Pragmatism and the Importance of Interdisciplinary Teams in Investigating Personality Changes Following DBS
Bibliographic record
Abstract
Gilbert and colleagues (2018) point out the discrepancy between the limited empirical data illustrating changes in personality (and related concepts of identity, agency, authenticity, autonomy, and self, i.e., PIAAAS) following implantation of deep brain stimulating (DBS) electrodes and the vast number of conceptual neuroethics papers implying that these changes are widespread, deleterious, and clinically significant. Their findings are reminiscent of C. P. Snow's essay on the divide between the two cultures of the humanities (representing the conceptual publications) and the sciences (representing the empirical work). This division in the literature raises significant ethical concerns surrounding unjustified fear of personality changes in the context of DBS and negative perceptions of clinician-scientists engaged in DBS. These concerns have real world implications for funding future innovative, DBS trials aimed to reduce suffering as well as hampering true interdisciplinary scholarship. We argue that the philosophical tradition of pragmatism and the value it places on empirical inquiry, experiential knowledge, and inter-disciplinary scholarship - reflecting diverse ways of knowing - provides a framework to start to address the important questions Gilbert and colleagues raise. In particular, we highlight the importance of expert clinician knowledge in contributing to the neuroethical questions raised by Gilbert and colleagues. Finally, we provide illustrative examples of some of our interdisciplinary empirical research that demonstrate the iterative cycle of inquiry characteristic of pragmatism in which conceptual neuroethics questions have led to empirical studies whose results then raise additional conceptual questions that give rise to new empirical studies in a way that highlights the contributions of the humanities and the sciences.
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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 | 0.113 | 0.189 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.023 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.001 | 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".