Abstract
The ability to control emotion is a skill which contributes to performance in the same way as cognitive and technical skills do to the successful completion of high stress operations. The interdependence between emotion, problem-solving and decision-making makes a negative emotion such as anxiety of interest in evaluating trainee performance in simulations which replicate stressful work conditions. Self-report measures of anxiety require trainees to interrupt the simulation experience to either complete psychological scales or make verbal reports of state anxiety. An uninterrupted, continuous measure of anxiety is, therefore, preferable for simulation environments. During this study, the anxiety levels of trainee pilots were tracked via electromyography, eye movements and pupillometry while undertaking required tasks in a flight simulation. Fixation duration and saccade rate corresponded reliably to pilot self-reports of anxiety, while pupil size and saccade amplitude did not show a strong comparison to changes in affective state. Large increases in muscle activation where recorded when higher anxiety was reported. The results suggest that a combination of physiological measures could provide a robust, continuous indicator of anxiety level. The implications of the current study on further development of physiological measures to support tracking anxiety as a tool for simulation training assessment are discussed.
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Ahn SJ, Bailenson J, Fox J, Jabon M (2009) Using automated facial expression analysis for emotion and behavior prediction. Paper presented at the annual meeting of the NCA 95th Annual Convention, Chicago, IL, Nov 11, 2009. http://vhil.stanford.edu/pubs/2010/ahn-hemm-facial-expression.pdf. Accessed 24 August 2010
Bailenson JN, Pontikakis ED, Mauss IB, Gross JJ, Jabon ME, Hutcherson CAC, Nass C, Oliver J (2008) Real-time classification of evoked emotions using facial feature tracking and physiological responses. Int J Hum Comput Stud 66:303–317
Ballard TJ (2009) The role of affect in goal setting and goal striving: a multilevel analysis. University of Queensland, Honors Thesis in Psychological Science
Blangsted AK, Sogaard K, Christensen H, Sjogaard G (2004) The effect of physical and psychosocial loads on the trapezius muscle activity during computer keying tasks and rest periods. Eur J Appl Physiol 91:253–258
Chapman C, Oka S, Bradshaw DH, Jacobson RC, Donaldson GW (1999) Phasic pupil dilation response to noxious stimulation in normal volunteers: relationship to brain evoked potentials and pain report. Psychophysiology 36:44–52
Damasio AR (1995) Descartes’ error: emotion, reason, and the human brain. Harper Collins, New York
Daniels LM, Stupnisky RH, Pekrun R, Haynes TL, Perry RP, Newall NE (2009) A longitudinal analysis of achievement goals: from affective antecedents to emotional effects and achievement outcomes. J Educ Psychol 101:948–963
El Kaliouby R, Robinson P (2005) Real-time inference of complex mental states from facial expressions and head gestures. In: Real-time vision for HCI. New York, Springer, pp 181–200
Ericsson KA, Charness N, Feltovich PJ, Hoffman RR (2006) The Cambridge handbook of expertise and expert performance. Cambridge University Press, Cambridge
Gee P (2010) Measuring emotions throughout skill acquisition: a multi-level analysis. In: Proceedings of ICAP, Jul 11–16, Melbourne
Gucciardi D, Gordon S (2009) Development and preliminary validation of the cricket mental toughness inventory (CMTI). J Sports Sci 27:1293–1310
Harrigan JA, O’Connell DM (1996) How do you look when feeling anxious? Facial displays of anxiety. Pers Individ Dif 21:205–212
Hunsley J (1990) The factor structure of the multiple affect adjective check list—revised (MAACL-R): some statistical considerations. J Psychopathol Behav Assess 12:99–101
Juang M, Alessi NE (2000) An introduction to virtual reality in psychiatry. Canadian psychiatric association. Bulletin 32:1–3
Lazarus RS (1966) Psychological stress and the coping process. McGraw-Hill, New York
Lehrer J. (2009) Deliberate calm’ guided US airways crew. The Los Angeles Times. http://www.latimes.com/news/opinion/commentary/la-oe-lehrer17-2009jan17,0,5063668.story. Accessed 6 Feb 2012
Liao W, Zhang W, Zhu Z, Ji Q, Gray WD (2006) Toward a decision-theoretic framework for affect recognition and user assistance. Int J Hum Comput Stud 64:847–873
Loft S, Bolland S, Humphreys M, Neal A (2009) A theory and model of conflict detection in air traffic control: incorporating environmental constraints. J Exp Psychol Appl 15:106–124
Lombard M, Ditton T (1997) At the heart of it all: the concept of presence. J Comput-Mediat Commun 3:1–3
Lundberg U, Forsman M, Zachau G, Eklof M, Palmerud G, Melin B, Kadefors R (2002) Effects of experimentally induced mental and physical stress on motor unit recruitment in the trapezius muscle. Work Stress 16:166–178
Matsumoto D, Wilson J (2008) Culture, emotion, and motivation. In: Sorrentino R, Yamaguchi S (eds) Handbook of motivation and cognition across cultures. Elsevier, New York, pp 541–563
Nilsen K, Sand T, Stovner L, Leistad R, Westgaard R (2007) Autonomic and muscular responses and recovery to one-hour laboratory mental stress in healthy subjects. BMC Musculoskelet Disord 8:81
Nilsen KB, Sand T, Borchgrevink P, Leistad RB, Ro M, Westgaard RH (2008) A unilateral sympathetic blockade does not affect stress-related pain and muscle activity in patients with chronic musculoskeletal pain. Scand J Rheumatol 37:53–61
Partla T, Surakka V (2003) Pupil size variation as an indication of affective processing. Int J Hum Comput Stud 59:185–198
Redden ES, Sheehy JB, Bjorkman EA (2004) The study and measurement of human performance by military service laboratories. Adv Hum Perform Cogn Eng Res 5:517–559
Russo MB, Stetz MC, Thomas ML (2005) Monitoring and predicting cognitive state and performance via physiological correlates of neuronal signals. Aviat Space Environ Med 76:C59–C63
Seo M, Barrett LF, Bartunek JM (2004) The role of affective experience in work motivation. Acad Manag Rev 29:423–439
Susskind JM, Lee DH, Cusi A, Feiman R, Grabski W, Anderson AK (2008) Expressing fear enhances sensory acquisition. Nat Neurosci 11:843–850
Tichon J (2007) Using presence to improve a virtual training environment. Cyberpsychol Behav 10(6):781–788
Tichon J (2012) Evaluation of virtual reality training using affect. Int J E-Learn 11:107–116
Witmer BG, Singer MJ (1998) Measuring presence in virtual environments: a presence questionnaire. Presence: teleoperators and virtual. Environments 7:225–240
Yoshie M, Kudo K, Ohtsuki T (2008) Effects of psychological stress on state anxiety, electromyographic activity, and arpeggio performance in pianists. Med Probl Perform Art Sept 23:120–132
Acknowledgments
The work presented in this paper was supported by grant from the US Air Force Office of Scientific Research (AOARD 104011) and the Australian Research Council (LP0883839). Thanks also to Aviation High, Hendra, Queensland.
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Tichon, J.G., Wallis, G., Riek, S. et al. Physiological measurement of anxiety to evaluate performance in simulation training. Cogn Tech Work 16, 203–210 (2014). https://doi.org/10.1007/s10111-013-0257-8
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DOI: https://doi.org/10.1007/s10111-013-0257-8