The effects of N-gram probabilistic measures on the recognition and production of four-word sequences
Bibliographic record
Abstract
The present study investigates the processing and production of four-word sequences such as I don’t really know, at the age of, and I think it’s the. Specifically, we investigate the influence of families of probabilistic measures such as unigram, bigram, trigram, and quadgram frequency of occurrence, logarithmic (log) probability of occurrence, and mutual information. Log probability of occurrence emerged as the predominant predictor family in the onset latency analysis, suggesting that recognition is mainly underpinned by competition between a target N-gram and its family members. In contrast, the amount of experience one has with an N-gram (frequency of occurrence) surfaced as the most prominent predictor in production. Further, probabilistic measures tied to trigrams surfaced as the best predictors in the onset latency analysis, while the measures tied to unigrams were most predictive of production durations.Finally, the interactions between probabilistic measures tied to unigrams, bigrams, trigrams, and quadgrams suggest that N-grams of different lengths are processed in parallel in both recognition and production.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".