A Neural Model for Compensation of Sensory Abnormalities in Autism Through Feedback From a Measure of Global Perception
Bibliographic record
Abstract
Sensory abnormalities and weak central coherence (WCC), a processing bias for features and local information, are important characteristics associated with autism. This paper introduces a self-organizing map (SOM)-based computational model of sensory abnormalities in autism, and of a feedback system to compensate for them. Feedback relies on a measure of balance of coverage over four (sensory) domains. Different methods to compute this measure are discussed, as is the flexibility to configure the system using different control mechanisms. Statistically significant improvements throughout training are demonstrated for compensation of a simple (i.e., monotonically decreasing) hypersensitivity in one of the domains. Fine-tuning control parameters can lead to further gains, but a standard setup results in good performance. Significant improvements are also shown for complex hypersensitivities (i.e., increasing and decreasing through time) in two domains. Although naturally best suited to compensate hypersensitivities--stimuli filtering may mitigate neuron migration to a hypersensitive domain--the system is also shown to perform effectively when compensating hyposensitivities. With poor coverage balance in the model akin to poor global perception, WCC would be consistent with inadequate feedback, resulting in uncontrolled hyper- and/or hyposensitivities characteristic of autism, as seen in the topologies of the resulting SOMs.
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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 | 0.000 |
| 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 | 0.001 |
| Open science | 0.001 | 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".