Characterizing the Mechanisms behind Improvements in Visual Sensitivity during Childhood
Bibliographic record
Abstract
The human visual system is not an ideal transmitter of information. A number of separate, quantifiable factors, such as internal noise (Barlow, 1956; Pelli, 1981), have been introduced to characterize what limits our visual sensitivity and how it changes as a result of attention (Lu & Dosher, 1998), training/learning (Li & Levi, 2004), or aging (Betts, Sekuler, & Bennett, 2007). In the current study, we used external noise to model the mechanisms underlying improvements in sensitivity to contrast during childhood. We measured the contrast thresholds of 5-year-olds, 7-year-olds, 9-year-olds, and adults (n = 20/age) in a two-alternative forced-choice orientation discrimination task using the quick-TvC method that adaptively varies the contrast of the signal at a number of levels of external noise (Lesmes, Jeon, Lu, & Dosher, 2006). Overall, contrast thresholds decreased over a wide range of external noise levels as age increased (mean optimal contrast: 8.7%, 5.1%, 4%, and 3.3% for 5-year-olds, 7-year-olds, 9-year-olds, and adults, respectively). A perceptual template model based on Dosher and Lu (1999) provided an excellent fit (r2 = 0.985) to the developmental changes in contrast thresholds at different levels of external noise and performance. The model suggested that a mixture of mechanisms underlie the changes: the improvements in contrast thresholds across ages were best modelled by a combination of reductions in internal additive noise (Aa), reductions in internal multiplicative noise (Am), and improved external noise exclusion (Af). Between 5 and 7 years of age, there were 40%, 70%, and 45% reductions in Aa, Am, and Af, respectively. The modelled improvements likely reflect developmental changes at cortical levels, rather than changes of front-end structural properties (Kiorpes, Tang, Hawken, & Movshon, 2003). Meeting abstract presented at VSS 2012
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".