Similarity modulates the face-capturing effect in change detection
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract We investigated whether similarity among faces could modulate the face-capturing effect in change detection. In Experiment 1, a singleton search task was used to demonstrate that a face stimulus captures attention and the odd-one-out hypothesis cannot account for the results. Searching for a face target was faster than searching for a nonface target no matter whether distractor–distractor similarity was low or high. The fast search, however, did not lead to a face-detection advantage in Experiment 2 when the pre- and postchange faces were highly similar. When participants in Experiment 3 had to divide their attention between two faces in stimulus displays for change detection, detection performance was worse than performance in detecting nonface changes. The face-capturing effect alone is insufficient to produce the face-detection advantage. Face processing is efficient but its effect on performance depends on the stimulus–task context. Acknowledgements This research was supported by a grant from National Science Council to Y.-Y. Yeh (NSC 95-2413-H-002-003). We thank R. Palermo, Y.-M. Huang, H.-F. Chao, and Y.-C. Chiu for their valuable comments on an earlier version of the manuscript. We also thank S.-H. Lin for his assistance on stimulus generation. Parts of the results were presented at the 13th annual meeting of OPAM, Toronto, Canada in 2005. Notes 1We thank Ro for providing us with the stimuli from his study. Only achromatic female faces were used in their experiments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 it