Inducing homonymy effects via stimulus quality and (not) nonword difficulty: Implications for models of semantic ambiguity and word recognition.
Bibliographic record
Abstract
Reports of a processing advantage for polysemes with related senses (e.g., <printer>/<academic> PAPER) in lexical decision and a processing disadvantage for homonyms (e.g., <river>/<money> BANK) in semantic categorization have prompted the development of conflicting accounts of these phenomena.Whereas a decision-making account (Hino, Pexman, & Lupker, 2006) suggests these effects are due to qualitative differences between the tasks, accounts based on temporal settling dynamics (Armstrong & Plaut, 2008) suggest that processing time is the critical factor.To compare these accounts, we manipulated nonword difficulty and stimulus quality to make lexical decision difficult and attempted to produce the same homonymy disadvantage as in semantic categorization.We found that stimulus degradation succeeded to this end, and nonword difficulty only consistently slowed nonword responses.This provides evidence both for settling dynamics accounts of semantic ambiguity in particular, and for interactive orthographic-to-semantic processing and the construction of more integrated models, in general.
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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 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".