The role of response inconsistency in older adults' face discrimination ability
Bibliographic record
Abstract
The efficiency with which observers discriminate faces declines with age. For example, using the classification image (CI) method with sub-sampled faces, Creighton et al (VSS 2014) showed that older adults sampled diagnostic face information less efficiently compared to younger adults. However, many older observers lacked obvious structure in their CIs, perhaps reflecting increased trial-to-trial variability in response strategy. Response consistency can be used to estimate the ratio of internal-to-external (i/e) noise affecting observers' decision. Here, we use the double-pass response consistency technique to estimate age-related changes in i/e ratios. Contrast thresholds were measured in 7 younger and 6 older observers performing a 2-AFC task for faces embedded in high external noise. In the first half of the experiment, noise fields were randomly generated on each trial, and stimulus contrast varied according to 2 interleaved staircases. In the second half of the experiment, this same sequence of face identities, contrast, and noise fields were repeated exactly, and percent correct and percent agreement (across the two halves) were calculated. The slope of the accuracy-vs.-consistency function was then used to estimate the magnitude of each observer's i/e ratio. To facilitate comparison with previous work (e.g., Gold et al, 2004; Creighton et al.), thresholds were measured with full and sub-sampled faces. Preliminary findings show higher thresholds in older than younger adults for both full- and sub-sampled faces, and this difference was greater for sub-sampled faces. The slopes of the consistency functions were slightly shallower for older than younger observers in the sub-sampled condition, suggesting increased thresholds partly reflect an age-related increase in older adults' i/e ratio. We currently are testing older observers from the original CI study to see if individual differences in response inconsistency are associated with the degree of structure observed in their CIs. Meeting abstract presented at VSS 2018
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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 | 0.002 | 0.013 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".