Steps towards understanding the phonological output buffer and its role in the production of numbers, morphemes, and function words
Bibliographic record
Abstract
The Stimulus Type Effect on Phonological and Semantic errors (STEPS) describes the phenomenon in which a person, following brain damage, produces words with phonological errors (fine => fige), but number words with semantic errors (five => eight). To track the origins of this phenomenon and find out whether it is limited to numbers, we assessed the speech production of six individuals with conduction aphasia following a damage in the left hemisphere, who made phonological errors in words. STEPS was found in all six participants, and was not limited to number words – several other word categories were also produced with semantic rather than phonological errors: function words, English letter names, and morphological affixes were substituted with other words within their category. This supports the building blocks hypothesis: when phonological sequences serve as building blocks in a productive process, they end up having pre-assembled phonological representations, ready for articulation. STEPS reflects a deficit that causes substitutions of one phonological unit with another. In the case of plain content words, this causes substitutions of one phoneme with another, but in the case of pre-assembled phonological unit, this causes substitutions of number words with other number words, function words with function words, and morphological affixes with other affixes. An analysis of the participants’ functional locus of deficit revealed that they all had a deficit in the phonological output buffer, and this was their only common deficit. We therefore concluded that the pre-assembled phonological units are stored in dedicated mini-stores in the phonological output buffer, which processes not only phonemes but also whole number words, function words, and morphemes. We also found that STEPS depends on the word's role: number words were produced with semantic errors only when they appeared in numeric context, and function words triggered semantic errors only in grammatical context. This suggests that the phonological representation of a word can be obtained either from the phonological output lexicon or from a store of pre-assembled representations in the phonological output buffer, depending on the word's role.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.006 |
| Scholarly communication | 0.003 | 0.013 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".