Datasets:
Noise ceiling + within-run correction (added 2026-08-25)
This directory is additive. It does not replace or correct any file elsewhere
in the dataset; the tables under overall/, by-model/ and diagnostics/ are
unchanged and still carry the run confound described in the top-level README.
What is new here is the measurement that was missing: a noise ceiling, and alignment expressed as a fraction of it.
Why it matters
The top-level README says of the brain-alignment tables: "these tables do not show that language models align with the brain, and they do not show that they fail to. The measurement cannot answer it." That was correct for those tables. With a ceiling, the question becomes answerable, and the answer is now a well-powered null rather than an uninterpretable one.
The numbers
ds003604, task Phon, ses-5, 43 subjects, 72 stimuli (perceptual controls excluded). Both RDMs are built from the same per-subject patterns and differ only by the correction.
| raw | within-run normalised | |
|---|---|---|
| noise ceiling (LOO lower / upper) | 0.774 / 0.792 | 0.849 / 0.859 |
| "different run" predicts dissimilarity | +0.557 | −0.041 |
Best layer per model against the corrected RDM:
| model | layer | Spearman rho | % of noise ceiling |
|---|---|---|---|
| beetle-humanscale-eng | 2 | +0.022 | 2.6% |
| babylm-gpt2-3 | 5 | +0.013 | 1.5% |
| pico-decoder-large | 0 | −0.001 | −0.1% |
Read it this way. Inter-subject reliability is 0.85 — well above the 0.2–0.4 typical in this literature — so there is a large, replicable target available to predict. Three models spanning three architectures capture 0–3% of it.
Two supporting points:
- The ceiling rises after correction (0.774 → 0.849). If the raw reliability had been mostly the run artefact, removing the artefact would have lowered the ceiling. It rose, so the correction removes noise rather than signal.
- The corrected layer profile is sensible — positive at early/middle layers, negative at the output — whereas the raw profile is not. The correction makes the measurement behave, and it still finds nothing.
Scope, stated plainly
One task × session cell, one session, 43 subjects. Whether this replicates across all twelve cells is exactly what the full corrected sweep is running to answer; those results will be added here as a separate, clearly-labelled set. Do not read this single cell as the whole dataset.
Files
| file | what |
|---|---|
ceiling_Phon_ses-5.csv |
ceiling + run-confound, raw vs corrected |
alignment_vs_ceiling_Phon_ses-5.csv |
per model per layer, rsa raw/corrected and fraction of ceiling |
summary_Phon_ses-5.json |
the same as a single record |
fig_ceiling_layerwise.png/pdf |
alignment vs the ceiling band, per model, with a zoomed layer profile |
fig_ceiling_confound.png/pdf |
run confound and ceiling, before vs after correction |
Reproducing
python scripts/ceiling_report.py --pattern-dir <patterns> --task Phon --session ses-5
python scripts/make_ceiling_figures.py --dir paper_results/ceiling
Session RDMs built after 2026-08-25 carry their per-subject RDMs and a
within_run_normalized flag, so the ceiling is recomputable from the .npz
alone and a corrected RDM can never be confused with an uncorrected one.
scripts/collect_ceilings.py tabulates ceilings for a whole tree;
scripts/verify_rdm_provenance.py fails loudly on a tree with mixed provenance.