In this issue, Rogers et al1 present the results of a study looking at the interplay of emotions and learning during simulation-based education. The role of emotions in learning, particularly simulation, has been of growing interest to educators. Medical education involves significant emotional experiences for the learners, and these emotions can have important effects on the cognitive and learning processes of individuals. Given that stress has been associated not only with greater memory consolidation (process of solidifying transient memory traces into long-term memory) but also with greater biases and inaccuracies in what is recalled, simulation educators are increasingly concerned with understanding and quantifying stress for learners during simulations.2 The goal of this editorial is to highlight possible directions for future research building on the work of Rogers et al.1 I first present arguments for looking at the role of appraisals as well as motivation when studying emotions. Second, I discuss the potential importance of individual roles as potential modifiers of emotional responses during team-based simulation sessions. Finally, I discuss the possible benefits of physiological and behavioral measures in the study of emotions.
Addressing the concerns of educators regarding stress reactions during simulation-based education, Rogers et al1 investigated whether participants in simulations had greater emotional responses than observers. As a secondary outcome measure, they examined the relationship between emotional arousal and learning. They found that participants, as expected, reported greater negative arousal than the observers. However, they also found that the participants reported greater positive arousal than the observers, a finding that is more unexpected. Finally, they did not find any relationship between emotional arousal or role (participants vs observer) and scores on a measure of learning.
This study represents the early stages of an important research agenda for the field of simulation-based education: how does the construction of our simulation sessions affect the emotional experience of our learners, and how do these emotional experiences affect their learning? As discussed by Rogers et al,1 the results of this study raise more questions than answers, as would be expected from any early inquiry into a topic. Although readers might be tempted to interpret these results as indicating that emotions do not influence learning, there are other factors potentially at play that could influence the results. The relationship between types of simulation experiences, emotional experiences, and subsequent learning is a complex one that merits further inquiry. Hereinafter, I describe some possible venues for future inquiry.
As highlighted by Rogers et al,1 participants in their simulation sessions experienced the co-existence of greater negative and positive emotions compared with the observers. This supports findings from the broader emotions domain that emotions can co-exist, with each emotion having a unique—and sometimes opposing effect - on cognitive processes.3 In their study, Rogers et al1 use the circumplex model of emotions4 to frame the emotional reactions of the learners. A commonly used model, the circumplex model's strength is that it parses out various emotions into two dimensions thought to be at the core of an emotional experience: arousal and valence. Dimensional models of emotion describe affective states as arising from overlapping neurophysiological systems thought to act in predictable ways on cognitive processes. Over the years, there has been some evidence in support of dimensional models of emotions.5–7
However, dimensional models such as the circumplex model have also been critiqued for being too simplistic to fully capture the complex relationship between emotions and cognitive processes.8 To better characterize the relationship between emotions and cognition, discrete models of emotions have been put forward. Distinct emotions are thought to organize behavior and physiology in a predictable fashion to allow the individual to deal with specific emotion-evoking events.9 In contradiction to dimensional models, discrete approaches argue that emotions of the same valence and arousal can have different effects on cognition. For example, fear and anger, though both negatively valenced high arousal emotions, differ in terms of certainty and power appraisals. Fear is associated with appraisals of low certainty and low power over a situation. Anger is associated with high certainty and high power. As a result of these appraisals, anger is associated with more optimistic assessments of the future and lower risk perceptions than is fear.10–12 Similar research has shown that sadness (eg, negative-low arousal) is associated with more pessimistic assessments of the future, but less aversion to taking risks.12 These data suggest that even when emotions overlap in valence and/or arousal, they can have significantly different effects on how individuals think and act.
In addition, emotions can have an impact on a learner's motivation and efforts toward understanding of educational materials, that is, their preparation, perseverance in the face of challenges, and strategies toward learning.13 According to Pekrun's control-value theory of emotions,14 achievement emotions are emotions directly tied to achievement activities (emotions experienced during an activity) and achievement outcomes (emotions experienced as a result of success or failure outcomes). Different emotions will affect one's motivation toward learning behaviors.
Positive emotions, such as enjoying a task, can lead to greater interest and greater intrinsic motivation to engage in the task for its own sake.15,16 Negative emotions (eg, boredom, anxiety, anger) can decrease interest and intrinsic motivation in a task. However, negative emotions can also increase extrinsic motivation (motivation to engage in a task as a means to an end).17 For example, the fear of performing badly in front of colleagues may result in greater extrinsic motivation, thereby motivating learners to engage in behaviors to enhance their learning. As such, both positive and negative emotions can enhance motivation to learn and, as a result, subsequent performance.14 These effects may be different for specific emotions. For example, Zhao18 observed that fear did not have any significant impact on motivation to learn but had a direct negative effect on learning itself. In contrast, guilt and sadness were positively associated with motivation to learn but had no direct effects on learning from errors. Another study suggests that transient shame can lead to greater attention to feedback.19 In contrast, deactivating positive emotions, such as relief, can have a detrimental effect on learning motivation and behaviors.20 In a study of medical students' learning with a virtual patient simulation program, relief was negatively associated with attention to feedback.19 Together, these studies suggest that future studies looking at the relationship between emotions and learning from simulation sessions would benefit from considering the distinct emotions experienced by learners.
Another aspect that merits further study would be to look at the effects of specific roles while participating in simulations. In their study, Rogers et al1 describe sessions in which 2 to 3 residents actively participated in the same scenario. In such cases, one individual would likely assume the role of leader with the others taking a more supporting role. As such, the emotional responses of all active participants might not be equivalent. In previous research by our group, we found that different roles were associated with different stress responses.21 Therefore, further work could look at the effect of different roles on participants' emotions as well as on subsequent learning.
This study also looked at self-reported emotions. This is a common approach, given financial and logistical challenges of measuring physiological stress responses (eg, cortisol and heart rate variability). As addressed by Rogers et al,1 this is also a potentially contentious approach to measuring emotions. Although the collection of cortisol samples often requires constraints that do not accommodate well with simulation-based education sessions,22 recent development in the measurement of heart rate variability has made data collection more straightforward and affordable.23 Where possible, adding physiological measures to subsequent studies looking at stress responses would allow for greater understanding of the relative contributions of subjective (“feeling” stressed) and physiological responses. Although the physiological measurement of other distinct emotions is not as reliable, great advances have been done in facial and behavioral analyses.24
In conclusion, Rogers et al,1 in their study, begin to address an important question for simulation-based education. By further understanding the emotional responses of our learners to simulation sessions, and the effects of these various emotions on learning, educators will be better equipped to create simulation-based curricula that will best prepare learners for future clinical practice.
1. Rogers T, Andler C, O'Brien B, van Schaik S. Self-reported emotions in simulation-based learning: active participants vs observers. Simul Healthc
2. LeBlanc VR. The effects of acute stress on performance: implications for health professions education. Acad Med
3. LeBlanc VR, McConnell MM, Monteiro SD. Predictable chaos: a review of the effects of emotions on attention, memory and decision making. Adv Health Sci Educ
4. Posner J, Russell JA, Peterson BS. The circumplex model of affect: an integrative approach to affective neuroscience, cognitive development, and psychopathology. Dev Psychopathol
5. Friedman BH. Feelings and the body: the Jamesian perspective on autonomic specificity of emotion. Biol Psychol
6. Ford JH, Addis DR, Giovanello KS. Differential effects of arousal in positive and negative autobiographical memories. Memory
7. Kensinger EA, Corkin S. Two routes to emotional memory: distinct neural processes for valence and arousal. Proceedings of the National Academy of Sciences
8. Remington NA, Fabrigar LR, Visser PS. Reexamining the circumplex model of affect. J Pers Soc Psychol
9. Barrett LF. Are emotions natural kinds? Perspect Psychol Sci
10. Lerner JS, Keltner D. Beyond valence: toward a model of emotion-specific influences on judgement and choice. Cognit Emot
11. Lerner JS, Gonzalez RM, Small DA, Fischhoff B. Effects for fear and anger on perceived risks of terrorism: a national field experiment. Psychol Sci
12. Raghunathan R, Pham MT. All negative moods are not equal: motivational influences of anxiety and sadness on decision making. Organ Behav Hum Decis Processes
13. Artino AR, Holmboe ES, Durning SJ. Can achievement emotions be used to better understand motivation, learning, and performance in medical education? Med Teach
14. Pekrun R. The control-value theory of achievement emotions: assumptions, corollaries, and implications for educational research and practice. Educ Psychol Rev
15. Ryan RM, Deci EL. Intrinsic and extrinsic motivations: classic definitions and new directions. Contemp Educ Psychol
16. McConnell MM, Eva KW. The role of emotion in the learning and transfer of clinical skills and knowledge. Acad Med
17. Schunk DH, Pintrich PR, Meece JL. Motivation in education: theory, research, and applications
. 3rd ed. Upper Saddle River, NJ: Pearson Education, Inc; 2008.
18. Zhao B. Learning from errors: The role of context, emotion, and personality. J Organ Behav
19. Naismith LM, Lajoie SP. Motivation and emotion predict medical students' attention to computer-based feedback. Adv Health Sci Educ
20. Artino AR Jr, Holmboe ES, Durning SJ. Control-value theory: using achievement emotions to improve understanding of motivation, learning, and performance in medical education: AMEE Guide No. 64. Med Teach
21. Sørensen JL, Van der Vleuten C, Rosthøj S, Ottesen B, Østergaard D, Johansen M, Ekelund K, Albrechtsen CK, Pedersen BW, LeBlanc V: Differing stress responses by healthcare professionals during simulated medical emergencies. Poster presented at the 41st Nordic Congress of Obstetrics and Gynecology. Odense, Denmark; June 12, 2018.
22. Hellhammer DH, Wüst S, Kudielka BM. Salivary cortisol as a biomarker in stress research. Psychoneuroendocrinology
23. Dobbs WC, Fedewa MV, MacDonald HV, et al. The accuracy of acquiring heart rate variability from portable devices: a systematic review and meta-analysis. Sports Med
24. Duffy MC, Lajoie S, Lachapelle K. Measuring emotions in medical education: methodological and technological advances within authentic medical learning environments. In: Educational Technologies in Medical and Health Sciences Education
. Cham: Springer; 2016:181–213.