Selectivity for faces as exogenous attentional cues
Bibliographic record
Abstract
In the standard exogenous cueing paradigm, a peripheral visual pre-cue affects the time to detect a subsequent peripheral target. This exogenous attention effect is thought to be a reflexive process based on simple properties of the cueing stimulus. This is complicated somewhat by recent experiments in which human faces are used as pre-cues, and their effects depend upon facial expression. These results have been interpreted in terms of a reflexive process selective for threat-related signals. Here we examine whether exogenous cueing may be based on more general ecological principles, by comparing the efficacy of human face pre-cues with random-phase controls. The target was a bright 0.25 deg disk. The face cues were 2 deg natural images of faces with neutral expressions. The control cues had identical amplitude spectra but randomized phase. The intensity, hue and saturation of the face images and their controls were matched in both mean and variance. Subjects were asked to maintain fixation on a central cross. After 500 msec, a cue was flashed for 20 msec, 8 deg randomly to the left or right of fixation. Following a variable SOA, the target was displayed 6 deg randomly to the left or right of fixation until response. The location of the cue was not predictive of the location of the target. 45 observers each completed 960 randomly-interleaved trials. Faces were found to be significantly more effective as exogenous attentional cues than random-phase controls. Interestingly, this effect was lateralized in the invalid-cue condition. Specifically, the effect of an invalid face cue was significantly less pronounced when the cue was presented in the left hemifield and the target was presented in the right hemifield, than vice-versa. This finding can be interpreted in terms of a specialization for faces in the right hemisphere of the human brain.
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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 | 0.001 | 0.002 |
| 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) | 0.002 | 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".