The correspondence problem in apparent motion perception and aging
Bibliographic record
Abstract
The largest spatial displacement of dots in a two-frame random-dot kinematogram yielding good direction discrimination performance (i.e., Dmax) is reduced in older observers (Roudaia et al., J. Vis., 2010). Dmax depends on dot density, which suggests that Dmax is constrained by the number of false targets in the display (Eagle & Rogers, Vis. Res., 1997). As such, the reduction in Dmax with aging may be caused by an age-related decline in the efficiency of solving the correspondence problem. The current study investigates that hypothesis. Stimuli consisted of white dots (0.04 × 0.04 deg) randomly distributed in a small, medium, or large square patch (height: 6.4, 12.7, or 25.4 deg) on a black background. The number of dots was varied to yield average density levels ranging from 0.025% to 5%. On each trial, two random-dot patterns were presented for 100 ms each, separated by a blank ISI lasting 40 ms. The second pattern was identical to the first pattern, but was shifted within a display window to the right or to the left by a displacement ranging from 0.03 to 5.6 deg. Direction discrimination accuracy of older (mean age: 74 years) and younger (mean age: 24 years) subjects was measured in 105 conditions blocked by patch size. Performance in both groups was best at medium displacements, and declined at smaller and larger displacements. Decreasing density did not affect performance at short displacements, but improved performance at large displacements. Importantly, this improvement was significantly greater in older than younger subjects, such that the effect of age at large displacements greatly reduced with decreasing density. These results support the hypothesis that age-related declines in performance at large displacements are caused by a reduced ability to solve the correspondence problem in motion.
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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 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".