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Nocturne trailer (Apple TV+, Zazie Beetz): where the predicted brain response peaks
The predicted brain response to the Nocturne trailer with Zazie Beetz peaks at 1:39, and the auditory system leads it. We ran the trailer through v2neuro, our brain-response model, and read the curve second by second. Scan your own video free, no card, at neuro-viral.com. Nocturne is an Apple TV+ series.
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The peak lands at 1:39, led by the auditory system
The strongest predicted cortical response in the 150-second scan sits at 1:39 (t=99s), at 0.82 on the scale we use across the site. The auditory system leads it: at that second the auditory network is the model's top-ranked system.
The single word the model hears at 1:39 is "you", mid-sentence. We only have the audio and the scan's structural data, not a view of the screen, so that is as far as the transcript goes. The peak is a model prediction of an average cortical response, not a measurement of anyone watching.
The response does not hold at that level for long. In the second right after the peak, 1:39 to 1:40 (t=99 to t=100), the predicted response drops from 0.82 to 0.54.
The first three seconds sit below the clip's own median
The scan runs for all 150 seconds of the trailer, one timestep per second. Across those 150 seconds the median predicted response is 0.52.
The opening three seconds sit under that median, at 0.35, 0.33 and 0.38. The predicted response starts low relative to the rest of the clip before it climbs.
The steepest fall runs from 1:30 to 1:33
The biggest three-second drop anywhere in the scan runs from 1:30 to 1:33 (t=90 to t=93), where the predicted response falls from 0.69 to 0.23.
That fall sits just ahead of the peak at 1:39. The curve drops into its sharpest low, then rises to its highest point six seconds later.
The auditory system does most of the work
Across the 150 seconds, the auditory system is the leading network for about a quarter of the clip (38 of the 150 seconds), more often than any other system. The 1:39 peak is the clearest case: the auditory system is the leading network at that second, the moment the whole scan reaches its highest point.
This is what v2neuro reports from the audio and the structure of the trailer. It predicts a group-average cortical response to the video. It does not read any individual viewer, and it is a model prediction, not a measurement.
What an editor can take from this scan
The useful read here is the shape of the curve, not a verdict on the trailer. The predicted response starts under the clip's median in the first three seconds, falls to its sharpest low from 1:30 to 1:33, then reaches its single highest point at 1:39 before dropping again in the next second.
If you want the same read on your own cut, you can run it through the model and see where its predicted brain response peaks, which system leads each second, and where the curve falls.
Frequently asked questions
Does the model know what is happening on the screen of the Nocturne trailer?
No. v2neuro works from the audio and the structural data of the clip. It does not see the video or know the scene, and this write-up never describes what is on screen.
When does the predicted brain response peak, and which system leads it?
The predicted response peaks at 1:39 (t=99s), at 0.82 on the scale used across the site, led by the auditory system. The auditory system is the top-ranked network at that second.
What does the predicted response do in the first three seconds?
It sits below the clip's own median. The first three seconds read 0.35, 0.33 and 0.38, against a median of 0.52 across all 150 seconds.
Where is the steepest fall in the scan?
The biggest three-second drop runs from 1:30 to 1:33 (t=90 to t=93), where the predicted response falls from 0.69 to 0.23. There is also a sharp single-second drop right after the peak, from 0.82 to 0.54 between 1:39 and 1:40.
What does this scan actually predict?
It is a predicted cortical response to the video, second by second, from v2neuro. It is a model prediction of an average cortical response, not a measurement of any individual viewer.
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