The neuroscience here hints at something that current AI systems still lack:
a direct, internal positive signal tied to closing a reasoning loop.
Transformers learn almost everything through language-like supervision. Wrong token = small penalty, right token = small reward. That’s great for pattern induction, but it means the model treats a correct chain-of-thought and a beautifully phrased but wrong chain-of-thought as almost the same kind of object—just sequences with slightly different likelihoods.
Human reasoning isn’t like that.
When a logic chain closes cleanly, the brain fires a strong internal reward. That “Aha” isn’t just emotion; it’s an endogenous learning signal saying: this structure is valid, keep this, reuse this. It’s effectively a structural correctness reward, orthogonal to surface language.
If AI ever gets a similar mechanism — a way to mark “self-consistent causal closure” as positively rewarded — we might finally bridge the gap between language-trained reasoning and true general learning. It would matter for:
fast abstraction formation
reliable logical inference
discovering new concepts rather than remixing old ones
Backprop gives us gradient-based correction, but it’s mostly negative feedback. There’s no analogue of the brain’s “internal positive jolt” when a new idea snaps together.
If AGI needs general learning, maybe the missing piece isn’t more scale — it’s this reward for closure.
Transformers learn almost everything through language-like supervision. Wrong token = small penalty, right token = small reward. That’s great for pattern induction, but it means the model treats a correct chain-of-thought and a beautifully phrased but wrong chain-of-thought as almost the same kind of object—just sequences with slightly different likelihoods.
Human reasoning isn’t like that. When a logic chain closes cleanly, the brain fires a strong internal reward. That “Aha” isn’t just emotion; it’s an endogenous learning signal saying: this structure is valid, keep this, reuse this. It’s effectively a structural correctness reward, orthogonal to surface language.
If AI ever gets a similar mechanism — a way to mark “self-consistent causal closure” as positively rewarded — we might finally bridge the gap between language-trained reasoning and true general learning. It would matter for:
fast abstraction formation
reliable logical inference
discovering new concepts rather than remixing old ones
Backprop gives us gradient-based correction, but it’s mostly negative feedback. There’s no analogue of the brain’s “internal positive jolt” when a new idea snaps together.
If AGI needs general learning, maybe the missing piece isn’t more scale — it’s this reward for closure.