Bibliographic record
Abstract
Abstract Face perception is a challenging task, given the richness of information contained in faces, such as identity, expression and age, their dynamic and complex shapes and the subtlety of the differences between faces. Our face expertise shows a marked orientation dependence, being superior for upright faces, and the mechanisms involved process not only facial features but their spatial relationships to each other in a holistic, integrated manner. Cognitive models propose that recognition involves a flow of information through a series of stages, with distinct pathways for different facial properties, such as identity and expression. Anatomically, face processing involve a core face processing network in the occipital and temporal lobes, including the fusiform face area, as well as more extended regions of the brain. Face recognition is impaired in the condition of prosopagnosia, which can be either acquired or developmental. It may also be anomalous in some subjects with autism. Key Concepts Many types of information can be gathered through face perception, including the identity, gender, emotional state, age and attractiveness of the person. The face inversion effect is a marker of an orientation‐dependent perceptual expertise we develop with faces. The composite face effect and the part–whole advantage are experimental observations that show that we process faces in a holistic manner. Not all parts of the face are equal in importance: the upper half and the eyes play a greater role in identifying the face. Cognitive models propose a series of processing stages, with information flowing from perception to memory, and in different streams for identity and expression processing. Neuroanatomic data show a core face network in the occipital and temporal lobes, more significant in the right side of the brain that interacts with additional regions in frontal and anterior temporal regions. Prosopagnosia is the inability to recognise the identity of familiar faces, or to learn to recognise new faces. There is debate about whether the recognition problem in prosopagnosia is limited to faces: some data indicate that identification of other objects for which the patient is also an expert may be affected too. Prosopagnosia can be developmental, perhaps through a hereditary defect that affects the development of face‐sensitive cortical regions or their connections. Patients with autism can have difficulty with recognising the identity or expression of faces: some suggest that this is due to their tendency to avoid looking at faces.
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.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.131 | 0.049 |
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 source (direct Gemma or distilled Codex), 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".