Mechanisms Underlying an Ability to Behave Ethically
Bibliographic record
Abstract
Cognitive neuroscientists have anticipated the union of neural and behavioral science with ethics (Gazzaniga 2005). The identification of an ethical rule--the dictum that we should treat others in the manner in which we would like to be treated--apparently widespread among human societies suggests a dependence on fundamental human brain mechanisms. Now, studies of neural and molecular mechanisms that underlie the feeling of fear suggest how this form of ethical behavior is produced. Counterintuitively, a new theory presented here states that it is actually a loss of social information that leads to sharing others' fears with our own, thus allowing us to treat others as we would like to be treated. Adding to that hypothetical mechanism is the well-studied predilection toward affiliative behaviors. Thus, even as Chomsky hypothesizes that humans are predisposed to utter grammatical sentences, we propose that humans are 'wired for reciprocity'. However, these two neural forces supporting ethical behavior do not explain individual or collective violence. At any given moment, the ability to produce behavior that obeys this ethical rule is proposed to depend on a balance between mechanisms for prosocial and antisocial behaviors. That balance results not only from genetic influences on temperament but also from environmental effects particularly during critical neonatal and pubertal periods.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".