Phonological Interlopers Tend to Repeat When Tip-of-the-Tongue States Repeat
Bibliographic record
Abstract
We elicited tip-of-the-tongue (TOT) states to replicate the finding that TOTs repeat for individual words. Humphreys and colleagues have attributed this error repetition phenomenon to implicit learning of the mappings between the lemma and phonology. We also examined whether or not interlopers - repeated information that persistently comes to mind - repeat during TOT states for individual words, along with the type of interlopers. Participants were given a TOT test and the same TOT test one week later. The test consisted of the presentation of a definition and participants indicated if they knew the word, did not know the word, or if they were in a TOT state. Participants were also given 15 s to think aloud about the target word on both test and retest. We found that information repeats significantly more often for repeated TOT states (26%) than repeated Don't Know states (13%). We also found that participants experienced significantly more repeated phonological interlopers during a repeated TOT state (59%) versus a repeated Don't Know state (12%). Theoretically, the results may suggest that the TOT state is best described as a subthreshold state, and that within this subthreshold state there is a specific erroneous pattern of activation (akin to a local minimum) rather than a non-specific pattern of activation. These findings are an important constraint toward the development of a more formal explanation of recurring TOTs.
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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 | 0.000 | 0.004 |
| 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) | 0.001 | 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 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".