Looking-at-nothing: an ocular motor index of face recognition in control subjects and developmental prosopagnosics
Bibliographic record
Abstract
When subjects are shown visual stimuli and then have to perform a task involving these stimuli after they have disappeared from the screen, they still tend to fixate the regions where those stimuli had been located, an effect called ‘looking-at-nothing’. We conducted three experiments to examine whether this effect could act as an implicit index of face recognition in both control subjects (n = 48) and individuals with developmental prosopagnosia (n = 8). On each trial, a subject saw a 3-second video of a person’s face and then saw a choice screen of four faces for 1.2 seconds. This was followed by a response screen with empty boxes, at which point they had to respond whether the face in the video had been present in the preceding choice screen. We analyzed the fixations made while subjects were viewing the response screen (without faces present). Control subjects were more likely to fixate the empty box where the target face was presented, The frequency of this looking-at-nothing effect was greater on hit than on miss trials. Conversely, the odds of a correct response were increased if the first fixation was made within the empty target box. The temporal dynamics of the looking-at-nothing effect showed that it was present for the first fixation only and then transitioned to reduced fixations on the empty target box. Across subjects there was a positive correlation between discriminative sensitivity and the frequency of the looking-at-nothing effect. Developmental prosopagnosic subjects showed a similar effect but reduced in magnitude, with the looking-at-nothing evident on hit but not miss trials. We conclude that the looking at nothing effect can index rapid face recognition in both control and developmental prosopagnosic subjects, and that it is correlated with explicit discriminative performance.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".