Semantic Learning Modifies Perceptual Face Processing
Bibliographic record
Abstract
Face processing changes when a face is learned with personally relevant information. In a five-day learning paradigm, faces were presented with rich semantic stories that conveyed personal information about the faces. Event-related potentials were recorded before and after learning during a passive viewing task. When faces were novel, we observed the expected N170 repetition effect-a reduction in amplitude following face repetition. However, when faces were learned with personal information, the N170 repetition effect was eliminated, suggesting that semantic information modulates the N170 repetition effect. To control for the possibility that a simple perceptual effect contributed to the change in the N170 repetition effect, another experiment was conducted using stories that were not related to the person (i.e., stories about rocks and volcanoes). Although viewers were exposed to the faces an equal amount of time, the typical N170 repetition effect was observed, indicating that personal semantic information associated with a face, and not simply perceptual exposure, produced the observed reduction in the N170 repetition effect. These results are the first to reveal a critical perceptual change in face processing as a result of learning person-related information. The results have important implications for researchers studying face processing, as well as learning and memory in general, as they demonstrate that perceptual information alone is not enough to establish familiarity akin to real-world person learning.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".