The role of name labels in the formation of face representations in event‐related potentials
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
In this paper, we discuss the role of name labels in facial recognition, arguing that the function of a proper name is to direct the level of specificity at which a face is perceived. First, we discuss the expertise hypothesis of face recognition in which the face is identified at the specific, subordinate level of the individual. This research has important implications with respect to how a name label affects the recognition of a face stimulus. Next, we consider evidence from neurophysiological research revealing how names facilitate the familiarity, categorization and individuation of a face as measured by event-related potentials (ERPs). We examine results from studies of perceptual expertise and the other-race effects (OREs) that suggest formation and retrieval of face representations are heavily dependent on name labels. In light of these findings, we propose the function of a proper name is to direct visual attention to the most subordinate-level category associated with the individuated identity of a face. The uniqueness of the proper name label dictates that the representation mediating face recognition will be a highly detailed, perceptual description of the person.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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 it