Relationships Among Emotion Categories: Emotion Aftereffects In High-Functioning Adults with Autism
Bibliographic record
Abstract
Visual aftereffects have been used to determine psychological relationships between perceived emotional facial expressions (Rutherford, Chattha & Krysko, 2007). Findings indicate that there is an asymmetrical relationship among perceived emotion categories: numerous negative emotions oppose few positive emotions. People with autism spectrum disorders (ASD) are believed to have atypical perception of emotional facial expressions (e.g. Rutherford & McIntosh, 2007). Two experiments use visual aftereffects to probe the psychological relationships among emotion categories in those with ASD. Experiment 1 was designed to test whether adults with ASD experienced aftereffects when viewing emotional facial expressions. Happy or Sad faces were the adapting image and a neutral image of the same model was the probe image. 19 ASD and 19 control participants saw the adapting image for 45s and the probe image for 800ms. Observers chose a label in a 4 AFC paradigm to describe the image. Clear evidence of aftereffects resulted. Experiment 2 was designed to probe relationships among the 6 basic emotions. Adapting images were the 6 basic emotions (one per trial) and the probe image was a neutral image of the same model. Response was obtained via 6 AFC task in which observers chose one of the six basic emotion labels to describe the probe image. The control group replicated previous findings. The ASD group showed evidence of afterimages, but different patterns of opposition: although happy opposed sad and sad opposed happy, the opposite of anger, fear and disgust was sad, whereas it was happy for the control group. Also, the opposite of surprise was predominantly disgust, for this group. This study is the first demonstration that we know of of visual aftereffects in a group with ASD. It also provides evidence that aftereffects can be used as a tool to reveal idiosyncratic organization of perceptual categories in special populations.
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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 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 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".