Sensory transmission, rate of extraction and asymptotic performance in visual backward masking as a function of age, stimulus intensity and similarity
Bibliographic record
Abstract
Speed of visual information processing alters as we age. Using a visual backward masking (VBM) task, we have compared cohorts of various ages (10, 15, 20, 40 and 60 years of age) on three parameters of a two-stage model (Lagged-Accrual Model, LAM) presented by Muise, LeBlanc, Lavoie and Arsenault, 1991. Of particular interest were the duration (Tlag) of initial chance performance reflecting sensory transduction and transmission, the rate (theta) of central information accrual and level (alpha) of asymptotic performance. These parameters were shown to vary systematically as a function of age, similarity of stimulus set (CGOQ vs IOSX) and stimulus intensity (0.57, 0.70, 0.86, and 1.06 cd/m2). Surprisingly, speed of sensory processing was already at its fastest for the 10 year-olds. The rate of extraction was at a maximum at 15 years with a sharp deline for older subjects. Older subjects were less able to take advantage of enhanced information available in increased stimulus intensities and dissimilarities. Differential asymptotic performance in the youngest group as a function of intensity suggests attentional lapses. Results will also be presented that suggest increasing stimulus intensity may “normalize” parametric VBM performance as a function of age. Older subjects may simply need more intense or high contrast stimuli. A discussion follows that may be pertinent to the comparison of different clinical populations in early visual information processing within the context of VBM.
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 | 0.002 | 0.005 |
| 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".