Want It Like You Mean It: Revisiting Goal Self-Concordance Through the Dissociation of Autonomous and Controlled Motivation in Relation to Meaning in Life

Engineering notes

Key topics: autonomous driving, control. See the paper for implementation details and experimental results.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original abstract

Abstract The Self-Concordance Model posits that autonomously motivated goal pursuit is a primary driver of well-being, yet standard measurement practices often conflate the presence of autonomy with the absence of external pressure via relative difference scores. Using a high-powered dataset ( N = 429), this study tested a Balance Model against an Autonomous-Primary Model. Using Bayesian structural equation modeling and directed acyclic graphs, the analyses indicated that the goal self-concordance difference score can obscure whether associations with meaning are driven by autonomous motivation, reduced controlled motivation, or both. Autonomous motivation was the sole robust motivational predictor of meaning in life judgments, whereas the goal self-concordance score contributed no unique variance when both were entered simultaneously. A functional dissociation emerged in which controlled motivation robustly predicted distress symptoms, while showing no unique association with meaning once distress covariates were included. The latent correlation between autonomous and controlled motivation was near zero ( r = − .09), consistent with their status as largely orthogonal constructs rather than opposite ends of a continuum. There was no evidence that controlled motivation moderated the association between autonomous motication and meaning. In this sample, the findings challenge the subtractive logic embedded in traditional self-concordance measurement and suggest that meaning in life is associated with autonomous direction rather than motivational balance.

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