Effects of contextual information on eye movements and recall performance in face learning
Bibliographic record
Abstract
The presence of person information can make learning new faces more efficient (e.g., Schwartz & Yovel, 2018; Wiese & Schweinberger, 2015). We sought to replicate this finding and extend it to face-name associations. We also examined how visual scanning patterns change when participants learn new faces and face-name associations. Based on past literature, we predicted that there would be fewer fixations to a face as it becomes familiar, and that a higher proportion of these fixations would be to the eyes. We also predicted that person information would enhance recollection performance. Participants (N=26) took part in a learning phase, where each face was associated with a name. For half of the faces, additional person information (a hobby) was provided. Lighting conditions were varied across repeated presentations to avoid image-based recognition. Recognition and naming of the learned faces were tested immediately after the learning phase, and after 1-week and 2-week intervals. Faces were presented in a different viewing angle during recall. For scanning patterns, we focused specifically on the number of fixations on different interest areas (eyes, nose, mouth, face). Preliminary results suggest that the total number of fixations to a face is reduced as it becomes more familiar. In addition, the eyes were generally viewed more often than any other area of interest region, regardless of the number of exposures. However, unlike previous research, we did not find that the proportion of total fixations to the eyes increased with familiarity. The presence of person information did not influence on scanning patterns during learning, nor recognition performance at testing. These findings advance our understanding of face recognition, particularly regarding how eye movements can be used as an index of familiarity, and under which conditions person information aids (or does not aid) in forming face representations and face-name associations.
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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 | 0.006 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".