Underestimation in linear function learning: Anchoring to zero or x-y similarity?
Bibliographic record
Abstract
Function learning research has shown that people tend to underestimate positive linear functions when extrapolating Y for X-values below the training range. Kwantes and Neal (2006) proposed that this underestimation occurs because people anchor their Y-estimates at zero. It is equally plausible, however, that people are biased to make Y-estimates similar to the presented X-value. To differentiate these 2 explanations, 135 participants extrapolated positive linear functions with a y-intercept either greater than or less than zero. In line with the anchoring hypothesis, participants underestimated in the lower extrapolation region when the y-intercept was positive, but overestimated when the y-intercept was negative. These results are consistent with a version of the extrapolation association model (EXAM; Delosh, Busemeyer, & McDaniel, 1997), which proposes that people interpolate linearly between the training exemplars and zero in the lower extrapolation region. (PsycINFO Database Record
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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 | 0.008 | 0.108 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".