Effects of Biofeedback and Imagery on Learning in a Competitive Environment Steven J. Radlo Introduction Imagery can be defined as “using all the senses to re-create or create an experience in the mind” (Vealey & Greenleaf, 2001, p. 248). Imagery has been shown to be effective for learning and enhancing motor skills (Martin, Moritz, & Hall, 1999; Murphy & Martin, 2002). The potency of imagery becomes evident when all senses are employed, the image is constructed as vivid and detailed as possible, and the image is controlled so that manipulation of the object can occur at freewill. Imagery has many functional purposes: cognitive (strategy planning, recall of declarative and procedural knowledge, organizing and rehearsing thoughts and action plans); behavioral (increase in motivation, confidence, attentional focus, and aids in achieving an optimal arousal level); neurophysiological (stimulates visuomotor brain regions and primes neural pathways for action). Imagery can be performed actively or passively. Active imagery involves imaging internally while being engaged in the environment and equipment, but not actually performing the task (e.g., standing at the free throw line and holding a basketball). Passive imagery entails imaging internally absent from the learning environment and equipment (e.g., imaging a free throw shot in the locker room). The use of biofeedback has been shown to be an effective method for monitoring and regulating arousal and concentration (Petruzzello, Landers, & Salazar, 1991; Zaichkowsky & Fuchs, 1988). The ability to demonstrate to the learner his or her stress/tension and concentration levels before and during performance is extremely enlightening and very important, practical information. Essentially, this is the role of biofeedback. One such biofeedback instrument is The Peak Achievement Trainer (PAT). The PAT is used to provide instantaneous easy-to-interpret information that can be used by performers as they freely engage in their activity. A unique feature of the PAT is its ability to dissect attention into two sub-components: arousal and concentration. Relaxing more intensely inhibits or lowers the amplitude of this signal. The signal is filtered online and thus allows the learner to see and hear when the amplitude of the signal is approaching an optimal level of arousal. Additionally, another graph on the computer screen will instantaneously show if the person is concentrating optimally (see accompanying picture). Often, students learning new motor skills or information in physical education classes are required to demonstrate achievement in tests or competitions. During times such as these, it is not uncommon to see a learner “choke” under pressure and produce performance not truly indicative of what they are capable of. Learning how to become optimally ready to perform and effectively cope with stress are important aspects in the development of skilled performance of cognitive and motor tasks. During stressful times (e.g., competition), the release of extraneous catecholamine and stress hormones can lead to a loss in concentration and attentional focus, memory lapses, and high arousal levels (Orlick, 1986; Rotella & Lerner, 1993; Zaichkowsky & Takenaka, 1993). To date, research examining the influence of EEG biofeedback and imagery has been scant. Furthermore, little research has compared the effects of active versus passive imagery while learning a closed motor task. The purpose of this study was to investigate the effectiveness of active and passive imagery and the use of EEG biofeedback on skill acquisition during competition. The task participants attempted to learn was the mirror-star trace. Stress was induced by using monetary incentives and head-to-head competition. It was hypothesized that participants using active imagery and biofeedback would trace more segments of the star, be able to increase their focused concentration scores, and show a decrease in their arousal level as compared to the passive imagers and the control group. MethodParticipants University students (N= 30) volunteered to participate in the study. All participants were asked to read and sign an informed consent during the first day. After ten 15 minute sessions of biofeedback (one session per day), participants were randomly divided into either an active imagery (AI), passive imagery (PI), or control group (C). Three more biofeedback sessions were conducted, but this time with the addition of imagery. The AI group imaged tracing the star while holding the stylus on the starting point. The PI group imaged the task in a different room without being able to view the apparatus. The C group received 3 sessions of watching an unrelated video. Motor Task The mirror-star trace was the task used to determine the effectiveness of the imagery treatment and the biofeedback. Having participants perform this type of novel task ensured that all participants were equal in skill level. Participants were asked to use the reflection of the star in the mirror to trace as many segments of the star as possible. If the stylus went outside of the star’s border, a click was made by an error counter to provide feedback to the participant. For scoring purposes, one segment of the star was subtracted for every 10 times the stylus strayed from the star’s border.Procedures On the day of testing (post-test), three participants (one from each group) were led to the Human Performance Laboratory at Western Illinois University. The participants were told that they would be competing against each other. The participants were informed that if their score was better than their opponents, then they would win $10, to be paid that day. Participants then put the PAT headwear on. Biofeedback was not provided to the participants but was used for data collection purposes. Participants completed 10 trials of tracing the star, each trial lasting 20 seconds. A 30 second rest period was provided between trials. Number of segments completed was recorded, as well as the number of errors made. Testing time was approximately 30 min. Once testing was completed, the participants were debriefed about the experiment and any questions they may have had were answered. References Anshel, M.H. (2003). Sport psychology: From theory to practice. San Francisco, CA: Benjamin Cummings. Cox, R.H. (2002). Sport psychology: Concepts and applications. New York: McGraw-Hill. Feltz, D.L., & Landers, D.M. (1983). The effect of mental practice on motor-skill learning and performance: A meta-analysis. Journal of Sport Psychology, 2, Fitts, P.M., & Posner, M.I. (1967). Human Performance. Belmont, CA: Brooks/Cole. Hinshaw, K.E. (1991). The effects of mental practice on motor skill performance: Critical evaluation and meta-analysis. Imagination, Cognition and Personality, 11, Magill, R.A. (2007). Motor learning and control: Concepts and applications. New York: McGraw-Hill. Martin, K.A., Moritz, S.E., & Hall, C.R. (1999). Imagery use in sport: A literature review and applied model. Sport Psychologist, 13, Maxwell, J.P., Masters, R.W., & Eves, F.F. (2000). From novice to know-how: A longitudinal study of implicit motor learning. Journal of Sport Sciences, 18, Morris, T., Spittle, M., & Watt, A.P. (2005). Imagery in sport. Champaign, IL: Human Kinetics. Murphy, S.M., & Martin, K.A. (2002). The use of imagery in sport. In T.S. Horn (Ed.), Advances in sport psychology, 2 nd ed. (pp ). Champaign, IL: Human Kinetics. Orlick, T.(1986). Psyching for sport. Champaign, IL: Leisure Press. Pargman, D. (2006). Managing performance stress: Models and methods. New York: Routledge. Petruzzello, S.J., Landers, D.M., & Salazar, W. (1991). Biofeedback and sport/exercise performance: Applications and limitations. Behavior Therapy, 22, Rotella, R.J., & Lerner, J.D. (1993). Responding to competitive pressure. In R.N. Singer, M. Murphey, & L.K. Tennant (Eds.), Handbook of research on sport psychology (pp ). New York: Macmillan. Vealey, R.S., & Greenleaf, C.A. (2001). Seeing is believing: Understanding and using imagery in sport. In J.M. Williams (Ed.), Applied sport psychology: Personal growth to peak performance, 4 th ed., (pp ). Weinberg, R.S., & Gould, D. (2003). Foundations of sport & exercise psychology, 3 rd ed. Champaign, IL: Human Kinetics. Zaichkowsky, L.D., & Fuchs, C.Z. (1988). Biofeedback applications in exercise and athletic performance. Exercise and Sport Sciences Reviews, 16, Zaichkowsky, L.D., & Takenaka, K. (1993). Optimizing arousal levels. In R.N. Singer, M. Murphey, & L.K. Tennant (Eds.), Handbook of research on sport psychology (pp ). New York: Macmillan. Results Performance Scores A 3 x 2 (Groups x Test) ANOVA for performance (# of segments) revealed a significant interaction, F(2,27) = 9.53, p =.001,  2 =.41. A simple effects post hoc test showed that the AI group significantly improved from pretest to post-test, as opposed to the PI and C groups. The interaction is shown in Figure 1.Concentration A 3 x 2 (Groups x Test) ANOVA for concentration showed a significant interaction, F(2,27) = 18.78, p <.001,  2 =.58. A simple effects post hoc test revealed that the AI group significantly improved their concentration skills from pretest to post-test, as opposed to the PI and C groups. The interaction is shown in Figure 2.Arousal A 3 x 2 (Groups x Test) ANOVA for arousal indicated a significant interaction, F(2,27) = 3.64, p =.04,  2 =.21. A simple effects post hoc test showed that the AI and PI groups were able to significantly decrease their arousal levels as opposed to the C group. The interaction is shown in Figure 3. Total Time in Optimal Arousal and Concentration A 3 x 2 (Groups x Test) ANOVA for total time (in seconds) in optimal arousal and concentration for all testing trials showed a significant interaction, F(2,27) = 34.89, p <.001,  2 =.72. A simple effects post hoc test revealed that the AI group significantly increased their time within the optimal arousal/concentration zone, as compared to the PI and C groups. The interaction is shown in Figure 4. Sustained Time in Optimal Arousal and Concentration A 3 x 2 (Groups x Test) ANOVA for total time (in seconds) in optimal arousal and concentration for individual trials showed a significant interaction, F(2,27) = 13.51, p <.001,  2 =.50. A simple effects post hoc test indicated that the AI group significantly increased their ability to sustain optimal arousal/concentration time for each trial. The interaction is shown in Figure 5. Discussion Overall, findings suggest that individuals learning a self-paced motor task during competitive situations will experience performance benefits when using active imagery and EEG biofeedback. Most textbook chapters (e.g., Anshel, 2003; Cox, 2002; Magill, 2007; Pargman, 2006; Weinberg & Gould, 2003), research articles (see Feltz & Landers, 1983; Hinshaw, 1991), and books solely dealing with the topic of imagery (e.g., Morris, Spittle, & Watt, 2005), will hedge toward the idea that internal imagery is more beneficial than external imagery, that imagery can have motivational power, increase or decrease arousal, and cognitively prepare the performer to achieve. But one dimension of imagery that is overlooked is the relatively simple question of whether active imagery is more beneficial for a learner rather than passive imagery. Active imagery allows individuals to experience mental images in a much more practical and constructive manner. Oftentimes, athletes rely on imaging during their preperformance routine. Examples include a basketball player imaging a perfect shot immediately before he or she makes a free throw attempt, or a golfer standing over the ball and imaging his or her ball traveling along the contour of the green before depositing itself in the hole. Of course, when athletes don’t have a chance to engage in their sporting environment, then imaging in a passive manner inside the locker room or along the sidelines can aid in enhancing performance too. By actively imaging, it seems intuitive that psychoneuromuscular facilitation, or neuronal tuning, would be triggered with more intensity than if an imager was simply lying on their back and imaging a sport scene. Psychoneuromuscular theorists would suggest that active imaging would facilitate the rate at which the performer activates mental nodes representing the desired motor behavior and therefore create a greater efferent flow. Furthermore, symbolic learning theorists would propose that having a performer image their task-to- be-learned in an active manner creates a stronger connection between perception and action, thus allowing symbolic memory codes to be translated into motor codes in a more efficient and expedited manner for action. Along with the imagery treatments, participants were trained using the PAT biofeedback instrument. Biofeedback demonstrates to the learner his or her current stress, tension, and concentration levels before and during performance. These processes are typically subconscious, thus performers are able to recognize when their arousal levels are too high or when concentration is not at a premium. Through weekly sessions of PAT training, participants were able to recognize, understand, and consequently moderate their arousal and concentration skills. Biofeedback sessions were complemented with such cognitive and behavioral techniques as diaphragmatic breathing, attentional focus practice, autogenic training, progressive muscular relaxation, cognitive restructuring, and positive self-talk. Because no significant differences were found during the pretest phase, and because all participants were able to improve their arousal and concentration scores during biofeedback practice, it can be concluded that the biofeedback appeared to help all three treatment groups. What discerned the groups was the comparison of performance and psychophysiological measures after the groups received their three sessions of imagery. Clearly, the group that received active imagery performed with greater proficiency and with psychophysiological scores indicative of a performer who is focused and not too overly aroused. Because participants performed in a competitive-like situation, not being able to optimally focus or being too over aroused may lead to sub-par performance. Learning theorists propose that this is so because stress, coupled with the early stages of learning, decreases attentional capacity and focus, while at the same time increasing nervousness and memory lapses (Fitts & Posner, 1967; Maxwell, Masters & Eves, 2000). In summary, results showed that combining active imagery with EEG biofeedback appeared to enhance the learning of a self-paced motor skill. Active imagers were able to increase their performance output, lower their arousal level, and increase their focused attention, as compared to the passive imagers and the control group. Overall, findings suggest that biofeedback was beneficial for all groups, but that imaging in an active manner appeared to be most beneficial for learning a perceptual-motor task, as compared to passive imaging or no imaging at all. Findings indirectly support psychoneuromuscular and symbolic learning explanations of imagery and motor learning.