The effect of dot lifetime, dot size, & percent area covered by dots on motion coherence thresholds: Implications for diagnosing reading difficulties
Bibliographic record
Abstract
Purpose: Prior research has established that individuals with reading difficulties tend to perform more poorly than controls on tasks requiring them to identify the overall direction of motion within a random dot kinematogram (RDK). However, it is unclear whether the size of the difference in performance between individuals with reading difficulties and their ‘normal’ counterparts is a function of reading ability or the stimulus parameters used. In this investigation, we examine the degree to which the lifetime of dots, the size of dots, and the percent area covered by dots affect the ability to identify the direction of motion in normal individuals. Methods: Observers (N=7) were asked to indicate whether the global direction of the dots contained within a 3 degree square region was leftward or rightward. The dots within the RDK varied in terms of lifetime (16.7 or 33.3ms), size (1 or 2 pixels in diameter), and percent area covered (1 or 21%). Results: Changes in the parameters of the RDK affected subjects' ability to identify the direction of motion. When the lifetime of the dots was short and the percent area covered was high, observers had more difficulty in judging the overall direction of movement for larger than for smaller dots. In addition, when the lifetime of the dots was short and the dot size was large, observers had more difficulty in identifying the motion direction for higher than for lower percent area covered. In contrast, these effects disappeared when the lifetime of the dots was lengthened; the overall pattern of results suggests that increasing the lifetime of the dots increased the difficulty of the task. Conclusion: Motion coherence thresholds are dependent on the parameters used during testing. One implication of this investigation is that the specific combination of parameters used may facilitate or hinder the detection of differences between those with reading difficulties and controls.
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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 | 0.010 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".