Scholarlink

<p>Computational modelling is a powerful tool to specify psychological theories and conduct model-based empirical research. Yet it has seen little use in Self-Determination Theory (SDT), one of the most successful theories of human motivation. Here, we use two basic SDT constructs, competence and optimal challenge, to demonstrate how computational modelling can benefit theory building and practical application for SDT. Drawing on conceptual analysis and a toy model, we identify three plausible intensional facets of verbal competence definitions that unevenly align with operationalisations and propositions on optimal challenge. We then show how computational modelling, inspired by the AI field of computational intrinsic motivation, can help inform the refinement of these and other constructs, provide point-precise predictions, complement cognition-level mechanistic accounts of competence, refine practical guidance, and support implementation in digital task and goal-setting applications.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Advancing Self-Determination Theory via computational modelling: the case of competence and optimal challenge

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年9月26日

查看全文
  • Sebastian Deterding,
  • Christian Guckelsberger,
  • Erik M. Lintunen,
  • Nadia M. Ady

摘要

Computational modelling is a powerful tool to specify psychological theories and conduct model-based empirical research. Yet it has seen little use in Self-Determination Theory (SDT), one of the most successful theories of human motivation. Here, we use two basic SDT constructs, competence and optimal challenge, to demonstrate how computational modelling can benefit theory building and practical application for SDT. Drawing on conceptual analysis and a toy model, we identify three plausible intensional facets of verbal competence definitions that unevenly align with operationalisations and propositions on optimal challenge. We then show how computational modelling, inspired by the AI field of computational intrinsic motivation, can help inform the refinement of these and other constructs, provide point-precise predictions, complement cognition-level mechanistic accounts of competence, refine practical guidance, and support implementation in digital task and goal-setting applications.