Effects of viewing condition and age on the functionality of eye movements for face recognition memory
Bibliographic record
Abstract
How do eye movements influence recognition memory for faces? Henderson et al. (2004) recently found that younger observers performed better when they studied faces under free-view conditions (in which no restrictions were placed on eye movements) and then tested under free-view conditions, compared to when observers studied faces under fixed-view conditions (in which fixations were restricted to a small central area of the face) and then tested under free-view conditions. Henderson et al. concluded that eye movements are functional: they help to encode faces in a way that facilitates subsequent recognition. However, it is possible that free-viewing during study lead to better recognition simply because the same mode of viewing was used in the test phase. The current experiment examined this idea in younger (age = 19) and older (age = 70) subjects. Viewing conditions (fixed and free) were combined factorially with the phase of the experiment (study and test), yielding four conditions. In the study phase, 24 faces were each presented for 10 secs, and subjects were instructed to remember the faces. In the test phase, 48 faces were each presented until observers responded whether a face was previously studied (50%) or novel (50%). 12 older and 12 younger subjects were tested in each condition. There was a main effect of condition: recognition (d') was significantly better in the two conditions that allowed free viewing during the study phase (F(1, 40)=8.01, p=0.007), irrespective of the type of viewing in the test phase. The main effect of age and the age x condition were not significant. Our findings support Henderson et al.'s claim that eye movements during study improve later recognition, and further show that this functionality persists with aging.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.003 | 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".