Biological intelligence uses a "multiscale competency architecture" (MCA). It
exhibits adaptive, goal directed behaviour at all scales, from cells to organs
to organisms. In contrast, machine intelligence is only adaptive and goal
directed at a high level. Learned policies are passively interpreted using
abstractions (e.g. arithmetic) embodied in static interpreters (e.g. x86).
Biological intelligence excels at causal learning. Machine intelligence does
not. Previous work showed causal learning follows from weak policy
optimisation, which is hindered by presupposed abstractions in silico. Here we
formalise MCAs as nested "agentic abstraction layers", to understand how they
might learn causes. We show that weak policy optimisation at low levels enables
weak policy optimisation at high. This facilitates what we call "multiscale
causal learning" and high level goal directed behaviour. We argue that by
engineering human abstractions in silico we disconnect high level goal directed
behaviour from the low level goal directed behaviour that gave rise to it. This
inhibits causal learning, and we speculate this is one reason why human recall
would be accompanied by feeling, and in silico recall not.

生物智能相较于机器智能在不同层面展现出调适性、目标导向的行为；本文通过对多层次情绪学习的形式化来理解生物智能如何学习因果关系，并指出在硅中工程化人类抽象会在高层目标导向行为和低层目标导向行为之间断开连接，这抑制了因果关系的学习。