Eying the eyes in social scenes: Diminished importance of social attention in simultanagnosia
Bibliographic record
Abstract
Simultanagnosia is a disorder of visual attention that results from bilateral lesions to the parieto-occipital junction. These patients have difficulty seeing more than one object at a time. We previously reported that simultanagnosics allocate abnormally few fixations to the eyes of people in social scenes. Given that healthy individuals look at the eyes of others to infer people's attentional states, this finding might reflect that a) for simultanagnosics the attentional states of others are not a high priority or, b) these patients are unable to use the eyes to infer the attention of others. To distinguish between these two alternatives we monitored the eye movements of simultanagnosic patient GB, and healthy controls, while they 1) described social scenes, or, 2) inferred the attention of people in the scenes. Consistent with past work, healthy individuals tended to look at the eyes of others in both conditions, but significantly more so when explicitly inferring the attentional states of the people depicted. GB fixated the eye regions far less than controls while describing social scenes, but performed similar to controls when explicitly asked to infer attentional states. Thus, like healthy subjects, GB shares a top-down understanding that the eyes are an important source of information for the attentional states of others. However, when deriving scene information, this attentional information is not normally prioritized by simultanagnosic patients to the same degree as it is by healthy individuals. This indicates that when multiple objects in a scene are not available concurrently a key social attention cue – eye gaze – is not used, despite the fact that knowledge of the value of this cue exists.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".