Effects of Category and Rhyme Decisions on Sentence Production
Bibliographic record
Abstract
The aim of the present experiment was to investigate differences between persons who stutter and persons who do not stutter during the production of sentences in a single task versus two dual-task conditions. Participants were required to form a sentence containing 2 unrelated nouns. In dual-task conditions, rhyme and category decisions were used as secondary tasks. The results for 14 adults who stutter and 16 adults who do not stutter are reported. Dependent variables were the number of correct rhyme and category decisions, decision latencies, length, number of propositions, sentence latency, speech rate of sentences, disfluencies, and stuttering rates. The results indicated that both groups reduced the average number of correct rhyme and category decisions when this task was performed concurrently with sentence generation and production. Similarly, the 2 groups of participants did not differ with respect to the correctness and latency of their decisions. Under single-task conditions the sentences of both groups had a comparable number of propositions. But under dual- as compared with single-task conditions persons who stutter significantly reduced the number of propositions whereas persons who do not stutter did not show a significant dual- versus single-task contrast. Experimental conditions did not significantly influence stuttering rates. These results suggest that persons who stutter require more processing capacity for sentence generation and articulation than persons who do not stutter and that both groups keep stuttering rates at a constant level by adjusting the number of propositional units of their linguistic productions. The results support the view that the organization of the speech-production system of persons who stutter makes it more vulnerable to interference from concurrent attention-demanding semantic tasks.
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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 | 0.006 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".