Question, contribution, and evidence boundary

The central question is:

Can the network infer the changing rule from the information it actually receives, and do the reported metrics measure rule persistence rather than generic repetition?

Cheung's modular recurrent OCD model supplies the direct computational motivation CheungOCD. His pruning and intervention studies provide related structural-intervention precedents CheungPruning,CheungModes. These are computational studies, not premises identifying the present variables with clinical symptoms or treatment effects.

The OCD article already acknowledges that two rule labels implement one function and that its immediate response-repetition metric does not prove persistence of the previous latent rule CheungOCD. The present contribution is an explicit observability theorem, the exact task reference, and independently specified interfaces for metrics, repair, and matching.

Psy01-A separates cue information, masks, parameter geometry, margins, and observation semantics CheungPsy01A. Psy03-A separates intervention budgets and implemented functions CheungPsy03A. Psy03-B separates permanent structural restrictions from performance effects CheungPsy03B. Psy03-C separates recurrent dynamics, task information, and probe interpretation CheungPsy03C. Psy02-A and Psy02-B supply documentary precedents for complete records and mechanism-identification boundaries CheungPsy02A,CheungPsy02B. They are cited as source records and methodological precedents, not as unexplained premises. All positive results used here have printed objects and proofs.

provenance. The development brief supplied for Psy03-D describes a modular rule-switching implementation DevelopmentBrief. The complete modular source was not included as an attachment in that supplied package. The public project file 069av2.py supplies the fourth rule and corresponding task generator CheungPublicCode. It is a mutable public record, not a certified byte-identical substitute for an unprovided attachment.

The mathematical task below is completely specified. Its theorems do not depend on reconstructing an unavailable training run. Implementation observations are classified separately as reported source patterns, public-record checks, and independently proved consequences of those patterns.

claim levels.

1. Information limitation: what can be predicted from the admitted observations.

2. Computational intervention effect: what changes under a specified implementation or training policy.

3. Cognitive or biological interpretation: what a computational variable corresponds to outside the model.

The first two require different certificates. The third requires an additional empirical correspondence.

. We use rule-ID change, label-function change, informative disagreement, old-function response, repeated response, and recovery pattern. The source's clinical and pharmacological names are retained only as documentary labels.