I haven’t seen compelling (to me) examples of people going successfully from psychology to algorithms without stopping to consider anything whatsoever about how the brain is constructed .
One reason it’s tricky to make sense of psychology data on its own, I think, is the interplay between (1) learning algorithms, (2) learned content (a.k.a. “trained models”), (3) innate hardwired behaviors (mainly in the brainstem & hypothalamus). What you especially want for AGI is to learn about #1, but experiments on adults are dominated by #2, and experiments on infants are dominated by #3, I think.
I guess this depends on how much you think we can make progress towards AGI by learning what’s innate / hardwired / learned at an early age in humans and building that into AI systems, vs. taking more of a “learn everything” approach! I personally think there may still be a lot of interesting human-like thinking and problem solving strategies that we haven’t figured out to implement as algorithms yet (e.g. how humans learn to program, and edit + modify programs and libraries to make them better over time), that adult and child studies would be useful in order to characterize what might even be aiming for, even if ultimately the solution is to use some kind of generic learning algorithm to reproduce it. I also think there’s this fruitful in-between (1) and (3), which is to ask, “What are the inductive biases that guide human learning?”, which I think you can make a lot of headway on without getting to the neural level.
Some recent examples, off the top of my head!
Jain, Y. R., Callaway, F., Griffiths, T. L., Dayan, P., Krueger, P. M., & Lieder, F. (2021). A computational process-tracing method for measuring people’s planning strategies and how they change over time.
Dasgupta, I., Schulz, E., Tenenbaum, J. B., & Gershman, S. J. (2020). A theory of learning to infer. Psychological review, 127(3), 412.
Harrison, P., Marjieh, R., Adolfi, F., van Rijn, P., Anglada-Tort, M., Tchernichovski, O., … & Jacoby, N. (2020). Gibbs Sampling with People. Advances in Neural Information Processing Systems, 33.
I guess this depends on how much you think we can make progress towards AGI by learning what’s innate / hardwired / learned at an early age in humans and building that into AI systems, vs. taking more of a “learn everything” approach! I personally think there may still be a lot of interesting human-like thinking and problem solving strategies that we haven’t figured out to implement as algorithms yet (e.g. how humans learn to program, and edit + modify programs and libraries to make them better over time), that adult and child studies would be useful in order to characterize what might even be aiming for, even if ultimately the solution is to use some kind of generic learning algorithm to reproduce it. I also think there’s this fruitful in-between (1) and (3), which is to ask, “What are the inductive biases that guide human learning?”, which I think you can make a lot of headway on without getting to the neural level.