The Human Brain Distinguishes between Single Odorants and Binary Mixtures
Bibliographic record
Abstract
Single odors are processed differently from odor mixtures in the cortex of rodents. We investigated whether single and binary odor mixtures activate different regions also in the human brain. We analyzed data from positron emission tomography scans using pyridine, citral, and 5 mixtures of pyridine and citral in proportions varying from 10/90 to 90/10, with 50/50 being the most impure. Comparing mixtures with single odorants gave activation in the left cingulate and right parietal and superior frontal cortices and bilateral activation in the anterior and lateral orbitofrontal cortices. We also found that brain activity in the lateral orbitofrontal cortex (OFC) increased with odorant impurity, whereas the anterior OFC was activated for binary odor mixtures and deactivated for single components. We conclude that binary odor mixtures and their individual components are processed differently by the human brain. The lateral portion of the OFC responds to mixture impurity in a graded fashion, whereas the anterior portion acts like an on-off detector of odor mixtures.
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 | 0.000 | 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) | 0.002 | 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".