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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".