Expecting tasks to help or hurt subsequent cognitive performance: Variability, accuracy, and bias in forecasted after‐effects
Bibliographic record
Abstract
Abstract After‐effects on cognition—where a prior activity either benefits or hinders subsequent cognitive performance—are empirically inconsistent. Do people have insight into when their subjective energy and cognition will be helped or hurt by engaging in prior activities? Studies 1a and 1b (combined N = 316) find that people expect more demanding and unenjoyable tasks to hinder their subsequent energy and cognitive performance, regardless of their willpower lay theory. Study 2 (N = 167) examines the accuracy of these forecasts using a within‐subject design. Participants’ forecasts of their future subjective states did predict their actual experienced subjective states, but participants were not able to accurately forecast their subsequent maths performance. Additionally, they significantly overestimated the detrimental effects of demanding prior activities on both subjective state and performance. Study 3 (N = 210) found that participants’ overestimation of detrimental after‐effects could result in unnecessary financial costs, suggesting these biased forecasts can have consequences.
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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 | 0.012 | 0.096 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".