Reducing contrast improves direction estimation at low speeds
Bibliographic record
Abstract
Under low visual contrast conditions, sensitivity to stimulus features is generally thought to be reduced. However, a number of studies have revealed complex deviations from this intuitive view. For example, Tadin et al. (2003) showed that direction discrimination of large, briefly presented, drifting gratings improves as contrast is reduced, and they interpreted their results as a perceptual correlate of the contrast dependence of surround-suppressed neurons in the visual cortex (Pack et al., 2005; Polat et al., 1998; Sceniak et al., 1999). Contrast dependence has also been demonstrated in cortical neurons for speed tuning (Pack et al., 2005; Livingstone & Conway, 2007), but no clear psychophysical correlate of this result has been found. Here, we investigated the ability of human subjects to estimate the direction of moving dot fields at a variety of spatial and temporal displacements under both low and high contrasts, and compared these results to neural responses recorded from cortical area MT of alert macaque monkeys under similar conditions. We observed that the estimation of motion direction depended both on the stimulus contrast and on the amount of spatial displacement undergone by the stimulus dots on each monitor refresh. Surprisingly, subjects were better at determining the motion direction of stimuli with small displacements at low contrast than at high contrast. For larger displacements this effect reversed. This result was mirrored in the activity of MT neurons. Additional experiments replicated the above interaction between contrast and spatial displacement for a variety of conditions, including those in which the mean luminance was matched between both contrast conditions. These data link contrast-adaptive responses in area MT with behavioral performance, and demonstrate that higher contrast is not better for motion direction processing at low speeds.
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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 | 0.002 |
| 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 | 0.001 | 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".