BRAIN, MIND AND MACHINE: WHAT ARE THE IMPLICATIONS OF DEEP BRAIN STIMULATION FOR PERCEPTIONS OF PERSONAL IDENTITY, AGENCY AND FREE WILL?
Bibliographic record
Abstract
Brain implants, such as Deep Brain Stimulation (DBS), which are designed to improve motor, mood and behavioural pathology, present unique challenges to our understanding of identity, agency and free will. This is because these devices can have visible effects on persons' physical and psychological properties yet are essentially undetectable when operating correctly. They can supplement and compensate for one's inherent abilities and faculties when they are compromised by neuropsychiatric disorders. Further, unlike talk therapy or pharmacological treatments, patients need not 'do' anything for the treatment to take effect. If one accepts, as we argue here, that brain implants are unique among implantable types of devices, then this can have significant implications for what it means to persist as the same person and be the source of one's thoughts and actions. By examining two of the most common indications for DBS in current use, namely in the motor (Parkinson's Disease) and psychiatric (Major Depression) domains, we further argue that although DBS, as it is currently applied, does not necessarily represent a unique threat to personal identity and agency per se, it introduces an unprecedented 'third party' into the debate on these concepts. In this way, DBS can be used as a tool to begin probing, both conceptually and empirically, some of philosophy's most perennial metaphysical questions.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.050 |
| Scholarly communication | 0.009 | 0.018 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".