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 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 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".