Repeated attempts, phonetic errors, and syllabifications in a case study:Evidence of impaired transfer from phonology to articulatory planning
Bibliographic record
Abstract
Background: In aphasia, impairments affecting stages after lexical access have been subdivided into three types: 1. impairments specifying a sequence of phonemes after lexical access (the reproduction variety of conduction aphasia, CA); 2. impairments holding on to these representations during articulatory planning (the short-term memory (STM) variety of CA); and 3. Impairments specifying integrated articulatory/motor plans for clusters of phonemes (apraxia of speech, AoS). Models of speech production, however, suggest more articulated possibilities (i.e., different subtypes of articulatory impairments).Aims: We investigated the impairment in a person with aphasia whose preliminary assessment revealed mixed speech characteristics, combining features typically used to identify CA – phonological errors across tasks and repeated attempts at the target – with features typically used to identify AoS – phonetic errors and word dysfluencies (phoneme elongations and syllabifications). Our preliminary hypothesis was that there was a difficulty transferring information from an (intact) phonological output buffer to articulatory planning. Slow/noisy transfer would predict dysfluencies, errors selecting motor programs, but also repeated attempts (RA) at revising the output in the face of intact feedback and intact original representations. This hypothesis also predicts effects of position and phonological complexity.Method and Procedure: We tested CS’s word and nonword repetition, word reading, and picture naming. We quantified lexical and non-lexical errors, repeated attempts, phonetic errors, and syllabifications. We assessed effects of word frequency, word length, phoneme position, and syllabic and phonological complexity.Results: CS made similar errors across tasks, consistent with a post-lexical impairment. His RAs most often built up a correct target from fragments and/or previously incorrect attempts, similar to a conduite d’approche. He also produced more errors in later positions, and more repeated attempts on longer words. However, inconsistent with decay from an output buffer, phonological errors did not increase with word length. Finally, frequency mattered, consistent with easier/faster access to simpler/more practiced motor plans.Conclusions: CS’s speech characteristics and anatomical lesion are consistent with transfer limitations between phonology and articulatory planning. However, CS has more difficulties in computing articulatory plans than in selecting and retaining phonological representations, as commonly attributed to CA. CS’s case suggests that different varieties of phonological and articulatory disorders need to be distinguished, beyond a strict dichotomy AoS/CA (e.g., involving transfer limitations and difficulties in computing, selecting, and/or initiating articulatory plans).
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".