Bibliographic record
Abstract
Cognitive effort is thought to be familiar in everyday life, ubiquitous across multiple variations of task and circumstance, and integral to cost/benefit computations that are themselves central to the proper functioning of cognitive control. In particular, cognitive effort is thought to be closely related to the assessment of cognitive control's costs. I argue here that the construct of cognitive effort, as it is deployed in cognitive psychology and neuroscience, is problematically unclear. The result is that talk of cognitive effort may paper over significant disagreement regarding the nature of cognitive effort, and its key functions for cognitive control. I highlight key points of disagreement, and several open questions regarding what causes cognitive effort, what cognitive effort represents, cognitive effort's relationship to action, and cognitive effort's relationship to consciousness. I also suggest that pluralism about cognitive effort-that cognitive effort may manifest as a range of intentional or non-intentional actions the function of which is to promote greater success at paradigmatic cognitive control tasks-may be a fruitful and irenic way to conceive of cognitive effort. Finally, I suggest that recent trends in work on cognitive control suggests that we might fruitfully conceive of cognitive effort as one key node in a complex network of mental value, and that studying this complex network may illuminate the nature of cognitive control, and the role of consciousness in cognitive control's proper functioning. This article is categorized under: Philosophy > Consciousness Philosophy > Psychological Capacities Neuroscience > Cognition.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.011 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".