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 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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".