Imageability x phonology interactions during lexical access
Bibliographic record
Abstract
Although many studies have demonstrated the effects of imageability and phonological neighborhood size, few have examined if these factors interact. Strain, Patterson, and Seidenberg (1995) explained an imageability effect in naming low-frequency exception words (only) as being due to a slowing of orthographic-to-phonological mapping for these words, which allowed semantics to have an effect. Tyler, Voice, and Moss (2000) showed an interaction between imageability and phonological cohort size in word repetition. Westbury and Buchanan (2006) found an interaction between imageability and phonology using an auditory false memory paradigm that measured the false recognition rate for phonological associates of semantically primed words. They explained the finding in terms of a greater reliance of abstract than concrete words on phonological representations. In this paper we test three related hypotheses: that the imageability x phonology interaction should be modulated by modality; that measures of phonological processing fluency should predict the size of the interaction; and that concrete and abstract words should show a systematic difference in number of phonological neighbours. We find support for all three hypotheses, suggesting that the interaction between imageability and phonology reflects a difference in the representation of abstract and concrete words in the lexicon.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".