A systematic examination of early perceptual influences on low-, mid and high-level visual abilities in Autism Spectrum Disorder
Bibliographic record
Abstract
Studies investigating visual perception in Autism Spectrum Disorder (ASD) have identified atypical abilities mediated by low-, mid-, and high-levels of processing (Mottron 2006, Bertone et al 2010a). Much of this research, however, has focused on isolated levels of processing (i.e. low or high). It is therefore unknown if a functional relationship exists between levels of information processing, and moreover, if alterations in early levels of visual analysis influence mid- and high-level perception in ASD. The goal of this project was to systematically assess whether manipulating either (i) the type (luminance vs texture), or (ii) access to early, local information differentially affects performance on tasks targeting low-, mid- and high-level perceptual processes in ASD. Three separate studies examined the effects of manipulating physical stimulus properties on progressively complex visuo-spatial tasks: low-level perception was assessed using luminance- and texture-defined gratings over a range of low to high spatial frequencies; mid-level perception was examined using luminance and texture-defined radial-frequency patterns manipulated to create “bumps” along their contours to optimize global (few bumps) and local (many bumps) processing; high-level perception was assessed using a face-identification task where access to local and global cues was manipulated by presenting faces from different orientations and viewpoints. For the low-level task, results revealed an increased sensitivity of the ASD group for high-spatial frequency information in the luminance-defined condition. For the mid-level task, the ASD group performed worse than the control group for luminance-defined RFPs with few modulations, but similarly for those with many modulations. For the high-level task, individuals with ASD were significantly worse identifying faces in the view-change condition in which local cues were limited. Our findings indicate that visual abilities mediated by low-, mid- and high-level mechanisms in ASD are differentially affected by the nature and access to early visual information during task completion. Meeting abstract presented at VSS 2015
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 | 0.000 | 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".