If we think about the human brain (loosely) as doing model-based reinforcement learning, and if different people have different genetically-determined reward functions,
To agree and expand on this—successful DL systems have a bunch of important hyperparameters, and many of these control balances between different learning sub-objectives and priors/regularizers. Any DL systems that use intrinsic motivation, and especially those that combine that with other paradigms like extrinsic reward reinforcement learning, tend to have a bunch of these hyperparams. The brain is driven by both complex instrinsic learning mechanisms (empowerment/curiosity, predictive learning, etc) and extrinsic reward reinforcement learning (pleasure, pain, hunger, thirst, sleep, etc), and so likely has many such hyperparams. The brain also seems to control learning schedules somewhat adaptively (which again is also important for SOTA DL systems) - and even perhaps per module to some extent (as brain regions tend to crystalize/myleninate in hierarchical processing order, starting with lower sensor/motor cortex and ending in upper cortex and PFC), which introduces even more hyperparams.
So absent other explanations, it seems pretty likely that humans vary across these hyperparms, which can have enormous effects on later development. High curiosity drive combined with delayed puberty/neotany (with adapted learning rate schedules) is already a simple sufficient explanation for much of the variation in STEM-type abstract intelligence, and more specifically explains the ‘jock vs nerd’ or phenomena as different stable early vs late mating strategy niches.
As it happens, I don’t think humans are genetically hardwired to be afraid of death in the first place
Yeah pretty sure they aren’t (or at least I wasn’t, had to learn). But since death is a minimally empowered state, its immediately obviously evaluated as very low utility.
To agree and expand on this—successful DL systems have a bunch of important hyperparameters, and many of these control balances between different learning sub-objectives and priors/regularizers. Any DL systems that use intrinsic motivation, and especially those that combine that with other paradigms like extrinsic reward reinforcement learning, tend to have a bunch of these hyperparams. The brain is driven by both complex instrinsic learning mechanisms (empowerment/curiosity, predictive learning, etc) and extrinsic reward reinforcement learning (pleasure, pain, hunger, thirst, sleep, etc), and so likely has many such hyperparams. The brain also seems to control learning schedules somewhat adaptively (which again is also important for SOTA DL systems) - and even perhaps per module to some extent (as brain regions tend to crystalize/myleninate in hierarchical processing order, starting with lower sensor/motor cortex and ending in upper cortex and PFC), which introduces even more hyperparams.
So absent other explanations, it seems pretty likely that humans vary across these hyperparms, which can have enormous effects on later development. High curiosity drive combined with delayed puberty/neotany (with adapted learning rate schedules) is already a simple sufficient explanation for much of the variation in STEM-type abstract intelligence, and more specifically explains the ‘jock vs nerd’ or phenomena as different stable early vs late mating strategy niches.
Yeah pretty sure they aren’t (or at least I wasn’t, had to learn). But since death is a minimally empowered state, its immediately obviously evaluated as very low utility.