I think there are a lot of things in that general category in the brain.
Yes, volume is definitely not the only thing going on with human brains. Human brains are not identical, the way ANNs can be identical save for a knob in a config file increasing the parameter count. (Nor is parameter count the only thing going on with DL scaling, for that matter.) Intelligence is highly polygenic, and the brain volume genetic correlations with intelligence are, while apparently causal, much less than 1 (while intelligence genetically correlates with lot of other things); the brain imaging studies also show predicting intelligence taps into a lot more aspects of static neuroanatomy or dynamic patterns than simply brain volume (or some deeper neuron-count). Things like myelination and mitochondrial function will matter, and will support the development processes. General bodily integrity and health and mutation load on all bodily systems will matter. All of these will influence development and the ability to develop connected-but-not-too-connected brain networks which can flexibly coordinate to support fluid intelligence activity. So while you can fiddle the knob and train the same model at different parameter scales and extract the power law, you can’t do that when you compare human brains: it’s as if not only are all the hyperparameters randomized a little each run, the GPUs trained on will convert electricity to FLOPs at wildly different rates, some GPUs just won’t multiply numbers quite right (each one multiplying wrongly in a different way), the occasional layer in a checkpoint might be replaced with some Gaussian noise… (So you can see the influence of volume at the species level because you’re comparing group means where all the noise washes out, but then at individual level it may be much more confusing.)
the brain imaging studies also show predicting intelligence taps into a lot more aspects of static neuroanatomy or dynamic patterns than simply brain volume
Do you have links for these studies? Would leave to have a read about the static and dynamic correlates of g are from brain imaging!
Yes, volume is definitely not the only thing going on with human brains. Human brains are not identical, the way ANNs can be identical save for a knob in a config file increasing the parameter count. (Nor is parameter count the only thing going on with DL scaling, for that matter.) Intelligence is highly polygenic, and the brain volume genetic correlations with intelligence are, while apparently causal, much less than 1 (while intelligence genetically correlates with lot of other things); the brain imaging studies also show predicting intelligence taps into a lot more aspects of static neuroanatomy or dynamic patterns than simply brain volume (or some deeper neuron-count). Things like myelination and mitochondrial function will matter, and will support the development processes. General bodily integrity and health and mutation load on all bodily systems will matter. All of these will influence development and the ability to develop connected-but-not-too-connected brain networks which can flexibly coordinate to support fluid intelligence activity. So while you can fiddle the knob and train the same model at different parameter scales and extract the power law, you can’t do that when you compare human brains: it’s as if not only are all the hyperparameters randomized a little each run, the GPUs trained on will convert electricity to FLOPs at wildly different rates, some GPUs just won’t multiply numbers quite right (each one multiplying wrongly in a different way), the occasional layer in a checkpoint might be replaced with some Gaussian noise… (So you can see the influence of volume at the species level because you’re comparing group means where all the noise washes out, but then at individual level it may be much more confusing.)
Do you have links for these studies? Would leave to have a read about the static and dynamic correlates of g are from brain imaging!