Technology

Speaking which is conveying thought, also far exceed 10 bits per second.

There was a study in 2019 that analyzed 17 different spoken languages to analyze how languages with lower complexity rate (bits of information per syllable) tend to be spoken faster in a way that information rate is roughly the same across spoken languages, at roughly 39 bits per second.

Of course, it could be that the actual ideas and information in that speech is inefficiently encoded so that the actual bits of entropy are being communicated slower than 39 per second. I'm curious to know what the underlying Caltech paper linked says about language processing, since the press release describes deriving the 10 bits from studies analyzing how people read and write (as well as studies of people playing video games or solving Rubik's cubes). Are they including the additional overhead of processing that information into new knowledge or insights? Are they defining the entropy of human language with a higher implied compression ratio?

EDIT: I read the preprint, available here. It purports to measure externally measurable output of human behavior. That's an important limitation in that it's not trying to measure internal richness in unobserved thought.

So it analyzes people performing external tasks, including typing and speech with an assumed entropy of about 5 bits per English word. A 120 wpm typing speed therefore translates to 600 bits per minute, or 10 bits per second. A 160 wpm speaking speed translates to 13 bits/s.

The calculated bits of information are especially interesting for the other tasks (blindfolded Rubik's cube solving, memory contests).

It also explicitly cited the 39 bits/s study that I linked as being within the general range, because the actual meat of the paper is analyzing how the human brain brings 10^9 bits of sensory perception down 9 orders of magnitude. If it turns out to be 8.5 orders of magnitude, that doesn't really change the result.

There's also a whole section addressing criticisms of the 10 bit/s number. It argues that claims of photographic memory tend to actually break down into longer periods of study (e.g., 45 minute flyover of Rome to recognize and recreate 1000 buildings of 1000 architectural styles translates into 4 bits/s of memorization). And it argues that the human brain tends to trick itself into perceiving a much higher complexity that it is actually processing (known as "subjective inflation"), implicitly arguing that a lot of that is actually lossy compression that fills in fake details from what it assumes is consistent with the portions actually perceived, and that the observed bitrate from other experiments might not properly categorize the bits of entropy involved in less accurate shortcuts taken by the brain.

I still think visual processing seems to be faster than 10, but I'm now persuaded that it's within an order of magnitude.

You may be misunderstanding the bit measure here. It’s not ten bits of information, basically a single byte. It’s ten binary yes/no decisions to equal the evaluation of 1024 distinct possibilities.

The measure comes from information theory but it is easy to confuse it with other uses of ‘bits’.

Right? They do nothing to expand upon what this plainly wrong claim is supposed to actually mean. Goddamn scientists need a marketing department of their own, because the media sphere in general sells their goods however the fuck they feel like packaging them.

You're misunderstanding the terminology used then.

In information theory, "bit" doesn't mean "bitrate" like you'd see in networks, but something closer to "compressed bitrate."

For example, let's say I build a computer that only computes small sums, where the input is two positive numbers from 0-127. However, this computer only understands spoken French, and it will ignore anything that's not a French number in that range. Information theory would say this machine receives 14 bits of information (two 7-bit numbers) and returns 8 bits. The extra processing of understanding French is waste and ignored for the purposes of calculating entropy.

The article also mentions that our brains take in billions of bits of sensory data, but that's ignored for the calculation because we only care about the thought process (the useful computation), not all of the overhead of the system.

Indeed it is. If you want to illustrate the point that silicon and copper are faster than bioelectric lumps of fat there are lots of ways to do this and it's no contest, but this is not a well done study.