In this section
@article{Deterding:2025:10.1007/s11031-025-10156-8,
author = {Deterding, S and Guckelsberger, C and Lintunen, EM and Ady, NM},
doi = {10.1007/s11031-025-10156-8},
journal = {Motivation and Emotion},
title = {Advancing Self-Determination Theory via computational modelling: the case of competence and optimal challenge},
url = {http://dx.doi.org/10.1007/s11031-025-10156-8},
year = {2025}
}
TY - JOUR
AB - 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.
AU - Deterding,S
AU - Guckelsberger,C
AU - Lintunen,EM
AU - Ady,NM
DO - 10.1007/s11031-025-10156-8
PY - 2025///
SN - 0146-7239
TI - Advancing Self-Determination Theory via computational modelling: the case of competence and optimal challenge
T2 - Motivation and Emotion
UR - http://dx.doi.org/10.1007/s11031-025-10156-8
UR - https://link.springer.com/article/10.1007/s11031-025-10156-8
ER -
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