The development of Internal noise
Bibliographic record
Abstract
Most behavioral visual development studies have focused on cortical processes, without concurrently investigating pre-cortical function. This study used a novel internal noise paradigm (Silvestre et al., 2018) to estimate calculation efficiency and equivalent input noise (EIN) due to either the amount of light detected by photoreceptors (i.e. photon noise) or internal noise occurring at a cortical level for both static and dynamic information at different developmental periods. Thirteen children (11-13 years, mean= 11.9), fifteen adolescents (14-17 years, mean= 15.6) and fourteen adults (19-39 years, mean= 25.5) participated in this study. All participants completed a 2AFC task to measure contrast thresholds to drifting (2, 7.5, 15 and 30 Hz) and static gratings (0.5 cpd) with and without external noise for different luminance intensities (5-519 Td). The EIN associated with cortical noise significantly differed between the children and adults for the dynamic detection task (p<.05), but not for the static detection task; photon noise did not significantly differ between the children and adults. Calculation efficiency significantly differed between the children and adults (p<.05) for both detection tasks. Regarding the EIN associated with the amount of light detected by photoreceptors, the photon noise reached adultlike levels for the children. However, the calculation efficiency for both detection tasks was significantly lower for the children compared to adults. These results suggest that the cortical noise limiting the processing of a detection task reached adult-like levels for the children for static stimuli, but not for dynamic stimuli, which reached maturity during adolescence and that the maturity of the visual system is reached earlier at the photoreceptor level than at the cortical level.
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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 | 0.000 | 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 | 0.000 |
| 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".