Beyond Numbers: An Ambiguity–Accessibility–Applicability Framework to Explain the Attraction Effect
Bibliographic record
Abstract
Abstract The attraction effect (AE) occurs when the addition of an inferior alternative (i.e., a decoy) to a choice set increases the choice share of the alternative to which it is most similar (i.e., a target), a phenomenon that violates the regularity principle. The AE occurs reliably when the attribute values are represented numerically, but not when the stimuli are perceptual. Such conceptual replication failures indicate a lack of clarity about the mechanisms that produce the AE. The present research develops a framework—the 3A framework—that specifies the distinct functions of ambiguity, accessibility, and applicability in the choice process. These factors, and their attendant mechanisms, explain when and why the AE emerges. They also specify conditions under which the AE is attenuated. Seven main experiments and four supplementary experiments examine when and why the AE emerges with perceptual stimuli, provide support for the 3A framework, and offer insights about how to produce the AE in choice contexts involving perceptual stimuli.
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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 | 0.071 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.003 |
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; both teacher heads agree on what is shown here.
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".