Classifying Mixed Percepts During Binocular Rivalry in Younger and Older Adults
Bibliographic record
Abstract
Several categories of mixed percepts can be seen during binocular rivalry, including the perception of both exclusive images overlapping, a mosaic comprising of pieces of each exclusive image, and a wave-like transition from one exclusive percept to the other (Yang et al., 1992). Recently, we demonstrated the overall proportion of mixed percepts decreases with aging (Beers et al., VSS 2013). However, it is unknown if all categories are affected by aging. To answer this question, we presented pairs of orthogonal, oblique sine wave gratings (diameter = 4.4; contrast level = 0.45) to fifteen younger (aged 18-26) and twenty older (aged 64-84) adults. On each trial, participants recorded each instance of a mixed percept category (overlapping, pieces, or wave-like) with a handheld button box. The total number of reported mixed percepts and the tallies for each category were analyzed. Older adults reported significantly fewer mixed percepts. Interestingly, the proportion of each category of mixed percept decreased at similar rates with aging. Eight participants from each age group returned for a second experiment in which stimulus size (diameter = 1.4 or 4.4) and contrast level (0.2 or 0.8), factors with well-known effects on characteristics of mixed percepts in younger adults, varied across trials. Overall both younger and older observers reported increased occurrences of mixed percepts at high compared to low contrast, primarily for the wave-like category. Younger adults reported fewer mixed percepts when viewing the smaller compared to the larger stimuli, consistent with previous results (Blake et al., 1992), a decrease primarily affected by the wave-like category. Older adults had significantly fewer instances of mixed percepts when viewing the small stimuli at low contrast, but the decrease was primarily affected by the pieces category. These classifications, and age-related differences, enhance our understanding of rivalry, as distinct neural mechanisms have been linked to specific categories. Meeting abstract presented at VSS 2016
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.000 |
| 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".