Classification images characterize age-related deficits in face discrimination
Bibliographic record
Abstract
Face perception is impaired in older adults, yet the cause of this decline is not well understood. Here we studied age-related changes in face perception using the random sub-sampling variant of the classification image (CI) method described by Nagai et al. (Vision Res, 2013). We obtained CIs for six older and eight younger observers performing a face discrimination task: Each trial presented a target face + noise, and observers indicated which of two faces they had seen. Noise contrast was adjusted using a staircase procedure maintaining ~71% correct, and observers completed two sessions (2900 trials total). As in previous studies (Sekuler et al, Cur Biol, 2004), younger observers consistently relied on pixels in the eye/brow region, and that strategy generally was apparent after just one session. Older adults demonstrated reduced sensitivity (higher contrast thresholds), made less efficient use of informative face regions (lower cross-correlations with the ideal template), and had CIs that differed qualitatively from younger observers. For example, only one older observer relied on the eye/brow region as heavily as younger observers, and most older observers showed no obvious structure in their CIs, even after two sessions. Greater individual differences also were observed for the older group: One observer with a high cross-correlation and low contrast threshold had no evident structure in the CI; while another showed a CI qualitatively similar to younger observers, yet had a low cross-correlation and high contrast threshold. Importantly, sensitivity and efficiency were correlated for older observers, suggesting the CI method captures older observers' perceptual strategy. The lack of consistent structure in older observer's CIs may result from increased variability in response strategy, an hypothesis we currently are testing using the response consistency method. Overall, our results are consistent with an age-related qualitative change in face processing. Meeting abstract presented at VSS 2014
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".