Episodic recognition memory for high-dimensional, human synthetic faces
Bibliographic record
Abstract
Purpose: We investigated the human visual memory for faces, and evaluated the effects of perceptual similarity on visual memory performance with varying sets of study and test items. By using realistic, computer synthesized faces as stimuli, we could systematically vary the perceptual similarity among the items to be remembered, thereby gauging inter-item similarity's effect on visual memory. Methods: In Experiment 1, Sternberg's recognition memory paradigm was applied to a set of 21 synthesized faces. On each trial, from 1 to 4, briefly presented Study faces were followed by a single Probe face. Subjects indicated whether the Probe had or had not been among the Study faces. To force reliance on episodic memory, Study and Probe items varied from trial to trial. In Experiment 2, the method of triads, followed by multidimensional scaling (MDS), was used to characterize subjects' perceptual similarity space for the faces. Results: Experiment 1 showed that recognition memory was strongly influenced by the number of faces comprising a study set, and by the recency of a face's occurrence on a trial. Expressing differences among faces in terms of distances derived from the MDS similarity space, we found that perceptual similarity among faces accounted for much of the variance in recognition memory performance. Finally, between-subject differences in the face-similarity space were relatively small. Conclusion: Inter-item similarity has powerful effects on episodic memory. We applied further analysis to examine the effects of similarity between the probe and the most similar lure, the similarity among all lures, and the similarity between the probe and all other lures. The fitness of visual memory models will be discussed in light of these results.
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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 | 0.001 | 0.001 |
| 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) | 0.001 | 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".