Computational Model of Self-Efficacy's Various Effects on Performance: Moving the Debate Forward

• 成果类型: Article
• 署名作者: Vancouver, Jeffrey B.; Purl, Justin D.
• 署名单位: University System of Ohio; Ohio University
• 刊物名称: JOURNAL OF APPLIED PSYCHOLOGY
• ISSN/ISSBN: 0021-9010
• DOI: 10.1037/apl0000177
• 发表日期: 2017
• 页码: 599-616
• 关键词: computational models; self-efficacy; motivation; theory integration

摘要

Self-efficacy, which is one's belief in one's capacity, has been found to both positively and negatively influence effort and performance. The reasons for these different effects have been a major topic of debate among social-cognitive and perceptual control theorists. In particular, the findings of various self-efficacy effects has been motivated by a perceptual control theory view of self-regulation that social-cognitive theorists' question. To provide more clarity to the theoretical arguments, a computational model of the multiple processes presumed to create the positive, negative, and null effects for self-efficacy is presented. Building on an existing computational model of goal choice that produces a positive effect for self-efficacy, the current article adds a symbolic processing structure used during goal striving that explains the negative self-efficacy effect observed in recent studies. Moreover, the multiple processes, operating together, allow the model to recreate the various effects found in a published study of feedback ambiguity's moderating role on the self-efficacy to performance relationship (Schmidt & DeShon, 2010). Discussion focuses on the implications of the model for the self-efficacy debate, alternative computational models, the overlap between control theory and social-cognitive theory explanations, the value of using computational models for resolving theoretical disputes, and future research and directions the model inspires.