Rubber hands and astral bodies: An argument for the reality of a cognitive science of magic
Bibliographic record
Abstract
Recent academic discussion of magic has largely fallen into three camps: the psychological study of magical thinking, in an effort to explain it away; the sociological or anthropological study of magical beliefs and practices, remaining agnostic about their efficacy; and some attempts to make magic presentable as a subject of serious cognitive science. The present paper presents an alternative approach to the empirical study of magical practices. Rather than present an apologetic for magical practitioners or argue for a future area of research, the present paper argues that magical practices, by many other names, are already subject to serious scientific investigation, and to pretend otherwise is disingenuous. Through evidence from the history and anthropology of magic and the psychological study of cognitive biases, the body schema, meditation, suggestion, ritual, and mystical experiences, it is argued that there already exists robust areas of empirical research dedicated to studying phenomena that have been historically folded under the umbrella of “magic.” It is further argued that increased communication and theoretical integration amongst these areas of research, forming a strong nomological network, could lead to both improved understanding of mechanisms underlying each phenomenon, as well as fruitful areas for future clinical and knowledge-driven research.
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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 | 0.004 | 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 | 0.065 |
| Scholarly communication | 0.007 | 0.017 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 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".