Neuroscience of human social instincts: a sketch
Bibliographic record
Abstract
In previous work, I have argued that we can divide the brain into a “Learning Subsystem” (cortex, striatum, etc.) housing randomly-initialized learning algorithms, and a “Steering Subsystem” (hypothalamus, brainstem, etc.) housing genetically-specified logic. Part of the Steering Subsystem is “human social instincts”—a suite of innate reactions and drives that are upstream of compassion, friendship, spite, norm-following, the sense of justice, and much more. The question addressed in this paper is: How do those human social instincts work? This problem is tricky because of a “symbol grounding problem”: Per above, I claim that our whole understanding of the world is built up by within-lifetime learning algorithms, and takes the form of a large unlabeled data structure. Certain activation states of this data structure—e.g., the activation state that represents someone insulting me—need to somehow trigger appropriate innate reactions. So there must be some way that the brain “grounds” these unlabeled learned concepts. How? In this article, I weave together many ideas supported by neuroscience research—visual heuristics in the superior colliculus, supervised learning in the amygdala, involuntary attention and learning rate modulation from the brainstem, and more—to sketch an answer. (26 pages, 20 figures)
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.004 | 0.012 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 0.006 |
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".