Attention capture by faces and trains: A developmental study
Bibliographic record
Abstract
There is evidence that faces capture attention during visual search (Langton, Law, Burton, & Schweinberger, 2008). However faces do not capture the attention of individuals with autism spectrum disorder (ASD) to the same extent (Riby, Brown, Jones, & Hanley, 2012). Whereas individuals with ASD show less preferential processing of faces, objects in which they are interested and with which they have developed expertise have been shown to capture attention (McGugin, McKeeff, Tong, & Gauthier, 2011). In an effort to understand this possible interaction between face and special interest object processing in individuals with ASD, we conducted a visual search experiment with children with ASD (ages 7-12) and compared their performance to that of their age and IQ matched peers, and adults without ASD. Participants searched for a target in an array of distractors, which included faces, trains (the most common special interest object among children with ASD), and various neutral object categories that were not of special interest (e.g., chairs, clocks, fruit). Contrary to our hypothesis that faces would capture attention to a greater extent than trains, we found that the presence of either a face or a train slowed response times relative to the neutral distractors for all participants. Why do faces and trains capture the attention of children and adults to the same extent? We explore this question with a developmental and methodological focus. Meeting abstract presented at VSS 2016
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".