Using detection or identification paradigms when assessing visual development: Is a shift in paradigm necessary?
Bibliographic record
Abstract
Given the inherent difference in judgment required to complete visual detection and identification tasks, it is unknown whether task selection differentially affects visual performance as a function of development. The aim of the present study is therefore to systematically assess and contrast visual performance using these two types of paradigms in order to determine whether paradigm-contingent differences in performance exist across different periods of development. To do so, we assessed sensitivity to both luminance- and texture-defined stationary and dynamic gratings using both detection and identification paradigms. Results demonstrated a relatively unchanged pattern of performance from the school ages through adolescence, suggesting that sensitivity was not differentially affected by choice of paradigm as a function of development. However, when averaged across age groups, a paradigm-contingent difference in sensitivity was evidenced for dynamic, texture-defined gratings only; it was easier to detect the spatial location of the gratings compared with identifying the direction of their motion. Paradigm-contingent differences were not evidenced for luminance-defined stimuli (whether stationary or dynamic), or for stationary, texture-defined gratings. In general, visual performance measured using either detection or identification paradigms is comparable across ages, particularly when information is stationary and defined by more simple visual attributes, such as luminance. Therefore, the use of detection paradigms might be advantageous under most circumstances when assessing visual abilities of very young and/or clinical populations in order to minimize potential challenges not related to visual perception (i.e., attentional) in these populations. Finally, paradigm-contingent differences in performance specific to dynamic, texture-defined information will be discussed.
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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 | 0.004 | 0.011 |
| 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 | 0.001 | 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".