Laterality and hemispheric specialization of self-face recognition
Bibliographic record
Abstract
Inspired by the pioneering work of Eran Zaidel beginning in the early 1970's on the role of the two cerebral hemispheres of the human brain in self-related cognition, we review research on self-face recognition from a laterality perspective. The self-face is an important proxy of the self, and self-face recognition has been used as an indicator of self-awareness more broadly. Over the last half century, behavioral and neurological data, along with over two decades of neuroimaging research evidence have accumulated on this topic, generally concluding a right-hemisphere dominance for self-face recognition. In this review, we briefly revisit the pioneering roots of this work by Sperry, Zaidel & Zaidel, and focus on the important body of neuroimaging literature on self-face recognition it has inspired. We conclude with a brief discussion of current models of self-related processing and future directions for research in this area.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".