Motivated Use of Numerical Anchors for Judgments Relevant to the Self
Bibliographic record
Abstract
The anchoring effect has been replicated so extensively that it is generally thought to be ubiquitous. However, anchoring has primarily been tested in domains in which people are motivated to reach accurate conclusions rather than biased conclusions. Is the anchoring effect robust even when the anchors are threatening? In three studies, participants made a series of probability judgments about their own futures paired with either optimistic anchors (e.g., "Do you think that the chances that your current relationship will last a lifetime are more or less than 95%?"), pessimistic anchors (e.g., "more or less than 10%?"), or no anchors. A fourth study experimentally manipulated motivation to ignore the anchor with financial incentives. Across studies, anchors that implied high probabilities of unwanted events occurring were ineffective. Together, these studies suggest that anchoring has an important boundary condition: Personally threatening anchors are ignored as a result of motivated reasoning processes.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".