The “Who” System of the Human Brain: A System for Social Cognition About the Self and Others
Bibliographic record
Abstract
Neuroscientists are fond of talking about brain systems for the processing of “what” and “where” information about objects and their locations. What is critically missing is the concept of a “who” system dedicated to the neural processing of information about social agents – both the self and others – and their interactions. I propose here the characterization of such a system, one that functions not only in perception but in production as well, such as when recounting stories about oneself and others. The most human-specific features of the “who” system are two complementary systems that I refer to as the other-as-self mechanism and the self-as-other mechanism. The major function of the other-as-self mechanism is to perceive other people egocentrically as proxies of the self, as occurs through the processes of mentalizing and empathizing in both everyday life and in the experience of the theatrical and literary arts. The major function of the self-as-other mechanism is to overtly depict other people during acts of communication through vocal and gestural processes of mimicry, such as occurs during quotation in conversation and through acting in the theatrical arts. Overall, the “who” system of the human brain mediates both perceptual and behavioral aspects of social cognition, and establishes the existential distinction between self and other in human cognition. I present neural models for the instantiation of the “who” system in the human brain, and conclude with a discussion of how narrative serves as a foundation for human cognition more generally, what I refer to as narrative-based cognition.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.019 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".