Dissociation between anterior and posterior cortical regions during self‐specificity and familiarity: A combined fMRI–meta‐analytic study
Bibliographic record
Abstract
The familiarity to the subject of any potential stimuli presents one of the major difficulties for the investigation of the self; the separation of effects resulting from familiarity from self-effects being extremely problematic. The aim of this study was thus to investigate the neural distinction between self and familiarity by combining two sets of fMRI data with a meta-analysis. In the first fMRI experiment, regions responding to self/familiarity were investigated using the subject's own name and names of familiar others. These effects were confirmed and extended in a second fMRI experiment using the subject's own name and a stranger's name, as spoken by familiar and unfamiliar voices. Finally, a meta-analysis of self- and familiarity-related studies was conducted. Neural activity in the anterior brain regions, such as the anterior cingulate (ACC) and anterior insula (AI), was found to be specific for self-specific stimuli. In contrast, posterior brain regions, such as the posterior cingulate, were activated by familiar stimuli. Finally, the distinction between anterior and posterior regions for self and familiarity was confirmed by meta-analytic data. This study demonstrates a clear anterior-posterior cortical partition between self-specificity and familiarity.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".