Can Neuroscience Contribute to Practical Ethics? A Critical Review and Discussion of the Methodological and Translational Challenges of the Neuroscience of Ethics
Bibliographic record
Abstract
Neuroethics is an interdisciplinary field that arose in response to novel ethical challenges posed by advances in neuroscience. Historically, neuroethics has provided an opportunity to synergize different disciplines, notably proposing a two-way dialogue between an 'ethics of neuroscience' and a 'neuroscience of ethics'. However, questions surface as to whether a 'neuroscience of ethics' is a useful and unified branch of research and whether it can actually inform or lead to theoretical insights and transferable practical knowledge to help resolve ethical questions. In this article, we examine why the neuroscience of ethics is a promising area of research and summarize what we have learned so far regarding its most promising goals and contributions. We then review some of the key methodological challenges which may have hindered the use of results generated thus far by the neuroscience of ethics. Strategies are suggested to address these challenges and improve the quality of research and increase neuroscience's usefulness for applied ethics and society at large. Finally, we reflect on potential outcomes of a neuroscience of ethics and discuss the different strategies that could be used to support knowledge transfer to help different stakeholders integrate knowledge from the neuroscience of ethics.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".