Applied Psychophysiology and Biofeedback

, Volume 41, Issue 3, pp 331–339 | Cite as

Pupil Size Changes as an Active Information Channel for Biofeedback Applications

  • Jan Ehlers
  • Christoph Strauch
  • Juliane Georgi
  • Anke Huckauf


Pupil size is usually regarded as a passive information channel that provides insight into cognitive and affective states but defies any further control. However, in a recent study (Ehlers et al. 2015) we demonstrate that sympathetic activity indexed by pupil dynamics allows strategic interference by means of simple cognitive techniques. Utilizing positive/negative imaginings, subjects were able to expand pupil diameter beyond baseline variations; albeit with varying degrees of success and only over brief periods. The current study provides a comprehensive replication on the basis of considerable changes to the experimental set-up. Results show that stricter methodological conditions (controlled baseline settings and specified user instructions) strengthen the reported effect, whereas overall performance increases by one standard deviation. Effects are thereby not restricted to pupillary level. Parallel recordings of skin conductance changes prove a general enhancement of induced autonomic arousal. Considering the stability of the results across studies, we conclude that pupil size information exceeds affective monitoring and may constitute an active input channel in human–computer interaction. Furthermore, since variations in pupil diameter reliably display self-induced changes in sympathetic arousal, the relevance of this parameter is strongly indicated for future approaches in clinical biofeedback.


Human–computer interaction Pupillometry Biofeedback Baseline 



This study was supported by the Collaborative Research Center (SFB Transregio 62) by the Deutsche Forschungsgemeinschaft (DFG).

Compliance with Ethical Standards

Conflict of interest

Authors declare that they have no conflict of interest.

Ethical Approval

All procedures performed in studies were in accordance with the ethical standards of the institutional research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed Consent

Informed consent was obtained from all individual participants included in the study.


  1. Al-Omar, D., Al-Wabil, A., & Fawzi, M. (2013). Using pupil size variation during visual emotional stimulation in measuring affective states of non communicative individuals. In C. Stephanidis (Ed.), Universal access in human-computer interaction. User and context diversity (pp. 253–258). Berlin: Springer.CrossRefGoogle Scholar
  2. Ark, W., Dryer, D., & Lu, D. (1999). The emotion mouse. In H. J. Bullinger, & J. Ziegler (Eds.), Human-computer interaction: Ergonomics and user interfaces (pp. 818–823). Lawrence Erlbaum Assoc.Google Scholar
  3. Bayer, M., Sommer, W., & Schacht, A. (2011). Emotional words impact the mind but not the body: Evidence from pupillary responses. Psychophysiology, 48, 1–9.CrossRefGoogle Scholar
  4. Beatty, J., & Kahneman, D. (1966). Pupillary changes in two memory tasks. Psychonomic Science, 5, 371–372.CrossRefGoogle Scholar
  5. Bradley, M. M., & Lang, P. J. (2000). Measuring emotion: Behavior, feeling and physiology. In R. D. Lane & L. Nadel (Eds.), Cognitive neuroscience of emotion (pp. 242–276). Oxford: Oxford University Press.Google Scholar
  6. Bremner, F. D. (2012). Pupillometric evaluation of the dynamics of the pupillary response to a brief light stimulus in healthy subjects. Investigative Ophthalmology and Visual Science, 53, 7343–7347.CrossRefPubMedGoogle Scholar
  7. Dan-Glauser, E. S., & Scherer, K. R. (2011). The Geneva affective picture database (GAPED): A new 730-picture database focusing on valence and normative significance. Behavior Research Methods, 43(2), 468–477.CrossRefPubMedGoogle Scholar
  8. Ehlers, J., Bubalo, N., Loose, M., & Huckauf, A. (2015). Towards voluntary pupil control—training affective strategies? In Proceedings of the 2nd international conference on physiological computing systems (pp. 5–12). doi: 10.5220/0005240000050012.
  9. Ehlers, J., Georgi, J., & Huckauf, A. (2014). Improving voluntary pupil size changes for HCI. In Proceedings of the 8th international conference on pervasive computing technologies for healthcare (pp. 343–346). ACM.Google Scholar
  10. Ekman, I., Poikola, A., Mäkäräinen, M., Takal, T., & Hämäläinen, P. (2008). Voluntary pupil size change as control in eyes only interaction. In Proceedings of the 2008 symposium on eye tracking research & applications. ETRA ‘08 (pp. 115–118). ACM.Google Scholar
  11. Fredrickson, B. L., Mancuso, R. A., Branigan, C., & Tugade, M. M. (2000). The undoing effect of positive emotions. Motivation and Emotion, 24, 237–258.CrossRefPubMedPubMedCentralGoogle Scholar
  12. Georgi, J., Kowalski, D., Ehlers, J., & Huckauf, A. (2015). Real-time feedback towards voluntary pupil control in human-computer interaction: Enabling continuous pupillary feedback. In H. M. Fardoun, V. M. R. Penichet, & D. M. Alghazzawi (Eds.), Communications in Computer and Information Science (Vol. 515). ICTs for improving patients rehabilitation research techniques (pp. 104–115). Berlin: Springer Verlag.CrossRefGoogle Scholar
  13. Healey, J., & Picard, R. W. (2005). Detecting stress during real-world driving tasks using physiological sensors. IEEE Transactions on Intelligent Transportation Systems, 6, 156–166.CrossRefGoogle Scholar
  14. Hess, E. H. (1972). Pupillometrics. In N. S. Greenfield & R. A. Sternbach (Eds.), Handbook of psychology (pp. 491–531). New York: N.S. Holt, Rinehart and Winston.Google Scholar
  15. Jackson, I., & Sirois, S. (2009). Infant cognition: Going full factorial with pupil dilation. Developmental Science, 12, 670–679.CrossRefPubMedGoogle Scholar
  16. Jacob, R. J. K. (1996). The future of input devices. ACM Computing Surveys, 28, 177–179.CrossRefGoogle Scholar
  17. Janisse, M. P. (1974). Pupil size, affect and exposure frequency. Social Behavior and Personality, 2, 125–146.CrossRefGoogle Scholar
  18. Jennings, J. R., Kamarck, T., Stewart, C., Eddy, M., & Johnson, P. (1992). Alternate cardiovascular baseline assessment techniques: Vanilla or resting baseline. Psychophysiology, 29, 742–750.CrossRefPubMedGoogle Scholar
  19. Kahneman, D., & Beatty, J. (1966). Pupil diameter and load on memory. Science, 154, 1583–1585.CrossRefPubMedGoogle Scholar
  20. Lehrer, P. M., Vaschillo, E., & Vaschillo, B. (2000). Resonant frequency biofeedback training to increase cardiac variability: Rationale and manual for training. Applied Psychophysiology and Biofeedback, 25(3), 177–191.CrossRefPubMedGoogle Scholar
  21. Loewenfeld, I. E. (1966). Comment on Hess’ findings. Survey of Opthalmology, 11, 291–294.Google Scholar
  22. Loewenstein, G., & Lerner, J. S. (2003). The role of affect in decision making. In R. Davidson, H. Goldsmith, & K. Scherer (Eds.), Handbook of affective science (pp. 619–642). Oxford: Oxford University Press.Google Scholar
  23. Palinko, O., Kun, A. L., Shyrokov, A., & Heeman, P. (2010). In Proceedings of the 2010th symposium on eye-tracking research & applications (pp. 141–144).Google Scholar
  24. Partala, T., & Surakka, V. (2003). Pupil size variation as an indication of affective processing. International Journal of Human-Computer Studies, 59, 185–198.CrossRefGoogle Scholar
  25. Raymond, J., Varney, C., Parkinson, L. A., & Gruzelier, J. H. (2005). The effects of alpha/theta neurofeedback on personality and mood. Cognitive Brain Research, 23(2), 287–292.CrossRefPubMedGoogle Scholar
  26. Sakakibara, M., Takeuchi, S., & Hayano, J. (1994). Effect of relaxation training on cardiac parasympathetic tone. Psychophysiology, 31, 223–228.CrossRefPubMedGoogle Scholar
  27. SensoMotoric Instruments. iView XTM Hi-Speed 1250.
  28. Sequeira, H., Hot, P., Silvert, L., & Delplanque, S. (2000). Electrical autonomic correlates of emotion. International Journal of Psychophysiology, 71, 50–56.CrossRefGoogle Scholar
  29. Sirois, S., & Jackson, I. (2014). Pupillometry. WIREs Cognitive Science, 5, 679–692.CrossRefPubMedGoogle Scholar
  30. Steinhauer, R., Siegle, G., Condray, R., & Pless, M. (2004). Sympathetic and parasympathetic innervation of pupillary dilation during sustained processing. International Journal of Psychophysiology, 52, 77–86.CrossRefPubMedGoogle Scholar
  31. Stoll, J., Chatelle, C., Carter, O., Koch, C., Laureys, S., & Einhäuser, W. (2013). Pupil responses allow communication in locked-in syndrome patients. Current Biology, 23, R647–R648.CrossRefPubMedGoogle Scholar
  32. Strauch, C., Georgi, J., Huckauf, A., & Ehlers, J. (2015). Slow trends: A problem in analysing pupil dynamics. In Proceedings of the 2nd international conference on physiological computing systems. Google Scholar
  33. Vasudeva, S., Claggett, A. L., Tietjen, G. E., & McGrady, A. V. (2003). Biofeedback-assisted relaxation in migraine headache: Relationship to cerebral blood flow velocity in the middle cerebral artery. Headache: The Journal of Head and Face Pain, 43(3), 245–250.CrossRefGoogle Scholar
  34. Wilhelm, B., Giedke, H., Luèdtke, H., Bittner, E., Hofmann, A., & Wilhelm, H. (2001). Daytime variations in central nervous system activation measured by pupillographic sleepiness test. Journal of Sleep Research, 10, 1–7.CrossRefPubMedGoogle Scholar
  35. Winn, B., Whitaker, D., Elliott, D. B., & Phillips, N. J. (1994). Factors affecting light-adapted pupil size in normal human subjects. Investigative Ophthalmology and Visual Science, 35, 1132–1137.PubMedGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Jan Ehlers
    • 1
  • Christoph Strauch
    • 1
  • Juliane Georgi
    • 1
  • Anke Huckauf
    • 1
  1. 1.Department of General PsychologyUlm UniversityUlmGermany

Personalised recommendations