Question, contribution, and proof boundary

The central question is:

Which probe-performance differences reflect recurrent dynamics, which reflect information loss or distribution shift in the task, and which can legitimately be attributed to structural interventions?

Cheung's pruning and intervention studies supply the immediate computational motivation CheungPruning,CheungModes,CheungCliff. His separate pruning-based recurrent model of OCD illustrates another application of structural and functional interventions CheungOCD. Its task, observations, and conclusions are not identified with the present four-class probe battery.

Psy01-A separates masks, weight geometry, margins, noise, and optimization schedules CheungPsy01A. Psy03-A studies individual intervention budgets CheungPsy03A. Psy03-B studies irreversible structural state and threshold feedback CheungPsy03B. The distinctive contribution here is recurrent dynamics and what the probe observations identify.

Psy02-A and Psy02-B motivate the separation of carrier dynamics, instruments, complete records, and mechanism identification CheungPsy02A,CheungPsy02B. Their roles are documentary and methodological. The independent results needed here are proved for the objects printed in this source.

source. The complete supplied 068g_cognitive_impact_v2.py defines the implementation audit Cheung068G. No mutable repository revision is silently substituted. The audit is static and does not independently reproduce the source's numerical outcomes.

quantities that must remain separate.

1. Task information: the best prediction possible from the declared observation law.

2. Implementation performance: the accuracy of a particular recurrent computation and readout.

3. Construct interpretation: a correspondence between that observation and a biological or cognitive mechanism.

The first two can be studied mathematically. The third needs separately supplied validation.

. We use rotation-shift generalization, radial-scaling generalization, reduced-separation classification, high-input-noise classification, and coordinate-interference blending. The source's flexibility, visuospatial, attention, sensory, mania, and drug-class labels remain proposed analogies.

and evidence. The elementary norm, concentration, and channel tools are standard. The contribution is their source-specific assembly, the explicit gate-aware constants, task references, and interface counterexamples. No optimization-convergence theorem, executed simulation, compiled artifact, or clinical recommendation is claimed.