What determines whether faces are special?
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
“Is face perception special?” has become one of the most frequently asked questions among cognitive scientists. This issue has generated considerable debate and produced diversified rather than unified answers around the polarized “yes—no” positions. The ongoing confusion in this field now calls for a theoretical synthesis. The goal of this paper is to review and examine the conceptual basis of the contradictory claims and to offer a unified scheme for experimental inquiry. We argue that most differences in the stated claims can be traced to conceptual rather than empirical determinants. Assessment discrepancies arise prior to empirical investigations because of the use of unfounded assumptions. The key to resolving the current controversy will largely depend upon settling some conceptual issues. We propose to replace the commonly adopted approach of assessing a single criterion with one where the question is addressed along multiple dimensions that include comparison of face and object perception in terms of their innate specification, localization, and domain specificity using developmental, neuropsychological, and neurophysiological measures.
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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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