Simulating the effect of age-related neurobiological alterations (NBAs) on a first- and second-order orientation-identification task
Bibliographic record
Abstract
Introduction: With normal aging, various neurobiological alterations (NBAs) occur that cause subtle visuo-perceptual deficits. These deficits are generally greater for complex visual information. Identifying which NBA causes these deficits is difficult because such deficits are subtle and may be the result of various NBAs emerging more or less simultaneously. The goal of our study was to simulate various NBA using an artificial neural network to identify which one correlates best with psychophysical human aging findings. Methods: An artificial neural network learned to perform an orientation-identification task for simple, luminance-defined (first-order) and complex, texture-defined (second-order) stimuli. Habak & Faubert (2000) demonstrated that orientation-identification thresholds for a group of elderly observers (X=70.1 years) increased by 29 % for first-order stimuli and 77 % for second-order stimuli compared to young adults (X=23 years). Once the artificial neural network learned the tasks, various NBAs were simulated. Results: Simulations showed that several NBA hypotheses including synapse loss, myelin sheath degradation and degeneration of intra-cortical inhibition correlated with the psychophysical results for the two age groups tested by Habak & Faubert (2000). However, these hypotheses did not predict the same relative loss in performances for other age groups, such as the middle-aged and very elderly observers. Conclusion: The artificial neural networks were able to predict relative threshold increase reflecting different types of physiological changes during the aging process for two age groups. However, psychophysical data for other age groups are required to differentiate between NBAs.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.001 | 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".