I just want to comment that I think Minsky’s community of mind is a better overall model of agency than predictive coding. I think predictive coding does a great job of describing the portions of the brain responsible for perceiving and predicting the environment. It also does pretty well at predicting and refining the effects of one’s actions on the environment. It doesn’t do well at all with describing the remaining key piece: goal setting based on expected value predictions by competing subagents.
I think there’s a fair amount of neuroscience evidence pointing towards human planning processes being made up of subagents arguing for different plans. These subagents are themselves made up of dynamically fluctuating teams of sub-sub-agents according to certain physical parameters of the cortex. So, the sub-agents are kinda like competing political parties, that can fracture or join dynamically to adapt to different contexts.
Also, it’s important to keep in mind that actually the subagents don’t just receive maximum reward for being accurate. They actually receive higher rewards for things turning out unexpectedly better than was predicted. This slightly complicated surprise-enhanced-reward mechanism is common across mammals and birds, was discovered by behaviorists quite a while back (see: reinforcement schedules, for optimizing unpredictability to maximize behavior change. Also, see surprisal and dopamine). So yeah, not just 100% predictive coding, despite that claim persistently being made by the most enthusiastic predictive coding adherents. They argue for that, but I think their arguments are trying to turn a system that 90% agrees with them into one that 100% agrees with them by adding in a bunch of confusing epicycles that don’t match the data well.
I just want to comment that I think Minsky’s community of mind is a better overall model of agency than predictive coding. I think predictive coding does a great job of describing the portions of the brain responsible for perceiving and predicting the environment. It also does pretty well at predicting and refining the effects of one’s actions on the environment. It doesn’t do well at all with describing the remaining key piece: goal setting based on expected value predictions by competing subagents.
I think there’s a fair amount of neuroscience evidence pointing towards human planning processes being made up of subagents arguing for different plans. These subagents are themselves made up of dynamically fluctuating teams of sub-sub-agents according to certain physical parameters of the cortex. So, the sub-agents are kinda like competing political parties, that can fracture or join dynamically to adapt to different contexts.
Also, it’s important to keep in mind that actually the subagents don’t just receive maximum reward for being accurate. They actually receive higher rewards for things turning out unexpectedly better than was predicted. This slightly complicated surprise-enhanced-reward mechanism is common across mammals and birds, was discovered by behaviorists quite a while back (see: reinforcement schedules, for optimizing unpredictability to maximize behavior change. Also, see surprisal and dopamine). So yeah, not just 100% predictive coding, despite that claim persistently being made by the most enthusiastic predictive coding adherents. They argue for that, but I think their arguments are trying to turn a system that 90% agrees with them into one that 100% agrees with them by adding in a bunch of confusing epicycles that don’t match the data well.
I’m curious about what neuroscience evidence you’re thinking of which supports that model.
What you describe (subagents arguing for different plans) seems something between “fairly compatible” to “direct consequence of” predictive processing. Cf https://www.lesswrong.com/posts/3fkBWpE4f9nYbdf7E/multi-agent-predictive-minds-and-ai-alignment
Surprise-enhanced-reward seems interesting, will look it up