Dual processing streams in chemosensory perception
Bibliographic record
Abstract
Higher order sensory processing follows a general subdivision into a ventral and a dorsal stream for visual, auditory, and tactile information. Object identification is processed in temporal structures (ventral stream), whereas object localization leads to activation of parietal structures (dorsal stream). To examine whether the chemical senses demonstrate a similar dissociation, we investigated odor identification and odor localization in 16 healthy young subjects using functional MRI. We used two odors-(1) eucalyptol; (2) a mixture of phenylethanol and carbon dioxide)-which were delivered to only one nostril. During odor identification subjects had to recognize the odor; during odor localization they had to detect the stimulated nostril. We used general linear model (GLM) as a classical method as well as independent component analysis (ICA) in order to investigate a possible neuroanatomical dissociation between both tasks. Both methods showed differences between tasks-confirming a dual processing stream in the chemical senses-but revealed complementary results. Specifically, GLM identified the left intraparietal sulcus and the right superior frontal sulcus to be more activated when subjects were localizing the odorants. For the same task, ICA identified a significant cluster in the left parietal lobe (paracentral lobule) but also in the right hippocampus. While GLM did not find significant activations for odor identification, ICA revealed two clusters (in the left central fissure and the left superior frontal gyrus) for this task. These data demonstrate that higher order chemosensory processing shares the general subdivision into a ventral and a dorsal processing stream with other sensory systems and suggest that this is a global principle, independent of sensory channels.
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 | 0.001 | 0.001 |
| 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".