Laterality Effect for Faces in Chimpanzees (<i>Pan troglodytes</i>)
Bibliographic record
Abstract
Face perception in humans is governed more by right-hemispheric than left-hemispheric neural correlate. Some but not all neurophysiological studies depict a right-side dominance for face responsive neurons in the brains of macaques. Hence, it is an open question whether and to what extent a right-hemisphere preference of processing faces exists across primate brains. We investigated chimpanzees discriminating chimeric faces of chimpanzees and humans, i.e., the combination of either left or right sides of a face vertically flipped and merged into a whole face. We found an effect of choosing the left-chimeric face more often than the right-chimeric face as being the one of the two that is closer to the original face, reflecting an advantage for the right side of the brain to process faces, as reported in humans. Moreover, we found a modulation by age of the participants, suggesting that the exposure history with a particular category shapes the right-hemispheric neural correlate to a configural/holistic processing strategy. In other words, the findings in chimpanzee participants parallel those in human participants and are suggestive for similar neural machineries in the occipital-temporal cortices in both species.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".