Visual perception and visual cognition in healthy and pathological ageing
Bibliographic record
Abstract
This volume features state-of-the-art approaches to determining the effects of ageing on visual perception, visual cognition, and visually guided behavior.They incorporate psychophysics, eye movements, electrophysiology, and neuroimaging to determine how ageing affects vision in health and pathology.Brockmole and Logie (2013) present behavioral findings on the visual working memory (VWM) abilities of over 55,000 individuals, aged 8 and 75 years, who were studied on-line to provide an analysis of age-related change in VWM.The results showed that VWM varies over the lifespan, peaking at age 20, to be followed by a sharp linear decline.By the age 55 years, adults possess poorer immediate visual memory than 8 and 9 year olds.Allard et al. (2013) present their work on age-related deficits on second-order motion processing at all temporal frequencies including the ones for which no age-related effect on first-order motion processing has so far been observed.They conclude that aging affects the ability to track moving features.Van der Stigchel et al. (2013) tested four macular degeneration (MD) patients in a visual search paradigm and contrasted their performance with that of healthy controls with and without a simulated scotoma.Saccadic search latencies for the MD group were significantly longer in both conditions compared to controls.Legault et al. (2013) report the results of a study on the capacity of older participants to improve their tracking-speed thresholds in a dynamic, virtual reality environment.Their results show that this capacity is significantly affected by healthy aging but that perceptual-cognitive training can significantly reduce age-related effects in older individuals.Bower et al. (2013) show how perceptual learning can improve motion discrimination for older, compared to younger, individuals under high-and lowcontrast conditions.Both older and younger subjects exhibited lower duration thresholds after training.Graham et al. (2013) focus on patients with early Alzheimer's dementia with respect to the temporal stability of their aesthetic judgments of paintings.They find that the stability of aesthetic judgments for portrait paintings, landscape paintings, and landscape photographs is not different from those of controls, whereas the aesthetic stability for portrait photographs was significantly impaired in the AD group.Hamel et al. (2013) use a driving simulator paradigm with eye-and head-movement recordings in young and old subjects to assess age-related changes in visual exploratory behavior.No significant age effects were found regarding saccadic parameters.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".