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Applications of Cognitive Load Measurement

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Robust Multimodal Cognitive Load Measurement

Abstract

In this chapter, we present some typical application examples of cognitive load assessment and demonstrate the feasibility and applicability of multimodal cognitive load measurement approaches in various applications and instances of HCI.

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References

  1. N. Nourbakhsh, Machine Learning Methods for Multimodal Cognitive Load Measurement (The University of Sydney, Sydney, 2015)

    Google Scholar 

  2. J. Sweller, Cognitive load theory, learning difficulty, and instructional design. Learn. Instr. 4(4), 295–312 (1994)

    Article  Google Scholar 

  3. J. Sweller, J. Merrienboer, F. Paas, Cognitive architecture and instructional design. Educ. Psychol. Rev. 10(3), 251–296 (1998)

    Article  Google Scholar 

  4. A.D. Baddeley, Working memory. Science 255, 556–559 (1992)

    Article  Google Scholar 

  5. J. Back, C. Oppenheim, A model of cognitive load for IR: implications for user relevance feedback interaction, Inf. Res. 6(2) (2001)

    Google Scholar 

  6. Y. Shi, E. Choi, R. Taib, F. Chen, Designing cognition-adaptive human–computer interface for mission-critical systems, in Information Systems Development, ed. by G.A. Papadopoulos, W. Wojtkowski, G. Wojtkowski, S. Wrycza, J. Zupancic (Springer, Paphos, 2010), pp. 111–119

    Google Scholar 

  7. Victoria Police, Bushfires Death Toll Revised to 173 (2009). [Online]. Available: http://www.police.vic.gov.au/content.asp?Document_ID=20350

  8. M.A. Khawaja, F. Chen, N. Marcus, Measuring cognitive load using linguistic features: Implications for usability evaluation and adaptive interaction design. Int. J. Hum. Comput. Interact. 30(5), 343–368 (2014)

    Article  Google Scholar 

  9. D.L. Strayer, J.M. Cooper, J. Turrill, J. Coleman, N. Medeiros-Ward, F. Biondi, Measuring Cognitive Distraction in the Automobile (AAA Foundation for Traffic Safety, Washington, DC, 2013)

    Google Scholar 

  10. A.L. Kun, Z. Medenica, O. Palinko, P.A. Heeman, Utilizing pupil diameter to estimate cognitive load changes during human dialogue: A preliminary study, in AutomotiveUI 2011 Adjunct Proceedings, Salzburg, Austria, 2011

    Google Scholar 

  11. J. Engström, E. Johansson, J. Östlund, Effects of visual and cognitive load in real and simulated motorway driving. Transport. Res. F: Traffic Psychol. Behav. 8(2), 97–120 (2005)

    Article  Google Scholar 

  12. D.E. Crundall, G. Underwood, Effects of experience and processing demands on visual information acquisition in drivers. Ergonomics 41(4), 448–458 (1998)

    Article  Google Scholar 

  13. A. Hess, J. Jung, A. Maier, R. Taib, K. Yu, B. Itzstein, Elicitation of Mental States and User Experience Factors in a Driving Simulator (IEEE, Gold Coast, 2013), pp. 43–48

    Google Scholar 

  14. J. Jung, A. maier, A. Gro, et al, Investigating the effect of cognitive load on UX: A driving study, in Proceedings of the 3rd International Conference on Automotive User Interfaces and Interactive Vehicular Applications, 2011, pp. 134–137

    Google Scholar 

  15. J.A. Cannon-Bowers, E. Salas, S. Converse, Shared mental models in expert team decision making, in Individual and Group Decision-Making: Current Issues, ed. by J. Castellan (Lawrence Erlbaum Associates, Hillsdale, 1993), pp. 221–246

    Google Scholar 

  16. K.L. Hessler, A.M. Henderson, Interactive learning research: Application of cognitive load theory to nursing education. Int. J. Nurs. Educ. Scholarsh. 10(1), 133–141 (2013)

    Article  Google Scholar 

  17. J.Q. Young, J. Van Merrienboer, S. Durning, O. Ten Cate, Cognitive load theory: Implications for medical education: AMEE Guide No. 86. Med. Teach. 36(5), 371–384 (2014)

    Article  Google Scholar 

  18. K.J. Harms, Applying cognitive load theory to generate effective programming tutorials, in Proceedings of IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC) 2013, 2013, pp. 179–180

    Google Scholar 

  19. F. Anvari, H.M.T. Tran, M. Kavakli, Using cognitive load measurement and spatial ability test to identify talented students in three-dimensional computer graphics programming. Int. J. Inf. Educ. Technol. 3, 94–99 (2013)

    Article  Google Scholar 

  20. S. Gillmor, J. Poggio, S. Embretson, Effects of reducing the cognitive load of mathematics test items on student performance. Numeracy 8(1) (2015)

    Google Scholar 

  21. S. Kuldas, L. Satyen, H. Ismail, Greater cognitive effort for better learning: Tailoring an instructional design for learners with different levels of knowledge and motivation. Psychologica Belg. 54(4), 350–373 (2014)

    Article  Google Scholar 

  22. J. Sweller, P. Ayres, S. Kalyuga, Cognitive Load Theory (Springer, New York, 2011)

    Book  Google Scholar 

  23. J.T. Coyne, C. Baldwin, A. Cole, C. Sibley, D.M. Roberts, Applying real time physiological measures of cognitive load to improve training, in Foundations of Augmented Cognition. Neuroergonomics and Operational Neuroscience, ed. by D.D. Schmorrow, I.V. Estabrooke, M. Grootjen (Springer, Berlin/Heidelberg, 2009), pp. 469–478

    Chapter  Google Scholar 

  24. E. Martin, R. Bajcsy, Leveraging wireless sensors and smart phones to study gait variability, in Informatics Engineering and Information Science, ed. by A.A. Manaf, S. Sahibuddin, R. Ahmad, S.M. Daud, E. El-Qawasmeh (Springer, Berlin/Heidelberg, 2011), pp. 95–111

    Chapter  Google Scholar 

  25. G. Allali, F. Assal, R.W. Kressig, V. Dubost, F.R. Herrmann, O. Beauchet, Impact of impaired executive function on gait stability. Dement. Geriatr. Cogn. Disord. 26(4), 364–369 (2008)

    Article  Google Scholar 

  26. G. Yogev, M. Plotnik, C. Peretz, N. Giladi, J.M. Hausdorff, Gait asymmetry in patients with Parkinson’s disease and elderly fallers: When does the bilateral coordination of gait require attention? Exp. Brain Res. 177(3), 336–346 (2007)

    Article  Google Scholar 

  27. T. Nakamura, K. Meguro, H. Yamazaki, H. Okuzumi, A. Tanaka, A. Horikawa, K. Yamaguchi, N. Katsuyama, M. Nakano, H. Arai, H. Sasaki, Postural and gait disturbance correlated with decreased frontal cerebral blood flow in Alzheimer disease. Alzheimer Dis. Assoc. Disord. 11(3), 132–139 (1997)

    Article  Google Scholar 

  28. C. Rosano, J. Brach, S. Studenski, W.T. Longstreth, A.B. Newman, Gait variability is associated with subclinical brain vascular abnormalities in high-functioning older adults. Neuroepidemiology 29(3–4), 193–200 (2007)

    Article  Google Scholar 

  29. D. Joshi, S. Anand, Cyclogram and cross correlation: A comparative study to quantify gait coordination in mental state. J. Biomed. Sci. Eng. 03(03), 322–326 (2010)

    Article  Google Scholar 

  30. P.L. Sheridan, J.M. Hausdorff, The role of higher-level cognitive function in gait: Executive dysfunction contributes to fall risk in Alzheimer’s disease. Dement. Geriatr. Cogn. Disord. 24(2), 125–137 (2007)

    Article  Google Scholar 

  31. K. Na, Exploring the effect of cognitive load on the propensity for query reformulation behavior. PhD thesis, The Florida State University, 2012

    Google Scholar 

  32. P.J.-H. Hu, P.-C. Ma, P.Y.K. Chau, Evaluation of user interface designs for information retrieval systems: A computer-based experiment. Decis. Support. Syst. 27(1–2), 125–143 (1999)

    Article  Google Scholar 

  33. K.G. Seeber, Cognitive load in simultaneous interpreting: Measures and methods. Target 25(1), 18–32 (2013)

    Article  Google Scholar 

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Chen, F. et al. (2016). Applications of Cognitive Load Measurement. In: Robust Multimodal Cognitive Load Measurement. Human–Computer Interaction Series. Springer, Cham. https://doi.org/10.1007/978-3-319-31700-7_16

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  • DOI: https://doi.org/10.1007/978-3-319-31700-7_16

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-31698-7

  • Online ISBN: 978-3-319-31700-7

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