Comparing social attention in autism and amygdala lesions: Effects of stimulus and task condition
Bibliographic record
Abstract
The amygdala plays a critical role in orienting gaze and attention to socially salient stimuli. Previous work has demonstrated that SM a patient with rare bilateral amygdala lesions, fails to fixate and make use of information from the eyes in faces. Amygdala dysfunction has also been implicated as a contributing factor in autism spectrum disorders (ASD), consistent with some reports of reduced eye fixations in ASD. Yet, detailed comparisons between ASD and patients with amygdala lesions have not been undertaken. Here we carried out such a comparison, using eye tracking to complex social scenes that contained faces. We presented participants with three task conditions. In the Neutral task, participants had to determine what kind of room the scene took place in. In the Describe task, participants described the scene. In the Social Attention task, participants inferred where people in the scene were directing their attention. SM spent less time looking at the eyes and much more time looking at the mouths than control subjects, consistent with earlier findings. There was also a trend for the ASD group to spend less time on the eyes, although this depended on the particular image and task. Whereas controls and SM looked more at the eyes when the task required social attention, the ASD group did not. This pattern of impairments suggests that SM looks less at the eyes because of a failure in stimulus-driven attention to social features, whereas individuals with ASD look less at the eyes because they are generally insensitive to socially relevant information and fail to modulate attention as a function of task demands. We conclude that the source of the social attention impairment in ASD may arise upstream from the amygdala, rather than in the amygdala itself.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 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".