Bibliographic record
Abstract
Although nearly one third of the Canadian population is projected to be over the age of 65 by the year 2030, we know relatively little about how aging affects brain function generally, let alone how aging affects visual perception. The current dissertation was conducted as part of a research programme designed to better characterize how aging affects visual perception. Older persons exhibit a variety of deficits for perception of complex visual forms. The perception of these complex forms-including everyday forms such as faces and objects-is subserved by low-level channels that are selective, or tuned, for the orientation and spatial frequency of luminance-defined contours in the visual scene. The bandwidth of these channels is inversely related to the amount of information that they can pass on to higher visual processes; narrowly-tuned channels are better. Single-cell physiological investigations of primates suggest that visual cortex neurons thought to subserve these channels exhibit broader tuning in senescence. If these channels become broadly-tuned in older aging, this could explain age-related deficits for complex form perception. In Chapters 2 and 3 of the current thesis, I measured the tuning of these channels in otherwise healthy, older humans using psychophysical masking techniques. In Chapters 4 and 5, I measured the average tuning of the neurons thought to underlie these channels in older human adults, physiologically, using electroencephalography (EEG). Despite the aforementioned reports of functional decline in senescent neurons, psychophysical and physiological orientation and spatial frequency tuning did not differ between younger and older adults. One explanation for this discrepancy is that there is a methodological issue in the single-cell primate literature wherein anesthetics interact with senescence to produce seemingly broader neural tuning. Another explanation is that older humans do have otherwise detuned neurons and channels, but are able to tune their neurons and channels by the action of consciousness, attention, or age-related compensatory brain reorganization.
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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 | 0.001 |
| 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) | 0.001 | 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".