Shanghai University of Sport, Shanghai, China
After years of a dedicated training life, athletes accumulate a wealth of motor skills and knowledge in the sport to which they belong. This study reviews previous research on movement monitoring to examine the effects of social roles and rewards on athletes' movement monitoring. In the field of cognitive neuroscience, the FRN and OFRN are early components of action outcome monitoring, which are indicators of action outcome monitoring of self and others, while the P300 and OP300 are late components of action outcome monitoring, which are indicators of the emotional and motivational evaluation of action outcomes. The amplitude of the FRN and P300 feedback to oneself and its topography are moderated by social roles and are more responsive to teammates than to opponents. The feedback of reward information has a strong influence on the monitoring of action outcomes, as reward information influences the release of dopamine through the reward loop, which further alters the activation of the ACC, which in turn influences the FRN, thus influencing the monitoring of action outcomes for oneself or others. People can more accurately understand the movements they have mastered, i.e. the movements present in their self-motor representations, than unfamiliar movements. The brain can interpret immediate incoming sensorimotor and contextual information based on its own experience, and anticipate the outcome of others' movements, or understand the intentions implied in the movements.The impact of rewards on outcome monitoring is greater for athletes than for the general population in terms of processing more information about gains and losses than about gains and losses; social roles (self, teammates, opponents) and rewards (morerewards, less rewards, more penalties, less penalties) affect athletes' outcome monitoring, in terms of processing more information about gains and losses of self and teammates than about gains and losses of opponents.
Roles, Opponents, Teammates, Rewards, ERP, FMRI
Kai Zhao. An Exploration of the Role of Social Roles and Rewards in the Monitoring of Athletes' Action Outcome. Academic Journal of Humanities & Social Sciences (2023) Vol. 6, Issue 5: 97-104. https://doi.org/10.25236/AJHSS.2023.060516.
 Rabbitt, & P., M. (1966). Errors and error correction in choice-response tasks. Journal of Experimental Psychology, 71(2), 264.
 Westoff, R. Review: Research Methods in the Behavioral Sciences by Leon Festinger and Daniel Katz[J]. Milbank Memorial Fund Quarterly, 1955, 33(3):322-325.
 Glazer, & Jeremy. (2018). Learning from those who no longer teach: viewing teacher attrition through a resistance lens. Teaching & Teacher Education, 74, 62-71.
 Wang & Ge. (2017). A perspective on deep imaging. IEEE Access, 4, 8914-8924.
 Bellebaum, C., Polezzi, D., & Daum, I. (2010). It is less than you expected: the feedback-related negativity reflects violations of reward magnitude expectations. Neuropsychologia, 48(11), 3343-3350.
 Gentsch, A., Ullsperger, P., & Ullsperger, M. (2009). Dissociable medial frontal negativities from a common monitoring system for self- and externally caused failure of goal achievement. Neuroimage, 47(4), 2023-2030.
 Heldmann, M., Jascha Rüsseler, & Thomas F Münte. (2008). Internal and external information in error processing. BMC Neuroscience, 9(1), 1-8.
 Holroyd, Clay, B., Coles, Michael, & G., H. (2002). The neural basis of human error processing: reinforcement learning, dopamine, and the error-related negativity. Psychological Review, 109(4), 679-679.
 Matthew, M., Botvinick, Todd, & Braver. (2015). Motivation and cognitive control: insights from cognitive neuroscience. Annual Review of Psychology.
 Botvinick, M., & Braver, T. (2014). Motivation and cognitive control: from behavior to neural mechanism. Annual Review of Psychology, 66(1), 83-113.
 Luiz, & Pessoa. (2011). Reprint of: emotion and cognition and the amygdala: from "what is it?" to "what's to be done?" Neuropsychologia.
 Boehler, C. N., Hopf, J. M., Stoppel, C. M., & Krebs, R. M. (2012). Motivating inhibition – reward prospect speeds up response cancellation. Cognition, 125(3), 498-503.
 Boehler, C. N., Schevernels, H., Hopf, J. M., Stoppel, C. M., & Krebs, R. M. (2014). Reward prospect rapidly speeds up response inhibition via reactive control. Cognitive Affective & Behavioral Neuroscience, 14(2), 593-609.
 Krebs, Ruth, M., Boehler, Carsten, & N., et al. (2015). Neural conflict-control mechanisms improve memory for target stimuli. Cerebral Cortex.
 Woldorff, M. G. (2010). Distortion of erp averages due to overlap from temporally adjacent erps: analysis and correction. Psychophysiology, 30(1), 98-119.
 Nick, Yeung, Matthew, M, Botvinick, & Jonathan, et al. (2004). The neural basis of error detection: conflict monitoring and the error-related negativity. Psychological Review.
 Xuguang, Liu, Dr., R., Christopher, & Miall, et al. (1997). Analysis of action tremor and impaired control of movement velocity in multiple sclerosis during visually guided wrist-tracking tasks. Movement Disorders, 12(6), 992-999.
 Wolpert, D. M., Ghahramani, Z., & Flanagan, J. R. (2001). Perspectives and problems in motor learning. Trends in Cognitive Sciences, 5(11), 487-494.
 P., M., Glazer, and, R., Bindra, & and, et al. (2004). Down-regulation of rad51 and decreased homologous recombination in hypoxic cancer cells. International Journal of Radiation Oncology* Biology*Physics.
 Yu, Qianchun, Dong, Wenwen, Chen, Jianjun, Chen, Guimei, Wang, Huafeng, & Wang, Kai. (2012). Application of attentional network test to study the effect of table tennis on attentional improvement. Journal of Tianjin Institute of Physical Education (03), 219-223.
 Van Veen, V., & Carter, C. S. (2002). The timing of action-monitoring processes in the anterior cingulate cortex. Journal of cognitive neuroscience, 14(4), 593–602.
 Falkenstein, M., Hohnsbein, J., Hoormann, J., & Blanke, L. (1990). Effects of errors in choice reaction tasks on the ERP under focused and devided attention.
 Falkenstein, M., Hoormann, J., Christ, S., & Hohnsbein, J. (2000). Erp components on reaction errors and their functional significance: a tutorial. Biological Psychology, 51(2-3), 87-107.
 Dewei, Zeng, Shuqiang, Wang, Yanyan, & Shen, et al. (2017). A ga-based feature selection and parameter optimization for support tucker machine. Procedia Computer Science.
 Huang, G. B., Zhou, H., Ding, X., & Rui, Z. (2012). Extreme learning machine for regression and multiclass classification. IEEE Transactions on Systems Man & Cybernetics Part B, 42(2), 513-529.
 Sander, Nieuwenhuis, K., Richard, Ridderinkhof, & Durk, et al. (2002). A computational account of altered error processing in older age: dopamine and the error-related negativity. Cognitive Affective & Behavioral Neuroscience.
 Zhang Hui, Dai Jinbiao, Shi Fuying, et al. Technical and tactical characteristics of spaced-net confrontation (racket-holding) [J]. Journal of Shanghai Sports Institute, 2007, 31(4): 65-69.
 Botvinick M M, Braver T S, Barch D M, et al. Conflict monitoring and cognitive control. Psychol Rev, 2001, 108: 624-6526.
 Botvinick, Matthew, M., Carter, Cameron, S., Braver, & Todd, S., et al. (2001). Conflict monitoring and cognitive control. Psychological Review.
 Yeung, N., Botvinick, M., & Cohen, J. (2004). The neural basis of error detection: conflict monitoring and the error-related negativity. Psychological Review, 111(4), 931-59.
 Uzzell, G. A., Beatty, P. J., Paquette, N. A., Roberts, D. M., & Mcdonald, C. G. (2017). Error-induced blindness: error detection leads to impaired sensory processing and lower accuracy at short response-stimulus intervals. Journal of Neuroscience, 37(11), 1202-1216.
 Hajcak, G., Holroyd, C., Moser, J. S., & Simons, R. F. (2004). What is a bad outcome? The effect of value and experimental context on the feedback negativity. 44th Annual Meeting of the Society-for-Psychophysiological-Research.
 Notebaert, K, Nelis, S, Reynvoet, & B. (2011). The magnitude representation of small and large symbolic numbers in the left and right hemisphere: an event-related fmri study. Journal of Cognitive Neuroscience.
 Jan P. Gläscher, O'Doherty J P. Model‐based approaches to neuroimaging: combining reinforcement learning theory with fMRI data [J]. Wiley Interdisciplinary Reviews Cognitive Science, 2010, 1(4): 501-510.
 Pierce, L., Krigolson, O., Tanaka, J., & Holroyd, C. (2007). The ern and reinforcement learning in a difficult perceptual expertise task. Canadian Journal of Experimental Psychology Revue Canadienne De Psychologie Experimentale, 61(4), 372-372.
 Holroyd, Clay, B., HajiHosseini, & Azadeh. (2015). Sensitivity of frontal beta oscillations to reward valence but not probability. Neuroscience Letters: An International Multidisciplinary Journal Devoted to the Rapid Publication of Basic Research in the Brain Sciences, 602, 99-103.
 Liao, Yan Gang, & Ge, Chun Lin. (2003). A study of athletes' attention - A multilevel model analysis. Journal of Xi'an Sports College (06), 114-116.
 Ridderinkhof, R. K. (2002). Micro- and macro-adjustments of task set: activation and suppression in conflict tasks. Psychological Research, 66(4), 312-323.
 Brevers, D., Dubuisson, E., Dejonghe, F., Dutrieux, J., Petieau, M., & Cheron, G., et al. (2018). Proactive and reactive motor inhibition in top athletes versus nonathletes. Perceptual & Motor Skills, 31512517751751.
 Robbins, T. W. (2011). Dissociating inhibition, attention, and response control in the frontoparietal network using functional magnetic resonance imaging. cerebral Cortex, 21(5), 1155-1165.
 Kang, S. K., Hirsh, J. B., & Chasteen, A. L. (2010). Your mistakes are mine: self-other overlap predicts neural response to observed errors. Journal of Experimental Social Psychology, 46(1), 229-232.
 Stad, F. E., Van, H., Wiedl, K. H., & Resing, W. (2018). Predicting school achievement: differential effects of dynamic testing measures and cognitive flexibility for math performance. Learning and Individual Differences, 67, 117-125.
 Michael.J. Wright, Daniel. T. Bishop, Robin. C. Jackson & Bruce Abernethy. (2013). Brain regions concerned with the identification of deceptive soccer moves by higher-skilled and lower-skilled players. Frontiers in Human Neuroscience, 7, 1-15.
 Yang, A.H., & Yin, S.C. (2009). A study of event-related potentials (erp) in spatial attentional characteristics of table tennis players. Sports Science, 029(004), 35-43.
 Cavanagh, J. F., Wiecki, T. V., Cohen, M. X., Figueroa, C. M., Samanta, J., & Sherman, S. J., et al. (2011). Subthalamic nucleus stimulation reverses.
 Debener, S., Ullsperger, M., Siegel, M., Fiehler, K., Cramon, D. V., & Engel, A. K. (2005). Trial-by-trial coupling of concurrent electroencephalogram and functional magnetic resonance imaging identifies the dynamics of performance monitoring. The Journal of Neuroscience, 25(50).
 Hampshire, A., Chamberlain, S. R., Monti, M. M., Duncan, J., & Owen, A. M. (2010). The role of the right inferior frontal gyrus: inhibition and attentional control. Neuroimage, 50(3), 1313-1319.
 Nieuwenhuis, S., Ridderinkhof, K. R., Blom, J., Band, G., & Kok, A. (2010). Error-related brain potentials are differentially related to awareness of response errors: evidence from an antisaccade task. Psychophysiology, 38 (5), 752-760.
 Endrass, T., Reuter, B., & Kathmann, N. (2007). Erp correlates of conscious error recognition: aware and unaware errors in an antisaccade task. European Journal of Neuroscience, 26(6).
 Jentzsch, I., & Dudschig, C. (2009). Why do we slow down after an error? Mechanisms underlying the effects of post-error slowing. Quarterly Journal of Experimental Psychology, 62(2), 209-218.
 PurCeLl, B., & Kiani, R. (2016). Neural mechanisms of post-error adjustments of decision policy in parietal cortex. Neuron, 89(3), 658-671.
 Wessel, J. R., Danielmeier, C., & Ullsperger, M. (2011). Error awareness revisited: accumulation of multimodal evidence from central and autonomic nervous systems. J Cogn Neurosci, 23(10), 3021-3036.