Short Article: Learning to Fail: Reoccurring Tip-of-the-Tongue States
Bibliographic record
Abstract
This experiment looked at elicited tip-of-the-tongue (TOT) states to test the hypothesis that making an error once makes people more likely to make it again, via an implicit learning mechanism. We present a methodology that allows us to determine whether error reoccurrences are due to error learning or to the fact that some items tend to pose repeated difficulty to participants. We elicited TOTs by asking participants to supply the word that fitted a given definition. Each time participants indicated that they were experiencing a TOT they were randomly assigned a delay of either 10 or 30 seconds, during which they were asked to keep trying to retrieve the item. After the delay, the correct answer was supplied. We argue that this longer delay in a TOT state amounts to greater implicit learning of the erroneous state. A period of 48 hours later, participants returned to the laboratory and were asked to supply the words for the same definitions as those seen on Day 1. Results showed that TOTs were almost twice as likely to reoccur on words that had elicited a TOT and been followed by a long delay than on those that had been followed by a short delay.
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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 | 0.002 | 0.020 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".