Reducing, restoring, or enhancing autonomy with neuromodulation techniques
Bibliographic record
Abstract
This chapter focuses on the question of how deep brain stimulation (DBS) and other new and emerging neuromodulation techniques such as transcranial magnetic stimulation (TMS), transcranial direct current stimulation (tDCS), and neurofeedback can influence our autonomy, that is, our capacity to govern ourselves. First, the concept of autonomy is clarified and related to concepts like authenticity or free will. Distinctions are made between autonomy as capacity and as condition, local and global autonomy, autonomous choice and executive autonomy. Next, a brief overview of the main existing and emerging neuromodulation techniques is given. It is then argued that neuromodulation techniques can impact on autonomy in various ways. Neurodmodulation can reduce autonomy by impeding the capacities needed for autonomous choice, or by affecting a person’s values and preferences. However, if the person endorsed these changes, his global autonomy can remain intact. Furthermore, it is argued, neuromodulation can also restore or even enhance capacities necessary for autonomy. Finally, it is argued that differences between techniques have consequences for their impact on autonomy. “Passive” techniques like DBS are more prone to abuse and to disrupting autonomy than techniques-like neurofeedback-that require active participation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".