Olfaction in the Autism Spectrum
Bibliographic record
Abstract
The autism spectrum (AS) is characterised by enhanced perception in vision and audition, described by the enhanced perceptual functioning (EPF) model. This model predicts enhanced low-level (discrimination of psychophysical dimensions), and mid- and high-level (pattern detection and identification) perception. The EPF model is here tested for olfaction by investigating olfactory function in autistic and Asperger participants. Experiment 1 targeted higher-order olfactory processing by assessing olfactory identification in nine Asperger, ten autistic, and eleven typically developed individuals. Experiment 2 focused on low-level olfactory processing; we assessed odour detection thresholds and odour discrimination in five Asperger, five autistic, and five typically developed males. Olfactory identification was impaired in autistic participants relative to control and Asperger participants. Typical performance in low-level olfactory processing suggests that neural mechanisms involved in the perceptual phenotype of AS do not affect structures implicated in olfactory processing. Reduced olfactory identification is limited to autistic participants who displayed speech delay and may be due to a reduced facility to use verbal labels. The apparent absence of enhanced olfactory perception of AS participants distinguishes the olfactory system from the other sensory modalities and might be caused by the absence of an obligatory thalamic relay.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.001 | 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".