Averaging is a convenient fiction of neuroscience
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Averaging is a convenient fiction of neuroscience
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Re: Averaging is a convenient fiction of neuroscience
#2[deleted]
Re: Averaging is a convenient fiction of neuroscience
#3All models are convenient fictions. I heard a neuroscientist once describe averaging as a low-pass filter. People know it hides high-frequency dynamics. But unless you have a way to interpret the high-frequency signal, it looks an awful lot like noise.
Re: Averaging is a convenient fiction of neuroscience
#4All models are convenient fictions. I heard a neuroscientist once describe averaging as a low-pass filter. People know it hides high-frequency dynamics. But unless you have a way to interpret the high-frequency signal, it looks an awful lot like noise.
I think of summaries as the text equivalent of averaging. Some high frequency stuff you don’t want to loose in that case are things like proper names, specific dates, etc. In the face of such signal, you don’t want to average it out to a “him” and a “Monday”.
Re: Averaging is a convenient fiction of neuroscience
#5All models are convenient fictions. I heard a neuroscientist once describe averaging as a low-pass filter. People know it hides high-frequency dynamics. But unless you have a way to interpret the high-frequency signal, it looks an awful lot like noise.
My grad school research was with an NIH neuroscience lab studying low-level sensory processing that offered a fascinating perspective on what's really going on there! At least for the first few levels above the sense receptors in simpler animal models.
To oversimplify, you can interpret gamma-frequency activity as chunking up temporal sensory inputs into windows. The specific dynamics between excitatory and inhibitory populations in a region of the brain create a gating mechanism where only a fraction of the most stimulated excitatory neurons are able to fire, and therefore pass along a signal downstream, before broadly-tuned inhibitory feedback silences the whole population and the next gamma cycle begins. Information is transmitted deeper into the brain based on the population-level patterns of excitatory activity per brief gamma window, rather than being a simple rate encoding over longer periods of time.
Again, this is an oversimplification, not entirely correct, fails to take other activity into account etc etc, but I'm sharing it as an example of an extant model of brain activity that not only doesn't average out high-frequency dynamics, but explicitly relies on them in a complex nonlinear fashion to model neural activity at the population level at high temporal frequency in a natural way. And it's not completely abstract, you can relate it to observed population firing patterns in, e.g., insect olfactory processing, now the we have the hardware to make accurate high-frequency population recordings.
Re: Averaging is a convenient fiction of neuroscience
#6All models are convenient fictions. I heard a neuroscientist once describe averaging as a low-pass filter. People know it hides high-frequency dynamics. But unless you have a way to interpret the high-frequency signal, it looks an awful lot like noise.
It is a low-pass filter in the frequency domain with a roll-off that is not smooth. I quite like [1] as a quick reference.
https://www.analog.com/media/en/technical-documentation/dsp-...
Re: Averaging is a convenient fiction of neuroscience
#7I've become increasingly convinced that the idea of averaging is one of the biggest obstacles to understanding things... it contains the insidious trap of feeling/sounding "rigorous" and "quantitative" while making huge assumptions that are extremely inappropriate for most real world situations.
Once I started noticing this I can't stop seeing this almost everywhere- almost every news article, scientific paper, etc. will make clearly inappropriate inferences about a phenomenon based on the exact same mistake of confusing the average for a complete description of a distribution, or a more nuanced context.
Just a simple common example, is the popular myth that ancient people died of old age in their 30s, based on an "average life span of ~33 years" or such. In reality the modal life expectancy of adults (most common age of death other than 0) has been pretty stable in the 70s-80s range for most of human history- the low average was almost entirely due to infant mortality.
The above example is a case where thinking in terms of averages causes you to grossly misunderstand simple things, in a way that would be impossible even with basic common sense in a person that had never encountered the idea of math... yet it is a mistake you can reliably expect people in modern times to make.
Re: Averaging is a convenient fiction of neuroscience
#8Very interesting how measurement limitations drive scientific consensus.
The author portrays this as a major flaw in neuroscience, but it seems like a natural consequence of Newton's flaming laser sword; why theorize about something that you can't directly measure?
Re: Averaging is a convenient fiction of neuroscience
#9All models are convenient fictions. I heard a neuroscientist once describe averaging as a low-pass filter. People know it hides high-frequency dynamics. But unless you have a way to interpret the high-frequency signal, it looks an awful lot like noise.
I think of summaries as the text equivalent of averaging. Some high frequency stuff you don’t want to loose in that case are things like proper names, specific dates, etc. In the face of such signal, you don’t want to average it out to a “him” and a “Monday”.
That would be a median in your example no? A spurious average might be us thinking that the statistical mean word contains all vowels except for 'e', and that 'm' is twice as likely as the other most likely consonants.
Re: Averaging is a convenient fiction of neuroscience
#10All models are convenient fictions. I heard a neuroscientist once describe averaging as a low-pass filter. People know it hides high-frequency dynamics. But unless you have a way to interpret the high-frequency signal, it looks an awful lot like noise.
In physics the model we choose is based on the scale - as in the macro sense all quantum effects average out over the several sextillion atoms in, say, a wood screw.