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A Nonlinear Paradigm for Resilience, Workload, Performance, and Clinical Phenomena

Published online by Cambridge University Press:  04 July 2016

Stephen J. Guastello
Stephen J. Guastello*
Affiliation:
Department of Psychology, Marquette University
*
Correspondence concerning this article should be addressed to Stephen J. Guastello, Department of Psychology, Marquette University, P.O. Box 1881, Milwaukee, WI 53201-1881. E-mail: [email protected]
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Extract

Research on resilience in the workplace is currently limited by at least two issues: an inconsistent documentation and choice of the stress-producing events and a singular construct of what constitutes resilience (Britt, Shen, Sinclair, Grossman, & Klieger, 2016). This commentary summarizes some recent experimental research that was possibly too new to have been included in the review and that offers some insights to both concerns. The research is predicated on a theoretical model that explains the role of resilience in either work-related or clinical outcomes and the temporal dynamics of work performance.

Type
Commentaries
Information
Industrial and Organizational Psychology , Volume 9 , Issue 2 , June 2016 , pp. 509 - 516
Copyright
Copyright © Society for Industrial and Organizational Psychology 2016 

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References

Ashby, W. R. (1956). Introduction to cybernetics. New York, NY: Wiley.Google Scholar
Britt, T. W., Shen, W., Sinclair, R. R., Grossman, M., & Klieger, D. M. (2016). How much do we really know about employee resilience? Industrial and Organizational Psychology: Perspectives on Science and Practice, 9 (2), 378–404.CrossRefGoogle Scholar
Cantwell, R. H., & Moore, P. J. (1996). The development of measures of individual differences in self-regulatory control and their relationship to academic performance. Contemporary Educational Psychology, 21, 500–517.CrossRefGoogle ScholarPubMed
Cattell, H. E. P. (1994). Development of the 16PF fifth edition. In Conn, S. R. & Rieke, M. L. (Eds.), 16PF fifth edition: Technical manual (pp. 1–20). Champaign, IL: Institute for Personality and Ability Testing.Google Scholar
Guastello, S. J. (1985). Euler buckling in a wheelbarrow obstacle course: A catastrophe with complex lag. Behavioral Science, 30, 204–212.CrossRefGoogle Scholar
Guastello, S. J. (2003). Nonlinear dynamics, complex systems, and occupational accidents. Human Factors in Manufacturing, 13, 293–304.CrossRefGoogle Scholar
Guastello, S. J. (2013). Catastrophe theory and its applications to I/O psychology. In Cortina, J. M. & Landis, R. (Eds.), Frontiers of methodology in organizational research (pp. 29–61). New York, NY: Routledge.Google Scholar
Guastello, S. J. (2014a). Catastrophe models for cognitive workload and fatigue: Memory functions, multitasking, vigilance, financial decisions and risk. Proceedings of the Human Factors and Ergonomics Society, 58, 908–912.Google Scholar
Guastello, S. J. (2014b). Vigilance phenomena, cognitive workload, and fatigue. American Psychologist, 69, 85–87.CrossRefGoogle ScholarPubMed
Guastello, S. J. (2015). The complexity of the psychological self and the principle of optimum variability. Nonlinear Dynamics, Psychology, and Life Sciences, 19, 511–528.Google ScholarPubMed
Guastello, S. J. (Ed.). (2016). Cognitive workload and fatigue in financial decision making. New York, NY: Springer.CrossRefGoogle Scholar
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 ScholarPubMed
Guastello, S. J., Boeh, H., Schimmels, M., Gorin, H., Huschen, S., Davis, E.,. . . 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
Guastello, S. J., Boeh, H., Shumaker, C., & Schimmels, M. (2012). Catastrophe models for cognitive workload and fatigue. Theoretical Issues in Ergonomics Science, 13, 586–602.CrossRefGoogle Scholar
Guastello, S. J., Gorin, H., Huschen, S., Peters, N. E., Fabisch, M., Poston, K., & Weinberger, K. (2013). The minimum entropy principle and task performance. Nonlinear Dynamics, Psychology, and Life Sciences, 17, 405–424.Google ScholarPubMed
Guastello, S. J., & Gregson, R. A. M. (Eds.). (2011). Nonlinear dynamical systems analysis for the behavioral sciences using real data. Boca Raton, FL: CRC Press/Taylor & Francis.Google Scholar
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
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 ScholarPubMed
Guastello, S. J., Shircel, A., Malon, M., & Timm, P. (2015). Individual differences in the experience of cognitive workload. Theoretical Issues in Ergonomics Science, 16, 20–52.CrossRefGoogle Scholar
Hancock, P. A., & Warm, J. S. (1989). A dynamic model of stress and sustained attention. Human Factors, 31, 519–537.CrossRefGoogle ScholarPubMed
Hart, S. G., & Staveland, L. E. (1988). Development of the NASA Task Load Index (TLX): Results of experimental and theoretical research. In Hancoke, P. A. & Meshkati, N. (Eds.), Human workload (pp. 138–183). Amsterdam, the Netherlands: North-Holland.Google Scholar
Katerndahl, D. (2010). Cracking the linear lens. Nonlinear Dynamics, Psychology, and Life Sciences, 14, 349–352.Google Scholar
Kato, T. (2012). Development of the Coping Flexibility Scale: Evidence for the coping flexibility hypothesis. Journal of Counseling Psychology, 59, 262–273.CrossRefGoogle ScholarPubMed
MacLean, M. H., & Arnell, K. M. (2010). Personality predicts temporal attention costs in the attentional blink paradigm. Psychonomic Bulletin & Review, 17, 556–562.CrossRefGoogle ScholarPubMed
Navarro, J., & Rueff-Lopes, P. (2015). Healthy variability in organizational behavior: Empirical evidence and new steps for future research. Nonlinear Dynamics, Psychology, and Life Sciences, 19, 529–552.Google ScholarPubMed
Pascual-Leone, J. (1970). A mathematical model for the transition rule in Piaget's developmental stages. Acta Psychologia, 32, 301–345.CrossRefGoogle Scholar
Pincus, D., & Metten, A. (2010). Nonlinear dynamics in biopsychosocial resilience. Nonlinear Dynamics, Psychology, and Life Science, 14, 353–380.Google ScholarPubMed
Ribeiro, J., & Lourenço, C. (2016). Dynamics of resilient and non-resilient mood. Nonlinear Dynamics, Psychology, and Life Science, 20, 23–48.Google ScholarPubMed
Rose, C. L., Murphy, L. B., Byard, L., & Nikzad, K. (2002). The role of the Big Five personality factors in vigilance performance and workload. European Journal of Personality, 16, 185–200.CrossRefGoogle Scholar
Schuldberg, D. (2015). What is optimum variability? Nonlinear Dynamics, Psychology, and Life Sciences, 19, 553–568.Google ScholarPubMed
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
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 ScholarPubMed
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 ScholarPubMed
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
Taylor, J. A. (1953). A personality scale of manifest anxiety. Journal of Abnormal and Social Psychology, 48, 285–290.CrossRefGoogle ScholarPubMed
Thompson, H. L. (2010). The stress effect: Why smart leaders make dumb decisions—and what to do about it. San Francisco, CA: Jossey-Bass.Google Scholar
Zeeman, E. C. (1977). Catastrophe theory: Selected papers, 1972–1977. Reading, MA: Addison-Wesley.Google Scholar