Effects of different real-time feedback types on human performance in high-demanding work conditions

Experiencing stress during training is a way to prepare professionals for real-life crises. With the help of feedback tools, professionals can train to recognize and overcome negative effects of stress on task performances. This paper reports two studies that empirically examined the effect of such a feedback system. The system, based on the COgnitive Performance and Error (COPE) model, provides its users with physiological, predicted performance and predicted error-chance feedback. The first experiment focussed on creating stressful scenarios and establishing the parameters for the predictive models for the feedback system. Participants (n=9) performed fire-extinguishing tasks on a virtual ship. By altering time pressure, information uncertainty and consequences of performance, stress was induced. COPE variables were measured and models were established that predicted performance and the chances on specific errors. In the second experiment a new group of participants (n=29) carried out the same tasks while receiving eight different combinations of the three feedback types in a counterbalanced order. Performance scores improved when feedback was provided during the task. The number of errors made did not decrease. The usability score for the system with physiological feedback was significantly higher than a system without physiological feedback, unless combined with error feedback. This paper shows effects of feedback on performances and usability. To improve the effectiveness of the feedback system it is suggested to provide more in-depth tutorial sessions. Design changes are recommended that would make the feedback system more effective in improving performances. © 2016 The Authors. Published by Elsevier Ltd.

[1]  J. Beaubien,et al.  The use of simulation for training teamwork skills in health care: how low can you go? , 2004, Quality and Safety in Health Care.

[2]  Neerincx,et al.  Optimizing cognitive task load in naval ship control centres: Design of an adaptive interface , 2006 .

[3]  Kunihide Sasou,et al.  Team errors: definition and taxonomy , 1999 .

[4]  R. Gatchel,et al.  A multiple-response evaluation of EMG biofeedback performance during training and stress-induction conditions. , 1978, Psychophysiology.

[5]  V. Shute Focus on Formative Feedback , 2008 .

[6]  John R. Anderson,et al.  Cognitive Tutors: Lessons Learned , 1995 .

[7]  Mikael Forsman,et al.  Consistency in physiological stress responses and electromyographic activity during induced stress exposure in women and men , 2004, Integrative physiological and behavioral science : the official journal of the Pavlovian Society.

[8]  M. Lambert,et al.  The Effect of a Single Session of Short Duration Biofeedback-Induced Deep Breathing on Measures of Heart Rate Variability During Laboratory-Induced Cognitive Stress: A Pilot Study , 2013, Applied Psychophysiology and Biofeedback.

[9]  Tom Kontogiannis,et al.  Stress and team performance: principles and challenges for intelligent decision aids , 1999 .

[10]  Harald Schaub,et al.  Errors in Planning and Decision‐making and the Nature of Human Information Processing , 1994 .

[11]  Sallie E. Gordon Focusing on the Human Factor in Future Expert Systems , 1988 .

[12]  J. B. Brooke,et al.  SUS: A 'Quick and Dirty' Usability Scale , 1996 .

[13]  P. Kenealy,et al.  Please Scroll down for Article the Quarterly Journal of Experimental Psychology Section a Mood State-dependent Retrieval: the Effects of Induced Mood on Memory Reconsidered , 2022 .

[14]  James E. Driskell,et al.  Stress exposure training. , 1998 .

[15]  J. Greeno Gibson's affordances. , 1994, Psychological review.

[16]  Isabel L. Kampmann,et al.  Controlling Social Stress in Virtual Reality Environments , 2014, PloS one.

[17]  Mark A. Neerincx,et al.  Work content influences on cognitive task load, emotional state and performance during a simulated 520-days' Mars mission , 2016, Comput. Hum. Behav..

[18]  Christopher D. Wickens,et al.  An introduction to human factors engineering , 1997 .

[19]  John-Jules Ch. Meyer,et al.  The design and effect of automated directions during scenario-based training , 2014, Comput. Educ..

[20]  F. Javier Lerch,et al.  Cognitive Support for Real-Time Dynamic Decision Making , 2001, Inf. Syst. Res..

[21]  Anne-Marie Brouwer,et al.  Heart Rate Variability and Skin Conductance Biofeedback: A Triple-Blind Randomized Controlled Study , 2013, 2013 Humaine Association Conference on Affective Computing and Intelligent Interaction.

[22]  Neerincx,et al.  EEG alpha asymmetry, heart rate variability and cortisol in response to virtual reality induced stress , 2011 .

[23]  Michael E. McCauley,et al.  Stress Training Enhances Pilot Performance During a Stressful Flying Task , 2010 .

[24]  Tina Mioch,et al.  The effect of time pressure and task completion on the occurrence of cognitive lockup , 2011 .

[25]  Enrico Ronchi,et al.  Virtual reality for fire evacuation research , 2014, 2014 Federated Conference on Computer Science and Information Systems.

[26]  Raanan Lipshitz,et al.  Why classical decision theory is an inappropriate standard for evaluating and aiding most human decision making. , 1993 .

[27]  I. Rock,et al.  The legacy of Gestalt psychology. , 1990, Scientific American.

[28]  C. Wickens,et al.  An Introduction to Human Factors Engineering Second Edition , 2010 .

[29]  W. Brinkman,et al.  Physiological measures and self-report to evaluate neutral virtual reality worlds , 2011 .

[30]  Mark A. Neerincx,et al.  Modelling environmental and cognitive factors to predict performance in a stressful training scenario on a naval ship simulator , 2015, Cognition, Technology & Work.

[31]  James Reason,et al.  Cognitive Aids in Process Environments: Prostheses or Tools? , 1987, Int. J. Man Mach. Stud..

[32]  Richard E. Clark,et al.  Why Minimal Guidance During Instruction Does Not Work: An Analysis of the Failure of Constructivist, Discovery, Problem-Based, Experiential, and Inquiry-Based Teaching , 2006 .

[33]  A. Mehrabian Pleasure-arousal-dominance: A general framework for describing and measuring individual differences in Temperament , 1996 .

[34]  Erica D. Kuligowski,et al.  A Review of Risk Perception in Building Fire Evacuation , 2014 .

[35]  François Bernier,et al.  Using Biofeedback while Immersed in a Stressful Videogame Increases the Effectiveness of Stress Management Skills in Soldiers , 2012, PloS one.

[36]  Amedeo Cesta,et al.  Training for crisis decision making - An approach based on plan adaptation , 2014, Knowl. Based Syst..

[37]  Yili Liu,et al.  Introduction to Human Factors Engineering (2nd Edition) , 2003 .

[38]  A. K. Blangsted,et al.  The effect of mental stress on heart rate variability and blood pressure during computer work , 2004, European Journal of Applied Physiology.

[39]  I. Cohen Improving trainees’ performances while under stress using real-time feedback , 2015 .

[40]  Lee Andresen,et al.  Experience-based learning , 2020, Understanding Adult Education and Training.

[41]  Mel Slater,et al.  Visual Realism Enhances Realistic Response in an Immersive Virtual Environment , 2009, IEEE Computer Graphics and Applications.

[42]  Mark A. Neerincx,et al.  Assembling a synthetic emotion mediator for quick decision making during acute stress , 2012, ECCE.

[43]  Douglas S. Bell,et al.  Interface design principles for usable decision support: A targeted review of best practices for clinical prescribing interventions , 2012, J. Biomed. Informatics.

[44]  Gary Klein,et al.  Rapid Decision Making on the Fire Ground , 1986 .

[45]  Cleotilde González Decision Support for Real-Time, Dynamic Decision-Making Tasks , 2005 .

[46]  Janet A. Sniezek,et al.  Personality in Extreme Situations: Thinking (or Not) under Acute Stress☆ , 2001 .

[47]  Andruid Kerne,et al.  The Team Coordination Game: Zero-fidelity simulation abstracted from fire emergency response practice , 2011, TCHI.