There goes the neighbourhood: Contextual control over the breadth of lexical activation when reading aloud
Bibliographic record
Abstract
There are currently two computational accounts of how the time to read pseudohomophones (like BRANE) and their nonword controls (like FRANE) varies with changes in context. In Reynolds and Besner's (2005) account, readers vary the breadth of lexical activation in response to changes in context. A competing account proposed by Kwantes and Marmurek (2007) and independently by Perry, Ziegler, and Zorzi (2007) has readers varying their response criterion in response to changes in context. The present work adjudicates between these two accounts by examining how the effect of neighbourhood density changes as a function of list context when reading pseudohomophones aloud. The results of an experiment and simulations from a leading computational model support the lexical breadth account, but are inconsistent with the response criterion account.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".