Anchoring in Time Estimation: The Effects of Explicit Anchoring on Prospective Time Estimates
Bibliographic record
Abstract
Quantitative estimates are biased when they are preceded by a guiding value.This is known as the anchoring effect (Tversky & Kahneman, 1974).Limited research has examined anchoring effects in duration judgment tasks.Five-hundred and twenty-eight undergraduates kept track of time while playing a Tetris game.The experiment consisted of a 4 (Durations: 30 seconds, 1 minute, 2 minutes, 4 minutes) X 3 (Anchors: 0.5, 1, 2) between-subjects design.After the task, the participants estimated the game's duration.As expected, raw estimates increased linearly with duration.However, evidence for anchoring was mixed.Overall, large anchors yielded overestimation, but small anchors did not yield underestimation.Moreover, these effects were inconsistent across durations.The results indicated that the anchoring bias might not emerge at short durations.Further research is required to replicate this research and determine the conditions under which anchors influence time judgments.
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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 | 0.034 | 0.256 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".