results: identify state-dependent physical drift

3 files changed, 916 insertions, 0 deletions

diff --git a/results/figs/physical_bias_state_dependence.png b/results/figs/physical_bias_state_dependence.png
new file mode 100644
index 0000000..6e15af6
--- /dev/null
+++ b/results/figs/physical_bias_state_dependence.png
diff --git a/results/figs/physical_bias_state_dependence_caption.md b/results/figs/physical_bias_state_dependence_caption.md
new file mode 100644
index 0000000..51d63dc
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+++ b/results/figs/physical_bias_state_dependence_caption.md
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+**Released physical drift contains a locally predictable state-dependent
+component.** A--B, gate-voltage trajectories from the four small-network
+`DriftTests` files released with Dillavou et al. (Zenodo 15692914). Colored
+points are the retained pre-saturation samples within 30 mV of the published
+single-task solution lines; faint curves show the complete post-initialization
+traces. C--D, tangential drift velocity along each solution line. A constant
+bias model (dotted) is compared with a per-edge local affine model
+`B_i(G_i)=b_i+k_i(G_i-G_ref_i)` (solid). With the pre-saturation threshold
+fixed at 50 mV before each trace's observed terminal gate plateau, the affine
+model reduces forward time-block test RMSE to 0.21 and 0.54 of the constant
+model in the two task pairs. The direction holds for terminal headrooms of 30,
+50, 80 and 100 mV using unsmoothed centered-difference velocities. This
+descriptive reanalysis establishes a measured state-dependent component
+representable by the proposed local predictor. It does not demonstrate that
+SDIL improves physical task learning; that is the prospective P1 test.
diff --git a/results/physical_bias/p0_state_dependence.json b/results/physical_bias/p0_state_dependence.json
new file mode 100644
index 0000000..ef641c2
--- /dev/null
+++ b/results/physical_bias/p0_state_dependence.json
@@ -0,0 +1,901 @@
+{
+  "analysis": "published_physical_drift_state_dependence_diagnostic",
+  "confirmatory": false,
+  "supports_task_learning_improvement": false,
+  "provenance": {
+    "zenodo_record": "15692914",
+    "doi": "10.5281/zenodo.15692914",
+    "release": "v1.0.1",
+    "source_tree": "maguzj-imperfect-learning-physical-systems-71b8d72"
+  },
+  "identification": {
+    "observable": "tangential gate velocity after reaching a one-task solution line",
+    "assumption": "the clean force has zero tangential projection on the published solution line",
+    "model_restriction": "each edge bias is affine in its own local gate voltage",
+    "limitation": "the traces identify a local affine surrogate over their visited states, not the complete physical vector field"
+  },
+  "preprocessing": {
+    "minimum_time": 1.05,
+    "terminal_headroom": 0.05,
+    "maximum_line_distance": 0.03,
+    "trim_selected_ends": 1
+  },
+  "pairs": {
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+        3.0,
+        3.5
+      ],
+      "published_constant_bias_at_reference_v_per_s": [
+        2.9,
+        4.7
+      ],
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+            "path": "/scratch/yurenh2/imperfect-learning-physical-systems-v1.0.1/maguzj-imperfect-learning-physical-systems-71b8d72/small network/DriftTests/exp1DriftTest_1.mat",
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