Reading Words Aloud: An Example of Retrieval from Semantic Memory
Bibliographic record
Abstract
Current models of word naming use neural networks to map letters to sounds. The mapping reproduces the complex statistical structure of letter and phoneme use in English and implies that the brain not only learns the structure of spelling and speech but also stores weights to map print to sound. Curiously, although the storage requirements are high, the stored information can only be used for one purpose. It does not support spelling of words from speech even though readers have little trouble spelling the words they know. In short, current models seem to be storing the wrong information. We argue that the brain stores letter and phoneme strings for all the words that we know, and that phenomena currently attributed to oddities of the mapping reflect averaging during retrieval of stored information. We demonstrate a representation and retrieval system for lexical knowledge and show that it can reproduce standard naming phenomena. Importantly, the retrieval system allows the structure of language to fall out of knowledge without requiring the structure to be reproduced in the processing machinery. Finally, we discuss why a model such as ours requires high-performance computing.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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; 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".