Regret for Errors of Commission and Omission in the Distant Term Versus Near Term: The Role of Level of Abstraction
Bibliographic record
Abstract
Why are errors of omission regretted more than errors of commission in the distant past, whereas the reverse is true for the near past? The authors hypothesized that abstract versus concrete representation is a significant contributor to this effect. In Study 1, the authors assessed participants' regret for errors of commission versus omission occurring in the distant versus near past while measuring the level of abstraction at which participants spontaneously described the dilemma. As predicted, participants' greater regret for errors of omission (vs. commission) in the distant term (vs. near term) was mediated by level of abstraction. In Study 2, temporal distance, level of abstraction, and error type were all independently manipulated. As expected, participants reported more regret for an error of omission in the distant past when it was represented abstractly versus concretely. The authors discuss the role of mental abstraction in the phenomenology of binary decisions and error-related regret.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".