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This has nothing to do with biology, it's an argument from signal processing, which is well-understood theory (Nyquist's theorem and so on). If an object oscillates at 1 MHz, and you take 1 million still frames per second, it will rest in the same place in every frame, and thus look static. In reality, such an object would look blurred to the human eye.(+) It's this kind of artifact that motion blur (to be more precise, low-pass filtering) can avoid. Edit: The article you linked to is very confused about some basic terminology. It equates response time artifacts of an LCD monitor that display sharp, digital images with motion blur. That's so wildly wrong I'm not even sure where to start. Maybe here: When displaying video, motion blur is a property of the source, response time one of the output device. (+) Edit 2: To expand on this, the human vision system integrates arriving photons over time, and this way implicitly behaves a lot like a low-pass filter. A low-pass filter is different from a fixed-rate shutter, which is what people mean when they say the eye doesn't have a fixed framerate. However, there is a limited temporal resolution. A more everyday example of this effect would be a dimmed LED. You can pulse an LED at 1 MHz, it will look dimmed, not blinking. But when filming/rendering this LED at 1 million still images per second, it will either be all on or all off, both of which are wrong (i.e., an artifact of your chosen shutter speed). |
>look blurred to the human eye
Ah but it has everything to do with biology. You are proposing a far too simple model for the signal processing actually at play. Unfortunately there is no clock going to the rods and cones, they simply fire signals off asynchronously and the timebase is reconstructed in your noggin. How would you go about filtering a few million samples all on their own timebases that are themselves not uniformly (or even periodically) sampled? It would be a truly awful approximation.