Voice recognition and the posterior cingulate: An fMRI study of prosopagnosia
Bibliographic record
Abstract
Voices, in addition to faces, enable person identification. Voice recognition has been shown to evoke a distributed network of brain regions that includes, in addition to the superior temporal sulcus (STS), the anterior temporal pole, fusiform face area (FFA), and posterior cingulate gyrus (pCG). Here we report an individual (MS) with acquired prosopagnosia who, despite bilateral damage to much of this network, demonstrates the ability to distinguish voices of several well-known acquaintances from voices of people that he has never heard before. Functional magnetic resonance imaging (fMRI) revealed that, relative to speech-modulated noise, voices rated as familiar and unfamiliar by MS elicited enhanced haemodynamic activity in the left angular gyrus, left posterior STS, and posterior midline brain regions, including the retrosplenial cortex and the dorsal pCG. More interestingly, relative to noise and unfamiliar voices, the familiar voices elicited greater haemodynamic activity in the left angular gyrus and medial parietal regions including the dorsal pCG and precuneus. The findings are consistent with theories implicating the pCG in recognizing people who are personally familiar, and furthermore suggest that the pCG region of the voice identification network is able to make functional contributions to voice recognition even though other areas of the network, namely the anterior temporal poles, FFA, and the right parietal lobe, may be compromised.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".