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# Oral-B recovery: temporal-difference somato-dendritic innovation
This branch follows the complete negative result in `ORAL_B.md`; it does not
reinterpret or overwrite that preregistration. It is motivated by Fig. 5 and
Extended Data Fig. 13 of Francioni et al. (2026): P+ versus P- SD residuals
separate epochs by the sign of recent error change, whereas absolute error
magnitude alone does not separate the populations.
## Structural diagnosis
The failed model always calibrated its vectorizer against the instantaneous
squared-loss descent direction `e_t s_i`. Its P+ minus P- residual contrast is
therefore proportional to current error magnitude. Conditional on lower
current error during improving epochs, the preregistered sign-inversion index
must be negative. Supplying error velocity as a second regressor cannot alter
the target being regressed. All 36 negative signs are consistent with this
structural mismatch.
## Candidate mechanism
The recovery factorizes the instruction into two locally obtainable terms:
1. a slowly learned per-cell causal-role coefficient `m_i`, estimated from
sparse antithetic perturbations and the scalar BCI cursor difference;
2. the within-episode performance innovation
`delta_t = |e_(t-1)| - |e_t|`, reset at episode boundaries.
The task instruction is `m_i delta_t`. Ordinary soma-predictable apical
traffic is added before the same per-cell neutral predictor, so the plasticity
signal remains the somato-dendritic innovation rather than a directly supplied
role label. Forward updates use only current presynaptic context, local
postsynaptic gain, and that innovation. The first recovery is plasticity-only
(`kappa=0`): the empirical residual points along observed performance change,
not necessarily a corrective online-control direction.
For perturbation vector `xi`, the role target is
```text
q_i = [(z(h + sigma xi) - z(h - sigma xi)) / (2 sigma)] xi_i,
```
whose expectation is the unknown causal role `s_i`. This consumes scalar
cursor observations rather than gradients or reverse-mode differentiation.
## R0 mechanics gate
R0 generates no task endpoint and touches no development or confirmation
environment. It passes only if deterministic checks establish all of:
- the mean perturbation role target has cosine above 0.99 with the analytic
role used only by the diagnostic;
- the local vectorizer update moves toward that role without reading it;
- the neutral predictor exactly removes affine soma traffic in the controlled
synthetic check;
- episode-initial performance velocity is zero;
- perfect role identification gives a strictly positive P+/P- error-change
sign-inversion index; and
- all original BCI pairing, lesion-mask, decoder, and finite checks still pass.
R0 is implemented by `performance_velocity` in `sdil/bci.py` and audited by
`experiments/bci_smoke.py` plus `experiments/verify_theory.py`. Passing R0
only permits a separately committed training-only structural screen after the
D4 accept confirmation closes. No success-rate endpoint, hyperparameter
selection, or oral-B score change is authorized by R0.
## Boundary for the next gate
Before any recovery task run, R1 must freeze its development-only task seeds,
forward/vectorizer rates, role-estimator cadence, cost accounting, and a stop
rule requiring both positive derivative sign and actual learning. A sign that
is positive merely because `delta_t` was inserted is not evidence of credit
assignment. Confirmation seeds 10--15 from the original oral-B protocol
remain untouched unless an R1 candidate passes every frozen learning,
innovation, role-vectorization, and plasticity-lesion requirement.
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