Empirical neuroenchantment: from reading minds to thinking critically
Bibliographic record
Abstract
While most experts agree on the limitations of neuroimaging, the unversed public-and indeed many a scholar-often valorizes brain imaging without heeding its shortcomings. Here we test the boundaries of this phenomenon, which we term neuroenchantment. How much are individuals ready to believe when encountering improbable information through the guise of neuroscience? We introduced participants to a crudely-built mock brain scanner, explaining that the machine would measure neural activity, analyze the data, and then infer the content of complex thoughts. Using a classic magic trick, we crafted an illusion whereby the imaging technology seemed to decipher the internal thoughts of participants. We found that most students-even undergraduates with advanced standing in neuroscience and psychology, who have been taught the shortcomings of neuroimaging-deemed such unlikely technology highly plausible. Our findings highlight the influence neuro-hype wields over critical thinking.
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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 | 0.019 | 0.135 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.046 |
| Scholarly communication | 0.005 | 0.013 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".