The Role of Information in Visual Word Recognition: A Perceptually-Constrained Connectionist Account
Bibliographic record
Abstract
Proficient readers typically fixate near the center of a word,with a slight bias towards word onset. We explore a novelaccount of this phenomenon based on combining information-theory with perceptual constraints in a connectionist model ofvisual word recognition. This account posits that the amountof information-content available for word identification variesacross fixation locations and across languages. These differ-ences contribute to the overall fixation location bias in differ-ent languages, make the novel prediction that certain wordsare more readily identified when fixating at an atypical fixa-tion location, and predict specific cross-linguistic differences.We tested these predictions across several simulations in En-glish and Hebrew, and in a behavioral experiment. The resultsconfirmed that the bias to fixate closer to word onset alignswith reducing uncertainty in the visual signal, that some wordsare more readily identified at atypical fixation locations, andthat these effects vary across languages.
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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) | 0.002 | 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 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".