The hope is that this same mechanism which seems well suited for handling imprinting also works for grounding sexual attraction (as an elaboration of imprinting) and then more complex concepts like representations of other’s emotions from facial expression, vocal tone, etc proxies, and then combining that with empathic simulation to ground a model of other’s values/utility for social game theory, altruism, etc.
Yes, that is my hope too! And the main thing I’m working on most days is trying to flesh out the details.
I do agree the amygdala does seem like a good fit for the location of the learned symbol circuit, although at that point it raises the question of why not also just have the proxy in the amygdala? If the amygdala has the required inputs from LGN and/or V1 it would be my guess that it could also just colocate the innate proxy circuit. (I haven’t looked in the lit to see if those connections exist)
For example, I claim that all the vision-related inputs to the amygdala have at some point passed through at least one locally-random filter stage (cf. “pattern separation” in neuro literature or “compressed sensing” in DSP literature). That’s perfectly fine if the amygdala is just going to use those inputs as feedstock for an SL model. SL models don’t need to know a priori which input neuron is representing which object-level pattern, because it’s going to learn the connections, so if there’s some randomness involved, it’s fine. But the randomness would be a very big problem if the amygdala needs to use those input signals to calculate a ground-truth proxy.
As another example, a ground-truth proxy requires zero adjustable parameters (because how would you adjust them?), whereas a learning algorithm does well with as many adjustable parameters as possible, more or less.
So I see these as very different algorithmic tasks—so different that I would expect them to wind up in different parts of the brain, just on general principles.
The amygdala is a hodgepodge grouping of nuclei, some of which are “really” (embryologically & evolutionarily) part of the cortex, and the rest of which are “really” part of the striatum (ref). So if we’re going to say that the cortex and striatum are dedicated to running within-lifetime learning algorithms (which I do say), then we should expect the amygdala to be in that same category too.
By contrast, SC is in the brainstem, and if you go far enough back, SC is supposedly a cousin of the part of the pre-vertebrate (e.g. amphioxus) nervous system that implements a simple “escape circuit” by triggering swimming when it detects a shadow—in other words, a part of the brain that triggers an innate reaction based on a “hardcoded” type of pattern in visual input. So it would make sense to say that the SC is still more-or-less doing those same types of calculations.
Yes, that is my hope too! And the main thing I’m working on most days is trying to flesh out the details.
For example, I claim that all the vision-related inputs to the amygdala have at some point passed through at least one locally-random filter stage (cf. “pattern separation” in neuro literature or “compressed sensing” in DSP literature). That’s perfectly fine if the amygdala is just going to use those inputs as feedstock for an SL model. SL models don’t need to know a priori which input neuron is representing which object-level pattern, because it’s going to learn the connections, so if there’s some randomness involved, it’s fine. But the randomness would be a very big problem if the amygdala needs to use those input signals to calculate a ground-truth proxy.
As another example, a ground-truth proxy requires zero adjustable parameters (because how would you adjust them?), whereas a learning algorithm does well with as many adjustable parameters as possible, more or less.
So I see these as very different algorithmic tasks—so different that I would expect them to wind up in different parts of the brain, just on general principles.
The amygdala is a hodgepodge grouping of nuclei, some of which are “really” (embryologically & evolutionarily) part of the cortex, and the rest of which are “really” part of the striatum (ref). So if we’re going to say that the cortex and striatum are dedicated to running within-lifetime learning algorithms (which I do say), then we should expect the amygdala to be in that same category too.
By contrast, SC is in the brainstem, and if you go far enough back, SC is supposedly a cousin of the part of the pre-vertebrate (e.g. amphioxus) nervous system that implements a simple “escape circuit” by triggering swimming when it detects a shadow—in other words, a part of the brain that triggers an innate reaction based on a “hardcoded” type of pattern in visual input. So it would make sense to say that the SC is still more-or-less doing those same types of calculations.