More effort, more variability, but greater relative precision: Distributional modeling of effort-performance coupling

Resumen de los autores

Ratings of perceived exertion (RPE) are widely used to prescribe exercise intensities. When used this way, researchers and practitioners may implicitly or explicitly rely on assumptions about the coupling between perceived effort and performance. These assumptions refer to two dimensions: the average shape of how effort translates to performance and the variability of performance across effort levels. Straightforwardly, effort-performance coupling can be thought of as linear, with constant variability across the intensity spectrum. However, classic psychophysical scaling laws (e.g., Fechner’s law) and motor control research (e.g., signal-dependent noise) predict that such coupling is non-linear, with intensity-contingent increases in variability. These assumptions are rarely made explicit and have not been jointly tested. To address this, we fitted and compared theory-driven Bayesian multilevel models reflecting distinct assumptions about the mean and the variability structure, using leave-one-out cross-validation for model selection. Data were collected from 101 climbers performing an isometric handgrip task with effort prescribed using a RPE 1-10 scale. We found robust evidence for a heteroscedastic variability structure, with a systematic increase in absolute force variability with prescribed RPE. Additionally, we found modest evidence favoring an exponential mean shape over a linear mean shape, suggesting that force increases disproportionately with effort. These effects were gender unspecific and are broadly consistent with Fechner’s psychophysical law and signal-dependent noise theory. Together, these findings suggest that a direct translation from effort to performance systematically misrepresents the actual relationship. Instead, practitioners and researchers should expect a disproportionate increase in performance with increasing absolute performance variability.

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Cómo citar (APA 7)

Hack, L., Rewitz, K., Wagner, S., & Wolff, W. (2026). More effort, more variability, but greater relative precision: Distributional modeling of effort-performance coupling. https://doi.org/10.31234/osf.io/34pjh_v2