Effort and its perception revisited: How physical-domain insights could lead toward a unified theory
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.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.022 |
| Scholarly communication | 0.008 | 0.015 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".