The Impact of Viewing Time to Internal Facial Features on Face Recognition Performance Following Implicit and Explicit Encoding
Bibliographic record
Abstract
The eyes play an important role in conveying social cues, including identity. Previous studies have shown that face recognition performance is higher when the eyes are visible or cued during encoding, compared to when the eyes are absent or when other features are cued. Here we explored the relationship between time spent looking at the eyes during encoding and face recognition performance, and whether this link might vary with task demands. Eye movements were compared while participants mentally assessed the trustworthiness of faces (implicit encoding task), and when they memorized the identity of faces (explicit encoding task). Behavioural performance was obtained during a surprise old/new face recognition test following the implicit task, and during an expected old/new face recognition test following the explicit task. With a preliminary sample (N = 38), participants spent less time looking at the mouth during encoding, compared to the left eye, right eye, and nose, which did not differ from each other. However, task demands did not differentially affect feature viewing times. Face recognition accuracy (d') was higher following the explicit encoding task compared to accuracy following the implicit encoding task. Moreover, for the explicit task only, longer viewing time to the left eye was weakly associated with higher accuracy, whereas longer viewing time on the nose was weakly associated with lower accuracy. These findings support a link between time spent looking at specific facial features and face recognition performance that seems dependent on the task demands at encoding, such that only the left eye seems to play a role in facilitating intentional face recognition. Meeting abstract presented at VSS 2018
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 | 0.003 |
| 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 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".