Image Reconstruction Reveals the Impact of Aging on Face Perception
Bibliographic record
Abstract
Abstract Extensive work has demonstrated a decline in face recognition abilities associated with healthy aging. To date, however, there has been limited insight into the nature and the extent of aging-related alterations in internal face representations. Here, we sought to address these issues by using an image reconstruction approach that capitalizes on the structure of behavioral data to reveal the pictorial content of visual representations. To this end, healthy young and older adults provided similarity judgments with pairs of face images. Facial shape and surface features were subsequently derived from the structure of the data for each participant and combined into image reconstructions of facial appearance. Our findings revealed that image reconstruction was successful for every participant, irrespective of age. However, reconstruction accuracies of shape and surface information were lower for older individuals than young individuals. Specifically, facial features diagnostic for face perception, such as eye shape and skin tone, were reconstructed poorly in older adults relative to young adults. At the same time, we found that age-related effects only accounted for a relatively small proportion of individual variability in face representations. Thus, our results provide novel insight into age-related changes in visual perception, they account for the decline in facial recognition occurring with age and they demonstrate the utility of image reconstruction to uncovering internal representations across a variety of populations.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| 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".