The effect of aging on the spatial pooling of local orientation signals
Bibliographic record
Abstract
Pooling orientation information allows observers to perceive form and texture that extend beyond spatially-limited receptive fields. Additionally, pooling the activity of low-level units may help the visual system to overcome the effects of noise in individual mechanisms, and may be important for the integration of local orientation elements into contours (e.g., Wang & Hess, 2005), an ability that appears to be impaired among older observers (Roudaia et al., 2008). The current study used methods describe by Dakin (2001) to investigate whether the ability to pool orientation information across space declines with normal healthy aging. Nineteen younger (mean age=23) and 17 older (mean age=71) observers discriminated textures composed of 128 3-cpd Gabors (radius = 1 deg) that were positioned randomly within an annular field (inner and outer radii = 0.5 & 3.4 deg). The orientation of each Gabor was selected randomly from one of two Normal distributions with means of +/−M and a variance of s2. The task was to discriminate the mean orientation, and threshold (defined as 2M) was measured as a function of s2. Performance on this task depends on i) the accuracy with which the orientations of the texture elements are encoded, and ii) the efficiency with which information is pooled across elements. Hence, an effect of aging on local orientation coding or spatial pooling should alter the threshold-vs.-variance (TvV) curves. However, the TvV curves did not vary as a function of age (F(1,5)=1.49, p=0.22). This result extends previous studies showing that aging does not alter the perception of orientation for local contours (Betts et al., 2007; Delahunt et al., 2008; Govenlock et al., in press), and suggests that the spatial pooling of orientation is preserved in old age.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".