Neural correlates of personally familiar faces: Parents, partner and own faces
Bibliographic record
Abstract
Investigations of the neural correlates of face recognition have typically used old/new paradigms where subjects learn to recognize new faces or identify famous faces. Familiar faces, however, include one's own face, partner's and parents' faces. Using event-related fMRI, we examined the neural correlates of these personally familiar faces. Ten participants were presented with photographs of own, partner, parents, famous and unfamiliar faces and responded to a distinct target. Whole brain, two regions of interest (fusiform gyrus and cingulate gyrus), and multiple linear regression analyses were conducted. Compared with baseline, all familiar faces activated the fusiform gyrus; own faces also activated occipital regions and the precuneus; partner faces activated similar areas, but in addition, the parahippocampal gyrus, middle superior temporal gyri and middle frontal gyrus. Compared with unfamiliar faces, only personally familiar faces activated the cingulate gyrus and the extent of activation varied with face category. Partner faces also activated the insula, amygdala and thalamus. Regions of interest analyses and laterality indices showed anatomical distinctions of processing the personally familiar faces within the fusiform and cingulate gyri. Famous faces were right lateralized whereas personally familiar faces, particularly partner and own faces, elicited bilateral activations. Regression analyses show experiential predictors modulated with neural activity related to own and partner faces. Thus, personally familiar faces activated the core visual areas and extended frontal regions, related to semantic and person knowledge and the extent and areas of activation varied with face type.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".