Acquired language disorders beyond aphasia: foreign accent syndrome as a neurological, speech, and psychiatric disorder
Bibliographic record
Abstract
This study examines historical conceptualizations of 'foreign accent syndrome' after brain trauma or as an aspect of psychiatric presentations, in addition to comparisons with current conceptualizations. Although classical understanding of aphasias as language disorders developed between 1861 and 1885, descriptions of non-aphasic speech disorders emerged later. Acquired accent following a stroke was first described in 1907 by Pierre Marie (1853-1940) in the context of the localizationist versus holistic debate. Early characterizations by Marie, Arnold Pick (1851-1924), and G.H. Monrad-Krohn (1884-1964) identified persisting speech changes following initial aphasia, which, from a contemporary viewpoint, provide insights into the dynamic nature of recovery after cerebral injury. These cases significantly contributed to the understanding of the neurological foundations of prosody and the non-linguistic aspects of speech. A deeper understanding of this disorder awaited contributions from various fields, including linguistics, speech-language pathology, psychiatry, and neuroimaging. Notably, there is an unusual gap in psychiatric causation reports prior to 1960, despite some intriguing indications from Josef Breuer's account of Anna O (1895). This study explores how historical perspectives continue to influence current conceptualizations of foreign accent syndrome.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".