Configuration does not affect the allocation of visual attention to foreground and background information differently in autism on a change detection task
Bibliographic record
Abstract
Background The visual processing of autistic people has been connected to a tendency to process local, or detailed, information more readily than the overall global, or configural, structure of visual information. This style has been highlighted in evidence of unique and efficient attention processing on certain tasks, such as the detection of changes. However, research on change detection in autism is mixed, with discrepancies seemingly attributed to the diversity of stimuli and the specific components of perception and attention involved in the task. Purpose We assessed rudimentary differences in change detection between autistic and non-autistic participants using a simple geometric task manipulating local/global processing and foreground/background attention. Method Thirty-two autistic adolescents and adults ( M age = 20.21, SD = 6.10) and 32 mental-age matched non-autistic adolescents and adults ( M age = 19.79, SD = 4.92) completed a change-detection task involving displays of rectangles presented in configural (global) and non-configural (local) arrangements. The participants were asked to indicate whether changes to the colours of these displays occurred. Results Three mixed-effect analyses of variance comparing accuracy, detection sensitivity, and response bias revealed a similar pattern of visual prioritization for both groups, with more accurate, sensitive and less biased change detection for foreground changes, especially non-configural ones. While accuracy levels were similar across groups, non-configural changes produced greater detection sensitivity for non-autistic than autistic participants. Conclusion The findings reflect similar attentional patterns between autistic and non-autistic adolescents and young adults in change detection, even when issues of both configural/non-configural arrangement and foreground/background elements were considered.
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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 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".