The process of stereoscopic perception: A magnetoencephalographic study
Bibliographic record
Abstract
Introduction Binocular disparity is one of many clues to perceive depth. Random-dot stereograms (RDS) designed by Julesz [1] enables us to make visual stimuli that exclusively have binocular disparity as a depth clue. Many physiological studies were done about visual cortical neurons which respond to binocular disparity [2], but it is still not clear what large-scale activity of such neurons detects correspondent regions in both retinae and calculates amounts of disparity, which is considered to be necessary to perceive stereoscopic image. This study investigated magnetic fields evoked by visual stimuli including binocular disparities to elucidate macroscopic responses of visual cortical neurons in the process of stereoscopic perception. 2 Methods The brain magnetic fields were recorded by a 64channel whole cortex MEG system (NeuroSQUID Model-100, CTF Systems, Canada). The sampling rate of data acquisition was 625 Hz, and acquired data were filtered by a 40-Hz low-pass filter and
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".