Assessing Kurt Goldstein’s lasting influence in the neuropsychology of language versus his use of aphasic symptoms as diagnostic insights into brain injuries
Bibliographic record
Abstract
In the history of the neurological relationship between human behavior and brain function in Europe and North America, various perspectives on brain localization and holistic functioning have been addressed. One of the founding figures of modern neuropsychology, Professor Hans-Lukas Teuber (1916-1977) of the Massachusetts Institute of Technology, reminded the scholarly community of its negligence of preceding traditions in day-to-day research endeavors. Teuber particularly emphasized that during the development of the aphasiology field (1950s-1960s) even major figures, such as the German-American neurologist Kurt Goldstein (1878-1965), had been neglected in the scientific community's collective memory. This happened despite Goldstein's contributions to cortical blindness, vicarious brain functioning, and neurorehabilitation. The outcome of the debates regarding the neurology of language had to be incompletely relearned in later decades. Neuropsychological concerns regarding the relationship between cortical localizationism and functional holism have made recourse to Goldstein's work necessary for reviving historical answers for current conundrums. It is therefore opportune to review Goldstein's work in the light of the history of aphasiology. Contemporary scholarship has once more drawn research attention to the works of Goldstein along with Norman Geschwind (1926-1984) and his pupils. It has also resurrected the underlying research of Carl Wernicke (1848-1905). This review article explores deep and lasting questions regarding the positioning of Goldstein's holism among the contemporary holistic perspectives. It does so by firstly discussing Wernicke's traditional model of distributed localizationism. Secondly, it describes Goldstein's previous work in the German brain sciences. Thirdly, it examines his aphasiological contributions on both sides of the Atlantic. Fourthly, it addresses the advancement of a dynamic localizational perspective by Geschwind and his pupils. This article intends to render a historical analysis fruitful for those exploring modern-day problems in the neurology of aphasia and clinical speech neuropsychology.
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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 | 0.007 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.009 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".