A review of computational models of word recognition and pronunciation
Bibliographic record
Abstract
How do we recognize words and assign a pronunciation? Computational models provide a formal description of the mechanisms and principles that guide the reading process. I review and evaluate the Interactive-Activation Model (IAM), Dual Route Cascaded (DRC) model, the Parallel Distributed Processing (PDP) model, and the Connectionist Dual Processing (CDP) model, as well as LEX, a variant of the MINERVA model of memory. I evaluate each model’s ability to account for consistency effects, serial effects, syllable effects, and phonological effects. Consistency effects pose a problem for the rule-based pronunciation of the DRC. Serial effects pose a problem for the purely parallel PDP models. Phonological effects pose a problem for all models save CDP. All models suffer from the distribution problem, weakening each model’s ability to learn spelling-to-sound relationships. LEX is the only model that handles polysyllabic words. As none of the models provide a complete answer to the question of ‘how do we read?’, ‘how do we pronounce?’, or ‘how do we recognize words?’, I outline a set of principles as guidelines for future model development. Models of reading should learn, include a visual attention mechanism, be sensitive to phonology, and account for meaning and spelling in addition to recognizing words and pronouncing them.
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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 | 0.000 | 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".