Psychophysiological Baseline Methods and Usage

  • Avonie Parchment
  • Ryan W. WohleberEmail author
  • Lauren Reinerman-Jones
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9743)


There are several different baseline techniques available for completing psychophysiological research, yet no overarching set of guidelines exists to help researchers choose the best method. This review examines several methods used in various fields and highlights the importance and pitfalls of each. As part of this effort we conducted a small study that examines three different baseline techniques. In line with the Law of Initial Value (LIV), outcomes signal a strong positive effect for measures when utilizing a resting baseline, a weaker positive effect when utilizing a baseline directly before tasking, and a nominal effect when calibrating using a comprehensive baseline. The authors caution future researchers to fully assess the needs of their experiment before utilizing comprehensive, vanilla, or resting baselines, and to weigh the consequences of the length and number of baselines utilized. Further investigation of low workload and vigilance tasking is needed to determine whether use of vanilla and comprehensive baselines provide better contrast than a resting baseline.


Resting baseline Comprehensive baseline Vanilla baseline Methods Psychophysiological measures 


  1. 1.
    Schirner, G., Erdogmus, D., Chowdhury, K., Padir, T.: The future of human-in-the-loop cyber-physical systems. Computer 1, 36–45 (2013)CrossRefGoogle Scholar
  2. 2.
    Mandryk, R.L.: Physiological measures for game evaluation. Game Usability: Advice from the Experts for Advancing the Player Experience, pp. 207–235 (2008) Google Scholar
  3. 3.
    Abich, J., Matthews, G., Reinerman-Jones, L.: Individual differences in UGV operation: a comparison of subjective and psychophysiological predictors. In: Proceedings of the Human Factors and Ergonomics Society Annual Meeting, vol. 59, no. 1, pp. 741–745. SAGE Publications (2015)Google Scholar
  4. 4.
    Cacioppo, J.T., Tassinary, L.G., Berntson, G.: Handbook of Psychophysiology. Cambridge University Press, Cambridge (2007)CrossRefGoogle Scholar
  5. 5.
    Wilder, J.: Stimulus and Response: The Law of Initial Value. Wright, Bristol (1967)Google Scholar
  6. 6.
    Cupini, L.M., Matteis, M., Troisi, E., Sabbadini, M., Bernardi, G., Caltagirone, C., Silvestrini, M.: Bilateral simultaneous transcranial doppler monitoring of flow velocity changes during visuospatial and verbal working memory tasks. Brain 119(4), 1249–1253 (1996)CrossRefGoogle Scholar
  7. 7.
    Jennings, J.R., Kamarck, T., Stewart, C., Eddy, M., Johnson, P.: Alternate cardiovascular baseline assessment techniques: vanilla or resting baseline. Psychophysiology 29(6), 742–750 (1992)CrossRefGoogle Scholar
  8. 8.
    Morcom, A.M., Fletcher, P.C.: Does the brain have a baseline? Why we should be resting a rest. Neuroimage 37(4), 1073–1082 (2007)CrossRefGoogle Scholar
  9. 9.
    Piferi, R.L., Kline, K.A., Younger, J., Lawler, K.A.: An alternative approach for achieving cardiovascular baseline: viewing an aquatic video. Int. J. Psychophysiol. 37, 207–217 (2000)CrossRefGoogle Scholar
  10. 10.
    Stern, R.M., Ray, W.J., Quigley, K.S.: Psychophysiological Recording, 2nd edn. Oxford University Press, Oxford (2001)Google Scholar
  11. 11.
    Gerin, W., Pieper, C., Pickering, T.G.: Anticipatory and residual effects of an active coping task on pre- and post-stress baselines. J. Psychosom. Res. 38, 139–149 (1994)CrossRefGoogle Scholar
  12. 12.
    Reinerman-Jones, L.E., Matthews, G., Langheim, L.K., Warm, J.S.: Selection for vigilance assignments: a review and proposed new direction. Theor. Issues Ergon. Sci. 12(4), 273–296 (2010)CrossRefGoogle Scholar
  13. 13.
    Fishel, S.R., Muth, E.R.: Establishing appropriate physiological baseline procedures for real-time physiological measurement. J. Cogn. Eng. Decis. Making 1(3), 286–308 (2007)CrossRefGoogle Scholar
  14. 14.
    Jacob, R.G., Shapiro, A.P.: Is the effect of stress management on blood pressure just regression to the mean? Homeostasis Health Dis. (1994)Google Scholar
  15. 15.
    Piper, S.K., Krueger, A., Koch, S.P., Mahnert, J., Habermehl, C., Stenbrink, J., Obrig, H., Schmitz, C.H.: A wearable multi-channel fNIRS system for brain imaging in freely moving subjects. Neuroimage 85(1), 64–71 (2014)CrossRefGoogle Scholar
  16. 16.
    Stroobant, N., Vingerhoets, G.: Transcranial Doppler ultrasonography monitoring of cerebral hemodynamics during performance of cognitive tasks: a review. Neuropsychol. Rev. 10(4), 213–231 (2000)CrossRefGoogle Scholar
  17. 17.
    Greene, J.D., Nystrom, L.E., Engell, A.D., Darley, J.M., Cohen, J.D.: The neural bases of cognitive conflict and control in moral judgment. Neuron 44(2), 389–400 (2004)CrossRefGoogle Scholar
  18. 18.
    Barber, D., Leontyev, S., Sun, B., Davis, L., Nicholson, D., Chen, J.Y.: The mixed-initiative experimental testbed for collaborative human robot interactions. In: Collaborative Technologies and Systems, IEEE, pp. 483–489 (2008)Google Scholar
  19. 19.
    Frederick, S.: Cognitive reflection and decision making. J. Econ. Perspect. 19, 25–42 (2005)CrossRefGoogle Scholar
  20. 20.
    Kaminsky, P., Simchi-Levi, D.: A new computerized beer game: a tool for teaching the value of integrated supply chain management. Glob. Supply Chain Technol. Manag. 1(1), 216–225 (1998)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Avonie Parchment
    • 1
  • Ryan W. Wohleber
    • 1
    Email author
  • Lauren Reinerman-Jones
    • 1
  1. 1.Institute for Simulation and TrainingUniversity of Central FloridaOrlandoUSA

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