Greater usage of the left eye causes better facial gender discrimination
Bibliographic record
Abstract
We recently discovered that the use of the eye located on the left side of the stimuli (henceforth the left eye) was positively correlated with performance. Here, we tested the causality of this link by combining a reinforcement procedure and a classification image technique (i.e. Bubbles). Using these techniques, we first induced the use of the right eye in one group of participants (N=11) and the use of the left eye in another group (N=11), and then examined the impact of these manipulations on performance. Participants completed four training sessions (250 trials/session) of a face-gender categorization task in which the faces of men and women were sampled with an adjustable number of randomly located Gaussian apertures (or bubbles). When their target area was sampled, participants received X points for correct responses and were taken 2X points for incorrect responses. Feedback about earnings was given after each trial. Although the participants remained unaware of the conditioning, classification image analyses revealed that the eye which use was reinforced was more positively correlated with accuracy than any other facial regions in both groups. Critically, the performanceâ€"as indexed by the number of bubbles required to reach 75% of correct responsesâ€"improved significantly more in the group for which the use of the left eye was reinforced than in the other group. Therefore, greater usage of the left eye causes better facial gender discrimination. We will discuss these findings in the context of hemispheric specialization in the brain. Meeting abstract presented at VSS 2014
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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".