Bibliographic record
Abstract
Sigmund Freud, the founder of psychoanalysis, was initially a neuroscientist but abandoned neuroscience completely after he made a last attempt to link both in his writing, "Project of a Scientific Psychology," in 1895. The reasons for his subsequent disregard of the brain remain unclear though. I here argue that one central reason may be that the approach to the brain during his time was simply not appealing to Freud. More specifically, Freud was interested in revealing the psychological predispositions of psychodynamic processes. However, he was not so much focused on the actual psychological functions themselves which though were the prime focus of the neuroscience at his time and also in current Cognitive Neuroscience. Instead, he probably would have been more interested in the brain's resting state and its constitution of a spatiotemporal structure. I here assume that the resting state activity constitutes a statistically based virtual structure extending and linking the different discrete points in time and space within the brain. That in turn may serve as template, schemata, or grid for all subsequent neural processing during stimulus-induced activity. As such the resting state' spatiotemporal structure may serve as the neural predisposition of what Freud described as "psychological structure." Hence, Freud and also current neuropsychoanalysis may want to focus more on neural predispositions, the necessary non-sufficient conditions, rather than the neural correlates, i.e., sufficient, conditions of psychodynamic processes.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.055 |
| Scholarly communication | 0.008 | 0.021 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.008 | 0.011 |
| 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".