From Dynamic Lesions to Brain Imaging of Behavioral Lesions: Alloying the Gold of Psychoanalysis with the Copper of Suggestion
Bibliographic record
Abstract
Contemporary studies in the cognitive neuroscience of attention and suggestion shed new light on psychoanalytic concepts of yore. Findings from neuroimaging studies, for example, seem to revive the notion of dynamic lesions—focal brain changes undetectable by anatomical scrutiny. With technologies such as brain imaging and reversible brain lesion, some findings from modern biological psychiatry seem to converge with nineteenth-century psychiatry, reminiscent of the old masters. In particular, suggestion has been shown to modulate specific neural activity in the human brain. Here we show that “behavioral lesions”—the influence that words exert on focal brain activity—may constitute the twenty-first-century appellation of “dynamic lesions.” While recent research results involving suggestion seem to partially support Freudian notions, correlating psychoanalysis with its brain substrates remains difficult. We elucidate the incipient role of cognitive neuroscience, including the relative merits and inherent limitations of imaging the living human brain, in explaining psychoanalytic concepts.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.021 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".