if the upstream circuit learns entirely from scratch, you can’t really have hardwired downstream predictors, for lack of anything stable to hardwire them to. I don’t see a clear argument for the premise.
consider the following hilariously oversimplified sketch of how to have hardwired predictors in an otherwise mainly-learning-from-scratch circuit…
I don’t have strong a priori opposition to this (if I understand it correctly), although I happen to think that it’s not how any part of the brain works.
If it were true, it would mean that “learning from scratch” is wrong, but not in a really significant way that impacts the big-picture lessons I draw from it. There still needs to be a learning algorithm. There still needs to be supervisory signals. It’s still the case that the eventual meaning of any particular neuron in this learning system is unreliable from the genome’s perspective. There still needs to be a separate non-learning sensory processing system if we want specific instinctual reactions with specific sensory triggers that are stable throughout life. It’s still the case that 99.99…% of the bits of information in the adult network coming from the learning algorithm rather than the genome. Etc. Basically everything of significance that I’ll be talking about in the series would still be OK, I think. (And, again, assuming that I’m understanding you correctly.)
Alternatively: you take a short-term predictor. You hardcode some of its initial parameters to embed a hardwired flinching system, as above.
This works to an extent at t=0, while still getting better and adapting over time.
Thinking about this, I recall a possible example in the fruit fly, where Li et al. 2020 found that there were so-called “atypical” MBONs (= predictor output neurons) that received input not only from dopamine supervisors, and inputs from randomly-pattern-separated Kenyon Cell context signals, but also inputs from various other signals in the brain—signals which (I think) do not pass through any randomizing pattern-separator.
If so, and if those connections are playing a “context” role (as opposed to being supervisory signals or real-time hyperparameter settings—I’m not sure which it is), then they could in principle allow the MBONs to do a bit better than chance from birth.
My comments above also apply here—in the event that this is true (which I’d still bet against, at least in the human case), it wouldn’t impact anything of significance for the series, I think.
Fair! There are many plausible models that the human brain isn’t.
My comments above also apply here—in the event that this is true (which I’d still bet against, at least in the human case), it wouldn’t impact anything of significance for the series, I think.
I haven’t seen much of anything (beyond the obvious) that said sketch explicitly contradicts, I agree.
I realize now that I probably should have explained the why (as opposed to the what)of my sketch a little better[1].
Your model makes a fair bit of intuitive sense to me; your model has an immediately-obvious[2] potentialflaw/gap of “if only the hypothalamus and brainstem are hardwired and the rest learns from scratch, how does that explain <insert innately-present behavior here>[3]”, that you do acknowledge but largely gloss over.
My sketch (which is really more of a minor augmentation—as you say it coexists with the rest of this fairly well) can explain that sort of thing[4], with a bunch of implications that seem intuitively plausible[5].
I find myself skeptical of treating e.g. the behavior of Virginia opossum newborns[6] as either solely driven by the hypothalamus and brainstem[7] (“newborn opossums climb up into the female opossum’s pouch and latch onto one of her 13 teats.”, especially when combined with “the average litter size is 8–9 infants”) or learnt from scratch (among other things, gestation lasts 11–13 days).
E.g. if everyone starts with the same basic rudimentary audio and video processing and learns from there I would expect people to have closer audio and video processing ‘algorithms’ than might be expected if everything was learnt from scratch—and indeed see e.g. Bouba/Kiki[8].
I find myself skeptical of treating e.g. the behavior of Virginia opossum newborns as either solely driven by the hypothalamus and brainstem (“newborn opossums climb up into the female opossum’s pouch and latch onto one of her 13 teats.”, especially when combined with “the average litter size is 8–9 infants”) or learnt from scratch (among other things, gestation lasts 11–13 days).
Hmm. Why don’t you think that behavior might be solely driven by the hypothalamus & brainstem?
For what it’s worth, decorticate infant rats (rats whose cortex was surgically removed [yikes]) “appear to suckle normally” according to Carter, Witt, Kolb, Whishaw 1982. That’s not definitive evidence (decortication is only a subset of the hypothetical de-Learning-Subsystem-ification) but I find it suggestive, at least in conjunction with other things I know about the brainstem.
Which shows up even in 4-month-olds. (Though note n=12 with 13 excluded from the study...)
As I noted in Post #2, “even a 3-month-old infant has had 4 million waking seconds of “training data” to learn from”. That makes it hard to rule out learning, or at least it’s hard in the absence of additional arguments, I think.
Why don’t you think that behavior might be solely driven by the hypothalamus & brainstem?
I tend to treat hypothalamus & brainstem reactions as limited to a single rote set of (possibly-repetitive) motions driven by a single clear stimulus. The sort of thing that I could write a bit of Python-esque pseudocode for.
Withdrawal reflexes match that. Hormonal systems match that[1]. Blink reflex matches that. Suckling matches that. Pathfinding from point A to any of points B-Z in the presence of dynamic obstacles, properly orienting, then suckling? Not so much...
(That being said, this is not my area of expertise.)
As I noted in Post #2, “even a 3-month-old infant has had 4 million waking seconds of “training data” to learn from”. That makes it hard to rule out learning, or at least it’s hard in the absence of additional arguments, I think.
On the one hand, fair.
On the other hand, one of the main interesting points about the Bouba/Kiki effect is that it appears to be a human universal[2]. I’d consider it unlikely[3] that there’s enough shared training data across a bunch of 3-month-olds to bias them towards said effect[4][5][6].
I’m not 100% sure and didn’t chase down the reference, but in context, I believe the claim “the [infant decorticate rats] appear to suckle normally and develop into healthy adult rats” should be read as “they find their way to their mother’s nipple and suckle”, not just “they suckle when their mouth is already in position”.
Pathfinding to a nipple doesn’t need to be “pathfinding” per se, it could potentially be as simple as moving up an odor gradient, and randomly reorienting when hitting an obstacle. I dunno, I tried watching a couple videos of neonatal mice suckling their mothers (1,2) and asking myself “could I write python-esque pseudocode that performed as well as that?” and my answer was “yeah probably, ¯\_(ツ)_/¯”. (Granted, this is not a very scientific approach.)
“Shared training data” includes not only the laws of physics but also the possession of a human brain and body. For example, I might speculate that both sharp objects and “sharp” noises are causes of unpleasantness thanks to our innate brainstem circuits, and all humans have those circuits, therefore all humans might have a shared tendency to give similar answers to the bouba/kiki thing. Or even if that specific story is wrong, I can imagine that something vaguely like that might be responsible.
Thanks for the great comment!
That would be Post #2 :-)
I don’t have strong a priori opposition to this (if I understand it correctly), although I happen to think that it’s not how any part of the brain works.
If it were true, it would mean that “learning from scratch” is wrong, but not in a really significant way that impacts the big-picture lessons I draw from it. There still needs to be a learning algorithm. There still needs to be supervisory signals. It’s still the case that the eventual meaning of any particular neuron in this learning system is unreliable from the genome’s perspective. There still needs to be a separate non-learning sensory processing system if we want specific instinctual reactions with specific sensory triggers that are stable throughout life. It’s still the case that 99.99…% of the bits of information in the adult network coming from the learning algorithm rather than the genome. Etc. Basically everything of significance that I’ll be talking about in the series would still be OK, I think. (And, again, assuming that I’m understanding you correctly.)
Thinking about this, I recall a possible example in the fruit fly, where Li et al. 2020 found that there were so-called “atypical” MBONs (= predictor output neurons) that received input not only from dopamine supervisors, and inputs from randomly-pattern-separated Kenyon Cell context signals, but also inputs from various other signals in the brain—signals which (I think) do not pass through any randomizing pattern-separator.
If so, and if those connections are playing a “context” role (as opposed to being supervisory signals or real-time hyperparameter settings—I’m not sure which it is), then they could in principle allow the MBONs to do a bit better than chance from birth.
My comments above also apply here—in the event that this is true (which I’d still bet against, at least in the human case), it wouldn’t impact anything of significance for the series, I think.
Fair! There are many plausible models that the human brain isn’t.
I haven’t seen much of anything (beyond the obvious) that said sketch explicitly contradicts, I agree.
I realize now that I probably should have explained the why (as opposed to the what) of my sketch a little better[1].
Your model makes a fair bit of intuitive sense to me; your model has an immediately-obvious[2] potential flaw/gap of “if only the hypothalamus and brainstem are hardwired and the rest learns from scratch, how does that explain <insert innately-present behavior here>[3]”, that you do acknowledge but largely gloss over.
My sketch (which is really more of a minor augmentation—as you say it coexists with the rest of this fairly well) can explain that sort of thing[4], with a bunch of implications that seem intuitively plausible[5].
Read: at all.
I mean, it was to me? Then again I find myself questioning my calibration of obviousness more and more...
I find myself skeptical of treating e.g. the behavior of Virginia opossum newborns[6] as either solely driven by the hypothalamus and brainstem[7] (“newborn opossums climb up into the female opossum’s pouch and latch onto one of her 13 teats.”, especially when combined with “the average litter size is 8–9 infants”) or learnt from scratch (among other things, gestation lasts 11–13 days).
And, in general, can explain behaviors ‘on the spectrum’ from fully-innate to fully-learnt. Which, to be fair, is in some ways a strike against it.
E.g. if everyone starts with the same basic rudimentary audio and video processing and learns from there I would expect people to have closer audio and video processing ‘algorithms’ than might be expected if everything was learnt from scratch—and indeed see e.g. Bouba/Kiki[8].
Admittedly, found after a quick search on Wikipedia for short gestation periods.
This is a testable hypothesis I suppose. Although likely not one that will ever be tested for various reasons.
Which shows up even in 4-month-olds. (Though note n=12 with 13 excluded from the study...)
Hmm. Why don’t you think that behavior might be solely driven by the hypothalamus & brainstem?
For what it’s worth, decorticate infant rats (rats whose cortex was surgically removed [yikes]) “appear to suckle normally” according to Carter, Witt, Kolb, Whishaw 1982. That’s not definitive evidence (decortication is only a subset of the hypothetical de-Learning-Subsystem-ification) but I find it suggestive, at least in conjunction with other things I know about the brainstem.
As I noted in Post #2, “even a 3-month-old infant has had 4 million waking seconds of “training data” to learn from”. That makes it hard to rule out learning, or at least it’s hard in the absence of additional arguments, I think.
I tend to treat hypothalamus & brainstem reactions as limited to a single rote set of (possibly-repetitive) motions driven by a single clear stimulus. The sort of thing that I could write a bit of Python-esque pseudocode for.
Withdrawal reflexes match that. Hormonal systems match that[1]. Blink reflex matches that. Suckling matches that. Pathfinding from point A to any of points B-Z in the presence of dynamic obstacles, properly orienting, then suckling? Not so much...
(That being said, this is not my area of expertise.)
On the one hand, fair.
On the other hand, one of the main interesting points about the Bouba/Kiki effect is that it appears to be a human universal[2]. I’d consider it unlikely[3] that there’s enough shared training data across a bunch of 3-month-olds to bias them towards said effect[4][5][6].
From what I’ve seen, anyway. I haven’t spent too too much time digging into details here.
Or as close as anything psychological ever gets to a human universal, at least.
Though not impossible. See also the mouth-shape hypothesis for the Bouba/Kiki effect.
Obvious caveat is obvious: said study did not test 3-month-olds across a range of cultures.
There’s commonalities in e.g. the laws of Physics, of course.
Arguably, this is “additional arguments”.
I’m not 100% sure and didn’t chase down the reference, but in context, I believe the claim “the [infant decorticate rats] appear to suckle normally and develop into healthy adult rats” should be read as “they find their way to their mother’s nipple and suckle”, not just “they suckle when their mouth is already in position”.
Pathfinding to a nipple doesn’t need to be “pathfinding” per se, it could potentially be as simple as moving up an odor gradient, and randomly reorienting when hitting an obstacle. I dunno, I tried watching a couple videos of neonatal mice suckling their mothers (1,2) and asking myself “could I write python-esque pseudocode that performed as well as that?” and my answer was “yeah probably, ¯\_(ツ)_/¯”. (Granted, this is not a very scientific approach.)
“Shared training data” includes not only the laws of physics but also the possession of a human brain and body. For example, I might speculate that both sharp objects and “sharp” noises are causes of unpleasantness thanks to our innate brainstem circuits, and all humans have those circuits, therefore all humans might have a shared tendency to give similar answers to the bouba/kiki thing. Or even if that specific story is wrong, I can imagine that something vaguely like that might be responsible.
Alright, I see what you’re saying now. Thanks for the conversation!