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Emotional Investment in Naturalistic Data Collection

  • Ian Davies
  • Peter Robinson
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6974)

Abstract

We present results from two experiments intended to allow naturalistic data collection of the physiological effects of cognitive load. Considering the example of command and control environments, we identify shortcomings of previous studies which use either laboratory-based scenarios, lacking realism, or real-world scenarios, lacking repeatability. We identify the hybrid approach of remote-control which allows experimental subjects to remain in a laboratory setting, performing a real-world task in a completely controlled environment. We show that emotional investment is vital for evoking natural responses and that physiological indications of cognitive load manifest themselves more readily in our hybrid experimental setup. Finally, we present a set of experimental design recommendations for naturalistic data collection.

Keywords

Cognitive Load Secondary Task Skin Conductance Normalise Heart Rate Driving Simulator 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Parrot AR.Drone, http://ardrone.parrot.com/
  2. 2.
    Aasman, J., Mulder, G., Mulder, L.J.M.: Operator effort and the measurement of heart-rate variability. Human Factors 29(2), 161–170 (1987)Google Scholar
  3. 3.
    Backs, R.W., Seljos, K.A.: Metabolic and cardiorespiratory measures of mental effort: the effects of level of difficulty in a working memory task. International Journal of Psychophysiology 16(1), 57–68 (1994)CrossRefGoogle Scholar
  4. 4.
    Hart, S.O., Staveland, L.E.: Development of NASA-TLX (Task Load Index): Results of empirical and theoretical research (1988)Google Scholar
  5. 5.
    Healey, J.A., Picard, R.W.: Detecting stress during real-world driving tasks using physiological sensors. IEEE Transactions on Intelligent Transportation Systems 6(2), 156–166 (2005)CrossRefGoogle Scholar
  6. 6.
    Healey, J.A., Seger, J., Picard, R.W.: Quantifying driver stress: Developing a system for collecting and processing bio-metric signals in natural situations. In: Proceedings of the Rocky Mountain Bio-Engineering Symposium (1999)Google Scholar
  7. 7.
    Jansma, J.M., Ramsey, N.F., Coppola, R., Kahn, R.S.: Specific versus nonspecific brain activity in a parametric N-back task. Neuroimage 12(6), 688–697 (2000)CrossRefGoogle Scholar
  8. 8.
    Lisetti, C., Nasoz, F.: Affective intelligent car interfaces with emotion recognition. In: Proceedings of 11th International Conference on Human Computer Interaction (2005)Google Scholar
  9. 9.
    Recarte, M.A., Nunes, L.M.: Mental workload while driving: Effects on visual search, discrimination, and decision making. Journal of Experimental Psychology: Applied 9(2), 119–133 (2003)Google Scholar
  10. 10.
    Reimer, B., Mehler, B., Wang, Y., Coughlin, J.F.: The impact of systematic variation of cognitive demand on drivers visual attention across multiple age groups. In: Human Factors and Ergonomics Society Annual Meeting Proceedings, pp. 2052–2056 (2010)Google Scholar
  11. 11.
    Robinson, P., el Kaliouby, R.: Computation of emotions in man and machines. Philosophical Transactions of the Royal Society B: Biological Sciences 364(1535), 3441 (2009)CrossRefGoogle Scholar
  12. 12.
    Veltman, J.A., Gaillard, A.W.K.: Physiological indices of workload in a simulated flight task. Biological Psychology 42(3), 323–342 (1996)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Ian Davies
    • 1
  • Peter Robinson
    • 1
  1. 1.Computer LaboratoryUniversity of CambridgeUK

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