Effort and its perception revisited: How physical-domain insights could lead toward a unified theory
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Effort influences decisions to initiate and sustain physical and cognitive tasks. Although the perception of effort is central to human behaviour, its underlying mechanisms—especially in the cognitive domain—remain poorly understood. Building on knowledge from physical exertion, this article introduces the concepts of effort and effort perception through a multidisciplinary lens, integrating insights from exercise sciences, (neuro)physiology, and psychology.We begin by highlighting the inconsistent definitions of effort in the literature and propose a transdisciplinary definition: the intentional engagement of physical and cognitive resources to perform—or attempt to perform—a task. We then review methods for measuring effort, emphasizing the current limitations of physiological and performance-based variables. We argue that, when adequately contextualized as a unique perception dissociated from other exercise-related perceptions, the self-report of effort currently provides the most viable way to investigate effort.Next, we explore theoretical models explaining effort perception in physical tasks, focusing on the corollary discharge model as a promising theoretical framework. While this model offers valuable insights, it does not fully account for exerting effort during cognitive tasks. We suggest refining the corollary discharge model to encompass cognitive exertion, thus breaking the traditional silos between the physical and cognitive domains.Finally, we outline key challenges for future research: defining “resources” more clearly, developing reliable measurement tools for effort and its (neuro)physiological correlates, and determining whether effort perception is domain-general or domain-specific. We end by discussing the broad implications of our new account of effort for performance, health, and behavioural science.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it