Theory of mind network activity is associated with metaethical judgment: An item analysis
Bibliographic record
Abstract
The theory of mind network (ToMN) is a set of brain regions activated by a variety of social tasks. Recent work has proposed that these associations with ToMN activity may relate to a common underlying computation: processing prediction error in social contexts. The present work presents evidence consistent with this hypothesis, using a fine-grained item analysis to examine the relationship between ToMN activity and variance in stimulus features. We used an existing dataset (consisting of statements about morals, facts, and preferences) to examine the variability in ToMN activity elicited by moral statements, using metaethical judgments (i.e. judgments of how objective/subjective morals are) as a proxy for their predictability/support by social consensus. Study 1 validated expected patterns of behavioral judgments in our stimuli set, and Study 2 associated by-stimulus estimates of metaethical judgment with ToMN activity, showing that ToMN activity was negatively associated with objective morals and positively associated with subjective morals. Whole brain analyses indicated that these associations were strongest in bilateral temporoparietal junction (TPJ). We also observed additional by-stimulus associations with ToMN, including positive associations with the presence of a person (across morals, facts, and preferences), a negative association with agreement (among morals only), and a positive association with mental state inference (in preferences only, across 3 independent measures and behavioral samples). We discuss these findings in the context of recent predictive processing models, and highlight how predictive models may facilitate new perspectives on both metaethics and the nature of distinctions between social domains (e.g. morals vs. preferences).
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".