Technologies of the extended mind: Defining the issues
Bibliographic record
Abstract
Living in the modern world entails substantial interaction with information technologies. The ways in which people interact with these devices—how they enter into daily practices—has become so profound that they qualify as technologies of the extended mind. This chapter distinguishes between these devices acting as cognitive support versus becoming bona fide extensions of our minds, and argues that this latter, new reality has substantial implications for the field of neuroethics. As exemplars, the implications of these technologies of the extended mind for concepts of autonomy and privacy of thought, as well as for the debate regarding cognitive enhancement, are investigated. The chapter calls for a new framework for thinking about neuroethics for technology that takes into account not just the effects of technology upon the brain, but one that also includes a more expansive concept of the mind.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.064 |
| Scholarly communication | 0.016 | 0.040 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.011 | 0.012 |
| Insufficient payload (model declined to judge) | 0.005 | 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".