TobyBartels comments on A Crash Course in the Neuroscience of Human Motivation - Less Wrong
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Comments (90)
Like everybody else, I think that this post is awesome.
One thing that bugs me. You write:
But the link there is to (the English Wikipedia's discussion of) compression functions in cryptography, which seem irrelevant. In particular, both dopamine rate and reward magnitude are ordered, and it seems that the function from the latter to the former should respect this order to be of any use; while cryptographic compression functions should thoroughly entangle the order (to the extent that it even makes sense to think of the domain and range as ordered sets at all) to make it difficult estimate the input (here reward magnitude) given only the output (here dopamine rate).
The best that I can think of that you might mean is that dopamine rate is a function of reward magnitude but this function is not one-to-one, so that different reward magnitudes (if sufficiently close) might be confounded into the same dopamine rate. (Thus one cannot determine the reward magnitude precisely knowing only the dopamine rate, but one can estimate it to a certain precision, unlike with a cryptographic compression function.) And even there, I don't see (in the surrounding discussion) why it's important that the function is not one-to-one.
The footnote in that sentence leads you to this short paper linked from my bibliography, which reads:
I'm sorry I don't have more time to go into this, hopefully the paper link helps.
Thanks, I don't know why I didn't follow the footnote.
But if I had, I would have added to my comment that the cited paper confirms my expectations. The function that they describe (as in your quoted paragraph) does preserve ordering, and seems to have nothing to do with the compressive functions described at Wikipedia. (The paper also doesn't use that term; the case-independent string ‘compr’ doesn't appear in it at all.)
But actually, the point of the article seems to be that the function from reward magnitude to dopamine rate varies with time, being renormalised from time to time to be most sensitive (literally, having highest derivative) at the most likely inputs, which I did not get from your post at all. But if I were an editor, wanting your post to best reflect the article without getting any longer, I'd suggest just changing ‘compressive function’ to ‘variable function’ and removing the irrelevant link.
Not that any of this should detract from your otherwise excellent and everywhere interest post!
Gack! I'm just completely wrong about this one. Thanks for reading so closely and correcting my mistake!
Upvoted.
You're welcome then!
Upvoted for reading closely and then reading a paper in the footnotes.