Anomalous Experiences, Trauma, and Symbolization Processes at the Frontiers between Psychoanalysis and Cognitive Neurosciences
Bibliographic record
Abstract
Anomalous or exceptional experiences are uncommon experiences which are usually interpreted as being paranormal by those who report them. These experiences have long remained difficult to explain, but current progress in cognitive neuroscience and psychoanalysis sheds light on the contexts in which they emerge, as well as on their underlying processes. Following a brief description of the different types of anomalous experiences, we underline how they can be better understood at the frontiers between psychoanalysis and cognitive neurosciences. In this regard, three main lines of research are discussed and illustrated, alongside clinical cases which come from a clinical service specializing in anomalous experiences. First, we study the links between anomalous experiences and hallucinatory processes, by showing that anomalous experiences frequently occur as a specific reaction to negative life events, in which case they mainly take the form of non-pathological hallucinations. Next, we propose to analyze these experiences from the perspective of their traumatic aspects and the altered states of consciousness they often imply. Finally, these experiences are considered to be the consequence of a hypersensitivity that can be linked to an increase in psychic permeability. In conclusion, these different processes lead us to consider anomalous experiences as primary forms of symbolization and transformation of the subjective experience, especially during, or after traumatic situations.
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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 | 0.002 | 0.003 |
| 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 | 0.024 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| 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".