Bibliographic record
Abstract
Because the brain generates and sustains mental states, it seems possible that neuroimaging techniques could reveal the content of these states and 'read' the mind. Gerben Meynen explores how brain-based mind reading (BMR) could be a technique in forensic psychiatry to 'assess defendants, prisoners, and possibly also prospective jurors.' 1 He describes three types of BMR and 'the different ways in which a person's mind can be read' (p. 4). Meynen discusses how BMR could be used for lie detection, to elucidate the role of intent in legal judgements and predict the likelihood of recidivism (p. 16). He mentions confidentiality, trust in the doctor-patient relationship, and the coercive use of these techniques as some of the ethical and legal issues they raise. The last of these issues has been especially pertinent to functional magnetic resonance imaging (fMRI)-based lie detection, which has had variable assessments of accuracy. Apart from this, the BMR techniques Meynen envisages for future application in forensic psychiatry are hypothetical. 'In fact, at present almost no technique appears to be ready for use in forensic psychiatric evaluations. Therefore, the topic of BMR basically derives its relevance and urgency from anticipated developments in the (near) future. And at present we do not really know the exact nature of the techniques that will eventually be ready for forensic psychiatry use ' (p. 5). Claims about the potential use of BMR in psychiatry and the criminal law are highly speculative. There are thus good reasons for being circumspect about the potential for BMR. Indeed, there are good reasons for skepticism about the very idea of mind reading through measuring brain structure and function and thus the very idea of BMR. This is not only because of limitations in the ability of neuroimaging to reveal actual brain activity but also because the mind is not located in the brain. The ontological question of the relation between the brain and the mind, and the epistemological questions of what we can ascertain about brain activity 1
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".