The temporal profile of visual information sampling and integration
Bibliographic record
Abstract
While intuition suggests that visual information sampling through time is continuous, some have argued instead that sampling occurs in temporally discrete moments (VanRullen, & Koch, 2003). Of related interest is the question of how visual information is integrated through time. For example, is the information simply summed? We attempted to clarify the nature of visual information sampling and integration through time using a temporal response classification approach. Five subjects were asked to decide which of two movies, presented successively at the center of the screen, was the brightest. Each movie consisted in a sequence of 30 Gaussian blobs (200 ms) of different contrasts and subtending one degree of visual angle. A patch of spatial bit noise displayed through a Gaussian aperture was presented for 200 ms at the movies' location immediately before and after each movie. The contrast of the Gaussian blobs varied randomly through time. Specifically, on each trial, both movies had the same average and maximum contrast values across their temporal extent, but they differed in the temporal distribution of the contrasts. Thus, the brightness decision could only be influenced by the interaction between the participant's sampling/integration profile and the temporal sequence of contrasts in the stimuli. The sequence of contrasts that “optimally” led to a bright percept was computed for each participant by performing multiple regressions on the contrast temporal sequences and the participant's decisions. Three participants out of five showed a clear oscillation in their information sampling function (ranging between 5 and 15 Hz), and a linear decrease of information intake through time; the other participants reported being incapable of performing the task. Our results support the hypothesis that the visual system samples information in a discrete manner. They also indicate that the weight given to the information sampled decreases as information accumulates.
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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 | 0.011 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".