Implicit facial emotion recognition in a case of cortical blindness
Bibliographic record
Abstract
Previous research has suggested that recognition of fearful faces may be carried out by pathways that bypass primary visual cortex (V1) and project to the amygdala. Some of the strongest evidence supporting this claim comes from two previous case studies of "affective blindsight" in which patients were able to correctly guess whether an unseen face was depicting a fearful or happy expression. In the current study we report a new case of affective blindsight in patient MC who is cortically blind following extensive bilateral lesions to V1 and most of her ventral stream. Despite her large lesions MC has preserved motion perception which is related to sparing of the motion sensitive region MT+ in both hemispheres. To examine affective blindsight in MC we asked her to perform gender and emotion discrimination tasks in which she had to guess, using a two alternative forced-choice procedure, whether the face presented was male or female, or was depicting a happy vs. fearful, or a happy vs. angry expression. Finally, we also asked MC to perform a four alternative forced-choice target localization task in which she simply had to guess whether a target (a large circle) was presented on the top, bottom, left, or right of a computer screen. Results indicated that MC was not able to determine the gender of the faces (51% accuracy), or localize targets (29%). However, MC was able to determine, at significantly above chance levels, whether the face presented was depicting a happy or fearful (67%, p=.006), or a happy or angry (64%, p=.025) expression. These data lend further support to the idea that there is a non-conscious visual pathway that bypasses V1, as well as higher-order face processing regions in the ventral stream, that is capable of processing affective signals from facial expressions. Meeting abstract presented at VSS 2013
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".