Trained Older Observers Are Equivalent to Untrained Young Adults for 3D Multiple-Object-Tracking Speed Thresholds
Bibliographic record
Abstract
There is ample evidence that the normal aging process affects visual perceptual processing. This is particularly true when images or scenes are more complex (Faubert, 2002). For instance, it has been demonstrated that older observers are less sensitive to higher-order visual information (Habak & Faubert, 2000; Herbert et al., 2002). A perceptual-cognitive task of particular relevance is multiple object tracking or MOT (Pylyshyn, 1989), which has been shown to be less efficient with aging (Sekuler et al., 2008; Trick et al., 2005). MOT is a task where the observer is required to simultaneously track multiple elements among many and the ability of the observer is evaluated by the number of elements that the observer can track without making a mistake. A question remains as to whether older observers can be trained to regain this age-related loss. Such regain has been demonstrated for other visual perceptual tasks such as the “useful field of view” a technique that requires dual processing (Richards et al., 2006). We evaluated the performance of older and younger observers (speed thresholds) in a 3D virtual environment and demonstrated that indeed older observers were less efficient at MOT. However, after several weeks of training, the older group performed as well as the untrained younger group. This is encouraging given that most of us are required to process multiple moving elements in our real world (e.g. tracking people in crowds, sports, driving, etc.). Our results in conjunction with other studies demonstrates that the older brain remains plastic and training is a viable option for regaining certain perceptual-cognitive abilities that were lost by the normal aging process. Regaining such capacities may have an impact on individual confidence in performing daily activities and may consequently improve their general quality of life.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".