Fear recognition in four patients with focal bilateral amygdala damage
Bibliographic record
Abstract
Bilateral damage to the amygdala can lead to a dramatic impairement in fear recognition through an inability to gaze at and utilize information about the eye region of faces (Adolphs et al., 2005). Here, we extended these findings by applying the Bubbles method, which asks viewers to discriminate happy and fearful faces from randomly sampled small regions of a face, with concomitant gaze tracking, to a group of four rare patients with focal bilateral amygdala damage (including new sessions with SM, the amygdala patient examined in Adolphs et al., 2005) as well as 20 healthy controls. Two of the amygdala patients behaved indiscriminably from healthy controls (JF and BG), while the other two (SM and AB) required an unusually large number of face samples to attain target performance, and neither gazed at nor made use of the eye region. Repeatedly instructing the two impaired amygdala patients to look at the eyes in faces reduced the number of face samples required to reach target performance within the range of healthy controls, and led these patients to gaze at and to use the eye on the right side of the face stimuli. In contrast, this instruction had little impact on the other amygdala patients and on healthy controls. We will argue that these distinct behavioral profiles found in amygdala patients are due to slight differences in their lesions. Meeting abstract presented at VSS 2013
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".