Author response: A neural basis for the spatial suppression of visual motion perception
Bibliographic record
Abstract
People usually find it easier to see things when they are big and bright, but there are occasionally exceptions. One example concerns moving objects: when they are small, we can identify their direction of motion easily, but this becomes much more difficult for larger objects. This decreased perceptual sensitivity appears to be linked to other mental processes. For example, studies have suggested that people with high IQs have more difficulty perceiving the motion of large objects, whereas people with various psychiatric disorders, such as schizophrenia, are better able to see such movement. Although several theories have been proposed, there is currently no good explanation for these findings. Liu et al. set out to determine why the part of the brain that is responsible for vision (the visual cortex) fails to register the direction of large moving objects and how this failure might relate to mental function in general. To do this, Liu et al. trained monkeys to report which direction different sized stimuli were moving on a screen. The electrical activity of nerve cells in the part of the visual cortex that deals with movement was recorded while the monkeys performed this task. The results of the experiments revealed that, on average, these cells responded strongly to large moving stimuli, even though the monkeys had trouble seeing their motion. However, nerve cells are “noisy” – they respond a bit differently every time they are presented with the same stimulus – and this noise was stronger for larger stimuli. By studying the mathematical relationship between the noise and what the animals perceived, Liu et al. found that the visual cortex attempts to suppress the noise and in the process often shuts off the responses to large stimuli entirely. This suppression is likely to cause the movement of large stimuli to be poorly perceived. If suppressing this kind of noise is really responsible for failures in perceiving motion, then this mechanism could also explain the connection between motion perception and other mental processes. Liu et al. are currently testing this idea.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.007 | 0.003 |
| Insufficient payload (model declined to judge) | 0.076 | 0.021 |
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".