Sure, we could see `animal = f(image)` as a partial function and lift it into some total function space, `maybe_animal = f(image)`. Is our reasoning here actua

Sure, we could see `animal = f(image)` as a partial function and lift it into some total function space, `maybe_animal = f(image)`. Is our reasoning here actually this total function though? We really do always need to keep in mind that animal reasoning will terminate, animals will act "guided by other reasoning", and resume the original reasoning process. Action really messes up this neat "totalising option" for partial functions. If i'm not sure if "fluffy" is a dog or a cat, i might wait longer for it to move; I might throw something at it; I might ask its owner. This isnt as simple as "don't know", since reasoning is kinda time-bound and time-parameterised, my very measuring process is sensitive to my own confidence in what-something-is. Is this really "f(image) = dont-know"? I dont think so. I think it's more like, "judgement = body-brain-state(t, reasoning-goal, {action policies}, ...large-number-of-other-things)". This gets to my issue. In my view what animals are doing is better described by a dynamical equation of state (like a wavefunction), as the whole system is operating under its own dynamical evolution (including, eg., what fluffy does in response to your puzzlement). I dont see Dog|Cat|DontKnow as the answer here. I dont think it's the actual total function which corresponds to our judgement, though it probably is total -- we just end up with "DontKnow" in all the cases actual intellkigence is requireds.... the very think we're aiming to model.