Predictions benefit performance in dynamic search across the adult lifespan
Bibliographic record
Abstract
Visual search is a common cognitive task that requires us to select relevant targets while ignoring irrelevant distractors. Changes attributed to developmental trajectories of executive control, distraction sensitivity, and perceptual capacities all contribute to a characteristic decline in search performance with age between younger and older adults. Traditional studies of visual search rely heavily on tasks using static search displays. However, outside the laboratory, visual search typically occurs in dynamic settings with stimuli coming in and out of our visual field. Furthermore, everyday dynamic search settings – such as crossing a busy road – often contain spatiotemporal regularities that afford anticipation of task-relevant events (e.g., predicting when a pedestrian light will change). We investigated whether individuals across the adult lifespan utilise predictive regularities to optimise visual-search performance in extended dynamic contexts. Participants (N=300; between 20 and 80 years old) performed a dynamic visual search task in which eight targets faded in and out among distractors over extended trials. Half of the targets appeared at fixed temporal onsets and spatial quadrants throughout the experiment. We estimated the behavioural benefits conferred by learned spatiotemporal regularities of targets and by the mounting temporal probability of targets appearing over time. Overall search performance decreased with advancing age. However, the behavioural benefits of predictions were preserved. Longer time intervals between sequential targets resulted in faster responses across all participant ages. The benefits related to memory-based predictions and to intervals between targets interacted with age. Benefits for memory-related target predictability became more pronounced for lengthier intervals between targets over with advancing age. While younger participants benefited from memory-related predictions from short inter-target intervals, older participants benefited primarily when the intervals were longer. Despite a generic decline in dynamic visual-search performance with ageing, we identified a striking preserved ability to extract and utilise regularities to guide behaviour.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".