Author response: Human intracranial recordings link suppressed transients rather than 'filling-in' to perceptual continuity across blinks
Bibliographic record
Abstract
The average person blinks once every few seconds, each time shutting off their view of the world for about a tenth of a second. Nevertheless, we rarely notice a blink. By contrast, we readily notice a single blank frame in a movie, even if the frame lasts far less than a blink. The fact that we do not usually notice our spontaneous blinks is a striking example of the discrepancy between the images we perceive versus the information that enters our eyes. This dissociation between the information that the eyes receive and what we perceive raises a number of questions. First, which brain areas represent the actual information from the eyes, and at what point do brain areas start to represent our subjective perception instead? Second, how does the brain "stabilize" our perception of vision despite the frequent interruptions that occur whenever we blink? In short, does the brain "fill in" the missing images or “edit out” the gaps? To answer these questions, Golan et al. turned to human patients who were undergoing a surgical procedure related to the treatment of epilepsy. In the course of such procedures, and strictly for diagnosis purposes, electrodes are temporarily placed directly on the surface of the brain – the cortex – making it possible to monitor the activity of individual cortical areas. Towards the back of the brain, where cortical processing of visual signals begins, neurons responded in a way that was consistent with the physical information the eye actually received rather than the perception of vision. Thus, neurons showed the same responses to easily seen blank frames in a movie as to unnoticeable blinks. However, as the signals streamed forward to down-stream brain regions involved in vision, neurons in successive areas were increasingly likely to distinguish between the perceptually visible blank frames versus the invisible blinks. Unexpectedly, Golan et al. found no evidence that the brain fills in the missing picture during blinks. Instead, it seems that the brain generates a continuous perception by actively "deleting" the brief neural signals that are turned on when our visual input has been shut off. The brain only does this for blinks but not for artificial interruptions – such as blank movie frames – which explains why we notice the latter but not the former. A future challenge will be to isolate the pathway that leads from the brain regions that generate blinks to the regions that deal with vision, and that enables us to tell blinks from blanks.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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 teacher head, 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".