When “bad” is good: How evaluative judgments eliminate the standard anchoring effect.
Bibliographic record
Abstract
Early accounts of judgmental anchoring attribute the effect to a deliberate, but insufficient, adjustment process; more recent theories point to automatic, priming-based processes as the underlying cause. In this article we introduce a novel anchor assessment manipulation and a decompositional analysis of the standard anchoring effect to determine the extent to which anchoring is driven by automatic versus deliberate processes. Prior to providing a target estimate, participants indicated whether the target was greater or less than the anchor, or whether the anchor would make a good or bad target estimate. Contrary to predictions of priming-based accounts, the decomposition of the anchoring effect revealed that participants generally provided estimates consistent with their prior assessment; in particular, anchoring was eliminated when participants considered the anchor to be a bad target estimate. These findings challenge the view of anchoring as an inevitable bias of numerical judgment and indicate that people have significant control over how they manage numerical information in judgments under uncertainty. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
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How this classification was reachedexpand
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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".