the map and decreased gaze transition entropy, thus indicating more deterministic scan patterns. These findings demonstrate how augmenting performance analyses with eye tracking metrics provides a more complete understanding of how visualizations affect a user’s interface experience.


Eye tracking Pupillometry Gaze analysis Transition entropy Human-Computer interaction Human factors Supervisory control 



The Office of Naval Research funded this work. The authors would like to thank Dr. Jeffrey Morrison, Program Officer for the Command Decision Making (CDM) program, for his continued support.


  1. 1.
    Coyne, J., Sibley, C., Monfort, S.: Assessing situation awareness in an unmanned vehicle control task: a case for eye tracking based metrics. In: Advances in Aviation Psychology, vol. 2, pp. 217–236. Routledge, Abingdon (2017)Google Scholar
  2. 2.
    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 (1988)Google Scholar
  3. 3.
    Endsley, M.R.: Situation awareness global assessment technique (SAGAT). In: Proceedings of the National Aerospace and Electronics Conference, pp. 789–795. Institute of Electrical and Electronics Engineers, New York (1988)Google Scholar
  4. 4.
    Crandall, B., Klein, G., Klein, G.A., Hoffman, R.R.: Working Minds: A Practitioner’s Guide to Cognitive Task Analysis. Mit Press, Cambridge (2006)CrossRefGoogle Scholar
  5. 5.
    Kahneman, D., Beatty, J.: Pupil diameter and load on memory. Science 154(3756), 1583–1585 (1966)CrossRefGoogle Scholar
  6. 6.
    Hess, E.H., Polt, J.M.: Pupil size in relation to mental activity during simple problem-solving. Science 143(3611), 1190–1192 (1964)CrossRefGoogle Scholar
  7. 7.
    Beatty, J., Lucero-Wagoner, B.: The pupillary system. In: Cacioppo, J.T., Tassinary, L.G., Berntson, G.G. (eds.) Handbook of Psychophysiology, 2nd edn, pp. 142–162. Cambridge University Press, Cambridge (2000)Google Scholar
  8. 8.
    Pincus, S.: Approximate entropy (ApEn) as a complexity measure. Chaos Interdisc. J. Nonlinear Sci. 5(1), 110–117 (1995)MathSciNetCrossRefGoogle Scholar
  9. 9.
    McKinley, R.A., McIntire, L.K., Schmidt, R., Repperger, D.W., Caldwell, J.A.: Evaluation of eye metrics as a detector of fatigue. Hum. Factors 53(4), 403–414 (2011)CrossRefGoogle Scholar
  10. 10.
    Brown, N., Coyne, J., Sibley, C., Foroughi, C.: Human performance in the simulated multiple asset routing testbed (SMART): an individual differences approach. In: Boring, R. (ed.) Advances in Human Error, Reliability, Resilience, and Performance, AHFE 2019, Advances in Intelligent Systems and Computing, vol 956. Springer, Cham (2020)Google Scholar
  11. 11.
    Coyne, J.T., Foroughi, C.K., Brown, N.L., Sibley, C.M.: Evaluating decision making in a multi-objective route planning task. In: Proceedings of the Human Factors and Ergonomic Society Annual Meeting, vol. 62, pp. 217—221 (2018)Google Scholar
  12. 12.
    Coyne, J., Moclaire, C., Brown, N., Foroughi, C., Sibley, C.: Normative interpupillary distance data reduces pupil size noise in a remote eye tracking system. In: Proceedings of the 20th International Symposium on Aviation Psychology, Dayton, OH (2019)Google Scholar
  13. 13.
    Holmqvist, K., Nyström, M., Andersson, R., Dewhurst, R., Jarodzka, H., Van de Weijer, J.: Eye Tracking: A Comprehensive Guide to Methods and Measures. OUP Oxford, Oxford (2011)Google Scholar
  14. 14.
    Borchers, H.: pracma: practical numerical math functions. R Package Vers. 1(3) (2015)Google Scholar
  15. 15.
    Spedicato, G.A., Kang, T.S., Yalamanchi, S.B., Yadav, D., Cordón, I.: The markovchain Package: a Package for Easily Handling Discrete Markov Chains in R (2016)Google Scholar

Copyright information

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021

Authors and Affiliations

  • Ciara Sibley
    • 1
    Email author
  • Cyrus Foroughi
    • 1
  • Noelle Brown
    • 1
  • Sabrina Drollinger
    • 2
  • Henry Phillips
    • 1
  • Joseph Coyne
    • 1
  1. 1.U.S. Naval Research LaboratoryWashingtonUSA
  2. 2.U.S. Naval Aerospace Medical InstitutePensacolaUSA

Personalised recommendations