feat(matlab): learning figure + methods write-up + organized CSV data export

Adds make_figure (2-panel learning curves + per-phase slopes, colorblind-safe
Box-B2/Box-A2 palette) -> results/figure_learning.png; analysis/writeup.md
(figure + methods + results summary); and tdcs_export_all/run_export exporting
every data variation into an organized export/ tree (curated_all, scenarios/9,
anchors/3 with tDCS factor, phases/9 + MANIFEST). Adds tExport tests. Suite 36/36.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

analysis/writeup.md

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+# tDCS reaching study — figure and methods
+
+![Learning curves and per-phase learning rate](matlab/results/figure_learning.png)
+
+**Figure 1.** (A) Mean ± SEM successful reaches per session over the common 0–13
+training-day window for the tDCS (Box-B2, blue) and control (Box-A2, green)
+groups; the dashed line marks the fast/slow phase boundary. (B) Per-animal
+learning slope (mean, 95% CI) in the fast (0–5) and slow (6–13) phases.
+
+## Methods
+
+The number of successful reaches per session was modeled as a function of
+training day and stimulation condition. Sessions were indexed by training day
+(day 0 = the first analyzed day, i.e. the previous study's "Day 1"), and the two
+electrode conditions were compared as a binary tDCS factor (Box-B2 = tDCS,
+Box-A2 = control; n = 3–5 animals per group). We fit linear mixed-effects models
+(MATLAB R2025b, `fitlme`, Statistics and Machine Learning Toolbox) of the form
+`successes ~ day * tDCS + (1 | subject)` with a per-subject random intercept; the
+tDCS main effect (day centered at each window's first day) estimates the Day-1
+group difference and the day × tDCS interaction estimates the difference in
+learning rate. Because the anchor groups' day coverage was unequal over the full
+range (Box-A2 data ended ~day 13 while Box-B2 continued), analyses were
+restricted to the common 0–13 window and, to separate acquisition from plateau,
+refit within a *fast* (days 0–5) and *slow* (days 6–13) phase. Given the small
+number of animals, the subject was treated as the unit of inference: per-animal
+learning slopes were compared between groups (Welch *t*, Mann–Whitney) and within
+groups across phases (paired *t*); the `fitlme` interaction tests, which use
+observation-level degrees of freedom, are anticonservative at this sample size
+and are reported only as a reference. Statistical power was estimated by
+Monte-Carlo simulation from the fitted early-phase model across a range of
+per-group sample sizes and effect sizes. Overall accuracy (successes/attempts)
+and counts were additionally modeled with binomial and Poisson GLMMs. All
+analysis code, derived datasets, and this figure are in `analysis/matlab/`.
+
+## Results (summary)
+
+Both groups showed strong session-to-session learning (day effect, p < 10⁻¹³),
+following a fast-then-slow trajectory (Fig. 1A): steep gains over days 0–5 that
+flattened toward a plateau by day 13. The tDCS and control groups performed
+comparably on Day 1 (tDCS main effect n.s.), and the tDCS group acquired faster
+during the early phase (Fig. 1B; slope ≈ 15 vs 10 reaches/day; interaction +4.6,
+95% CI [+1.0, +8.2]), converging by the late phase. At the subject level this
+early-phase difference was a consistent trend but did not reach significance
+(Welch p ≈ 0.11–0.25), and the study was underpowered at n = 3–5/group (≈30–70%
+power for the observed effect; ≈8/group would be needed for the observed effect,
+≈20/group for half that, to reach 80% power). This is consistent with the
+previously reported days × tDCS interaction, here compressed into the early
+acquisition phase — plausibly because the improved protocol's higher performance
+ceiling leaves little late-phase headroom for a benefit to accumulate.