Deep dyslexia and semantic errors: A test of the failure of inhibition hypothesis using a semantic blocking paradigm
Bibliographic record
Abstract
Deep dyslexia is an acquired reading disorder that involves the production of semantic errors and the inability to read aloud nonwords successfully. Several explanations for this reading impairment posit multiple loci of damage to account for the various error types produced in deep dyslexia. In contrast, the failure of inhibition hypothesis suggests that damage in the phonological output lexicon alone can explain these errors. Specifically, this hypothesis proposes normal processing via orthographic and phonological reading routes, as well as an intact semantic system. However, slowed or reduced inhibitory connections result in the failure to suppress spuriously activated neighbours in the phonological output lexicon, where neighbourhood can be defined in terms of phonology, orthography, or semantics. Given a failure to inhibit semantically related candidates, semantic reading errors occur. Important to the test of this hypothesis is that it evolves several predictions that are contrary to performance observed in the normal population. In particular, semantic errors are predicted to be greater in conditions where words are blocked according to semantic category than in random presentations. In addition, a semantic interference effect is expected. The results of semantic blocking were consistent with these predictions and lend support to the failure of inhibition hypothesis.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".