Experimental Analysis of Cusp Models

  • Stephen J. Guastello
  • Anton Shircel
  • Matthew Malon
  • Paul Timm
  • Kelsey Gonring
  • Katherine Reiter
Part of the Evolutionary Economics and Social Complexity Science book series (EESCS, volume 13)


This chapter presents an empirical assessment of the cusp catastrophe models for cognitive workload and fatigue as outlined in the previous chapter. Participants were 299 undergraduates who completed a series of psychological tests and measurements, which were followed by a financial decision making task that escalated in workload. The task required the participants to work in one of three speed conditions. Results supported both cusp models for both optimizing and risk taking criteria as evidenced by a superior degree of fit compared to the alternative linear models. For workload, conscientiousness and self-control as were the elasticity-rigidity (bifurcation) factors in optimizing, and field dependence and work ethic were elasticity variables in risk tasking; speed and decision complexity were the asymmetry variables. For fatigue, work completed and work speed were the bifurcation factors, as hypothesized, for both optimizing and risk taking; field independence was the asymmetry variable for both dependent measures, and performance on an anagram test was another compensatory ability that inhibited risk taking.


Risk Taking Speed Condition Fatigue Effect Risky Choice Fatigue Model 
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.


  1. Bem, S. L. (1974). The measurement of psychological androgyny. Journal of Consulting and Clinical Psychology, 42, 155–162.CrossRefGoogle Scholar
  2. Buchholz, R. A. (1977). The belief structure of managers relative to work concepts measured by a factor analytic model. Personnel Psychology, 30, 567–587.CrossRefGoogle Scholar
  3. Conrad, R. (1951). Speed and load stress in a sensorimotor skill. British Journal of Industrial Medicine, 8, 1–7.Google Scholar
  4. Goldberg, L. (2011). International personality item pool. Retrieved September 30, 2011, from http://ipip.ori.org
  5. Guastello, S. J. (1995). Chaos, catastrophe, and human affairs: Applications of nonlinear dynamics to work, organizations, and social evolution. Hillsdale: Lawrence Erlbaum Associates.Google Scholar
  6. Guastello, S. J. (2002). Managing emergent phenomena: Nonlinear dynamics in work organizations. Hillsdale: Lawrence Erlbaum Associates.Google Scholar
  7. Guastello, S. J. (2011). Discontinuities: SETAR and catastrophe models with polynomial regression. In S. J. Guastello & R. A. M. Gregson (Eds.), Nonlinear dynamical systems analysis for the behavioral sciences using real data (pp. 251–280). Boca Raton: CRC Press.Google Scholar
  8. Guastello, S. J. (2013). Catastrophe theory and its applications to I/O psychology. In J. M. Cortina & R. Landis (Eds.), Frontiers of methodology in organizational research (pp. 29–62). New York: Routledge.Google Scholar
  9. Guastello, S. J., Boeh, H., Schimmels, M., Gorin, H., Huschen, S., Davis, E., Peters, N. E., Fabisch, M., & Poston, K. (2012). Cusp catastrophe models for cognitive workload and fatigue in a verbally-cued pictorial memory task. Human Factors, 54, 811–825.CrossRefGoogle Scholar
  10. Guastello, S. J., Boeh, H., Gorin, H., Huschen, S., Peters, N. E., Fabisch, M., & Poston, K. (2013). Cusp catastrophe models for cognitive workload and fatigue: A comparison of seven task types. Nonlinear Dynamics, Psychology, and Life Sciences, 17, 23–47.Google Scholar
  11. Guastello, S. J., Malon, M., Timm, P., Weinberger, K., Gorin, H., Fabisch, M., & Poston, K. (2014). Catastrophe models for cognitive workload and fatigue in a vigilance dual-task. Human Factors, 56, 737–751.CrossRefGoogle Scholar
  12. Guastello, S. J., Reiter, K., Malon, M., Timm, P., Shircel, A., & Shaline, J. (2015). Catastrophe models for cognitive workload and fatigue in N-back tasks. Nonlinear Dynamics, Psychology, and Life Sciences, 19, 173–200.Google Scholar
  13. Guion, R. M. (1998). Assessment, measurement, and prediction for personnel decisions. Hillsdale: Lawrence Erlbaum Associates.Google Scholar
  14. Halpern, D. F., Benbow, C. P., Geary, D. C., Gur, R. C., Hyde, J. S., & Gernsbacher, M. A. (2007). The science of sex differences in science and mathematics. Psychological Science in the Public Interest, 8(1), 1–51.Google Scholar
  15. Hancock, P. A. (2007). On the process of automation transition in multitask human-machine systems. IEEE Transactions on Systems, Man, and Cybernetics – Part A: Systems and Humans, 37, 586–598.CrossRefGoogle Scholar
  16. Hart, S. G., & Staveland, L. E. (1988). Development of the NASA task load index (TLX): Results of experimental and theoretical research. In P. A. Hancock & N. Meshkati (Eds.), Human workload (pp. 138–183). Amsterdam: North-Holland.Google Scholar
  17. Kane, M. J., & Engle, R. W. (2002). The role of prefrontal cortex in working-memory capacity, executive attention, and general fluid intelligence: An individual-differences perspective. Psychonomic Bulletin & Review, 9, 617–671.CrossRefGoogle Scholar
  18. Ladouceur, C. D., Silk, J. S., Dahl, R. E., Ostapenko, L., Kronhaus, D. M., & Phillips, M. L. (2009). Fearful faces influence attentional control processes in anxious youth and adults. Emotion, 9, 855–864.CrossRefGoogle Scholar
  19. Naber, A. M., McDonald, J. N., Asenuga, O. A., & Arthur, W., Jr. (2015). Team members’ interaction anxiety and team training effectiveness: A catastrophic relationship? Human Factors, 57, 163–176.CrossRefGoogle Scholar
  20. Porcelli, A. J., & Delgado, M. R. (2009). Acute stress modulates risk taking in financial decisions. Psychological Science, 20, 278–283.CrossRefGoogle Scholar
  21. Rosser, J. B., Jr. (1997). Speculations on nonlinear speculative bubbles. Nonlinear Dynamics, Psychology, and Life Sciences, 1, 275–300.CrossRefGoogle Scholar
  22. Schutte, N. S., Malouf, J. M., Hall, L. E., Haggerty, D. J., Cooper, J. T., Golden, C. J., & Dornheirn, L. (1998). Development and validation of a measure of emotional intelligence. Personality and Individual Differences, 25, 167–177.CrossRefGoogle Scholar
  23. Slovic, P., & Peters, E. (2006). Risk perception and affect. Current Directions in Psychological Science, 15, 322–325.CrossRefGoogle Scholar
  24. Stamovlasis, D. (2006). The nonlinear dynamical hypothesis in science education problem solving: A catastrophe theory approach. Nonlinear Dynamics, Psychology and Life Science, 10, 37–70.Google Scholar
  25. Stamovlasis, D. (2011). Nonlinear dynamics and neo-Piagetian theories in problem solving: Perspectives on a new epistemology and theory development. Nonlinear Dynamics, Psychology and Life Science, 15, 145–173.Google Scholar
  26. Stamovlasis, D., & Tsaparlis, G. (2012). Applying catastrophe theory to an information-processing model of problem solving in science education. Science Education, 96, 392–410.CrossRefGoogle Scholar
  27. Taylor, J. A. (1953). A personality scale of manifest anxiety. Journal of Abnormal and Social Psychology, 48, 285–290.CrossRefGoogle Scholar
  28. Witkin, H. A., Oltman, P. K., Raskin, E., & Karp, S. A. (2002). A manual for the embedded figures test (2nd ed.). Palo Alto: Consulting Psychologists Press.Google Scholar
  29. Zuckerman, M., Buchsbaum, M. S., & Murphy, D. L. (1978). Sensation seeking and its biological correlates. Psychological Bulletin, 88, 187–214.CrossRefGoogle Scholar

Copyright information

© Springer Japan 2016

Authors and Affiliations

  • Stephen J. Guastello
    • 1
  • Anton Shircel
    • 2
  • Matthew Malon
    • 3
  • Paul Timm
    • 4
  • Kelsey Gonring
    • 1
  • Katherine Reiter
    • 1
  1. 1.Marquette UniversityMilwaukeeUSA
  2. 2.Kohler CorporationSheboyganUSA
  3. 3.Mount Mary UniversityMilwaukeeUSA
  4. 4.Mayo ClinicRochesterUSA

Personalised recommendations