Aging and the rate of visual information processing
Bibliographic record
Abstract
Multiple methods exist for measuring how age influences the rate of visual information processing. The most advanced methods model the processing dynamics in a task in order to estimate processing rates independently of other factors that might be influenced by age, such as overall performance level and the time at which processing onsets. However, such modeling techniques have produced mixed evidence for age effects. Using a time-accuracy function (TAF) analysis, Kliegl, Mayr, and Krampe (1994) showed clear evidence for age effects on processing rate. In contrast, using the diffusion model to examine the dynamics of decision processes, Ratcliff and colleagues (e.g., Ratcliff, Thapar, & McKoon, 2006) found no evidence for age effects on processing rate across a range of tasks. Examination of these studies suggests that the number of display stimuli might account for the different findings. In three experiments we measured the precision of younger and older adults' representations of target stimuli after different amounts of stimulus exposure. A TAF analysis found little evidence for age differences in processing rate when a single stimulus was presented (Experiment 1). However, adding three nontargets to the display resulted in age-related slowing of processing (Experiment 2). Similar slowing was observed when simply presenting two stimuli and using a post-cue to indicate the target (Experiment 3). Although there was some interference from distracting objects and from previous responses, these age-related effects on processing rate seem to reflect an age-related difficulty in processing multiple objects, particularly when encoding them into visual working memory.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".