From lollipops to lidocaine: The need for a universal print-to-speech framework.
Bibliographic record
Abstract
OBJECTIVE: There is a strong relationship between reading and articulation (Lervåg & Hulme, 2009; Pan et al., 2011). Given the tight coupling of these processes, innovative approaches are needed to understand the intricacies associated with print-speech connections. Here we ran a series of tightly controlled experiments to examine the impact of mouth perturbations on silent reading. METHOD: = 97; E1 = 27; E2 = 32; E3 = 38) completed each of the tasks two times: once with a somatosensory perturbation (lollipop, bite bar, or lidocaine) and once without. RESULTS: For each experiment, a linear mixed effects analysis was run. Overall, we found that the lollipop (E1) and lidocaine (E3) had some specific effects on word recognition (e.g., for "no" responses), particularly in the Spell-LDT, whereas the bite bar (E2) had no effect on word recognition. The picture categorization task was not impacted by any perturbations. CONCLUSION: These findings provide evidence that sensorimotor information is connected to reading. We discuss how these findings advance our understanding of a print-to-speech framework. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".