Datasets:

BrainAlign
/

cdl-devai-results

license: cc-by-4.0
tags:
  - neuroscience
  - fmri
  - brain-alignment
  - interpretability
  - language-models
configs:
  - config_name: summary_by_checkpoint
    default: true
    data_files: overall/by_checkpoint.csv
  - config_name: summary_by_family
    data_files: overall/summary_by_family.csv
  - config_name: brain_alignment
    data_files: by-model/*/brain_alignment.csv
  - config_name: interp_mechanistic
    data_files: by-model/*/interp_mechanistic.csv
  - config_name: interp_layerwise
    data_files: by-model/*/interp_layerwise.csv
  - config_name: localisation_isolation
    data_files: by-model/*/localisation_isolation.csv
  - config_name: localisation_onset
    data_files: by-model/*/localisation_onset.csv
  - config_name: behaviour
    data_files: by-model/*/behaviour.csv
  - config_name: ablation_alignment
    data_files: by-model/*/ablation_alignment.csv
  - config_name: ablation_behaviour
    data_files: by-model/*/ablation_behaviour.csv
  - config_name: claim_tests
    data_files: overall/claim_tests.csv
  - config_name: heldout_predictor
    data_files: overall/heldout_predictor.csv
  - config_name: diagnostics_layerwise
    data_files: diagnostics/layerwise_alignment.csv
  - config_name: diagnostics_run_confound
    data_files: diagnostics/run_confound_check.csv
  - config_name: ceiling_analysis
    data_files: ceiling-analysis/ceiling_*.csv
  - config_name: alignment_vs_ceiling
    data_files: ceiling-analysis/alignment_vs_ceiling_*.csv

CDL DevAI results — brain × interpretability × localisation, per model per checkpoint

Developmental analysis of 10 language-model families against the ds003604 auditory language fMRI dataset. For every training checkpoint of every model we measured three things and here report them side by side:

axis	what it asks	source tables
brain	does the model's representational geometry match the brain's?	`brain_alignment`
interp	how is the representation organised internally?	`interp_mechanistic`, `interp_layerwise`
localisation	are linguistic phenomena isolated into dedicated units?	`localisation_isolation`, `localisation_onset`

Start with the summary_by_checkpoint config (the default, and what the viewer shows first): one row per model × checkpoint, with all three axes as columns. 262 rows, 10 models.

⚠️ READ THIS BEFORE USING THE BRAIN ALIGNMENT NUMBERS

The brain alignment columns are confounded by scanner run and must not be read as a result about language models. This affects every rsa* column in brain_alignment, every brain_* column in the summary tables, and the ablation_alignment table.

In ds003604 each stimulus is presented in exactly one scanner run (for Phon, run-01 carries 48 of the 96 stimuli and run-02 the other 48). Run membership is therefore perfectly confounded with stimulus identity, and every cross-run stimulus pair inherits that run's drift, baseline shift and scaling. Measured:

"different run" predicts brain dissimilarity, Spearman rho, all 12 task × session cells:
    Gram   +0.866  +0.812  +0.828        Plaus  +0.741  +0.689  +0.761
    Phon   +0.562  +0.488  +0.624        Sem    +0.511  +0.510  +0.601

By comparison no stimulus property predicts these RDMs at all: trial type ≈ −0.02, text length ≈ 0, lexical overlap ≈ 0. The RDMs look highly reliable across independent subject cohorts (rho 0.74–0.92 between sessions of the same task) — but that is largely the reliability of an acquisition artefact, because run assignment is fixed by the protocol and so repeats identically for every subject.

A language model cannot represent which scanner run a stimulus appeared in, so its RSA against this structure is ≈0 by construction, and slightly negative in practice.

Two obvious explanations were tested and ruled out:

Cohort size. Sem/ses-7 was built from 40 subjects; rebuilt from 98 it agrees with the 40-subject version at rho = 0.928, identical stimuli. Bigger cohorts change nothing.
Layer choice. Alignment is flat across every layer — −0.012 to −0.021 across all 12 layers of babylm-gpt2-3 and all 14 of beetle-humanscale-eng. There is no middle-layer peak being missed. See diagnostics_layerwise.

What fixes it, and what happens when you do. Z-scoring each voxel within run before combining runs removes the confound cleanly — run predictiveness falls from +0.562 to −0.041. Alignment against the corrected RDM then peaks at +0.022 (layer 2, beetle-humanscale-eng, 2,556 stimulus pairs), which is not significant. The layer profile becomes sensible — early/middle layers positive, output layers negative — but the magnitude is ≈0. See diagnostics_run_confound.

Bottom line: 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. The corrected analysis, on one cell and one model, finds no detectable alignment. Do not cite the raw numbers in either direction.

UPDATE 2026-08-25 — there is now a noise ceiling, and the null is real

Everything above still stands for the tables it describes. What has changed is that the missing measurement now exists: see ceiling-analysis/.

On ds003604 Phon/ses-5 (43 subjects, 72 stimuli), rebuilt from fresh patterns:

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: beetle-humanscale-eng +0.022 (2.6% of ceiling), babylm-gpt2-3 +0.013 (1.5%), pico-decoder-large −0.001 (−0.1%).

Inter-subject reliability is 0.85 — far above the 0.2–0.4 typical in this literature — so there is a large, replicable target to predict, and three models across three architectures capture 0–3% of it. That is a clean, well-powered null, not an uninterpretable one.

Note the ceiling rises after correction (0.774 → 0.849): had the raw reliability been mostly the run artefact, removing it would have lowered the ceiling. It rose, so the correction removes noise rather than signal.

Scope: one task × session cell. The full twelve-cell corrected sweep is running and will be added as a separate, clearly-labelled set. The tables above are not modified by this update.

	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

Which columns are safe

axis	affected by the run confound?
`brain_`, `rsa`, `ablation_alignment`	❌ Confounded. Do not use as a model result.
`interp_*` (norm, gini, hoyer, per, condition_number, cka_to_prev)	✅ Safe — computed from LM activations only. The fMRI data is not involved.
`loc_` / `localisation_` (selectivity_index, overlap, gini, entropy, layer_com, n_active_layers)	✅ Safe — LM-internal localisation against text contrasts. No fMRI.
`behaviour` (mp_accuracy)	✅ Safe — minimal-pair accuracy, text only.
`ablation_behaviour` (causal_selectivity)	✅ Safe — ablation vs behaviour, no fMRI.

The one uncontaminated positive result in this release is the causal behaviour test: ablating a phenomenon's localized circuit costs 1.13% minimal-pair accuracy versus 0.55% for a random circuit of the same size — selectivity +0.0058, t = 1.98, p = 0.049, n = 316, driven mostly by Phon (+0.021). That is borderline and should be described as suggestive, not established.

Layout

overall/
  by_checkpoint.csv        <- THE MAIN TABLE. one row per (family, step),
                              brain + interp + localisation side by side
  summary_by_family.csv    <- one row per model: means, ranges, step-vs-alignment trend
  claim_tests.csv          <- per-family claim tests (claim, stat, value, p, n)
  heldout_predictor.csv    <- cross-family held-out predictive validation
  localisation_onset.csv
by-model/<family>/
  README.md                <- what this model is, its numbers, what is odd about it
  checkpoints.csv          <- this model's rows of the main table
  brain_alignment.csv      <- full per task × session × step detail  (CONFOUNDED)
  interp_mechanistic.csv   interp_layerwise.csv
  localisation_isolation.csv  localisation_onset.csv
  behaviour.csv            ablation_alignment.csv  ablation_behaviour.csv
  figures/<family>_overview.png
ceiling-analysis/          <- ADDED 2026-08-25. Noise ceiling + within-run correction,
                              and alignment as a fraction of the ceiling. Additive:
                              replaces nothing above. Start here for whether the
                              null is real.
diagnostics/
  layerwise_alignment.csv  <- alignment at every layer (rules out the layer explanation)
  run_confound_check.csv   <- raw vs run-partialled alignment, per layer
figures/                   <- cross-model figures (fig1-fig8, tables)
superseded/early_tier1/    <- an earlier PARTIAL pass (7 families, 8/12 cells). Kept for
                              completeness, deliberately NOT a viewer config. Do not use.
provenance_tier_ledger.json <- the run record: per-tier status, exit code, duration, peak GPU

Every table carries family and model_ref, so you can filter by model in the viewer without downloading anything.

The models

10 families, 262 checkpoints total. brain_rsa_mean is shown only so you can see it is flat and near zero; per the warning above it is not interpretable.

family	ckpts	steps	brain RSA (confounded)	trend ρ (p)	interp PR	interp gini	loc selectivity	behaviour acc
pico-decoder-tiny	21	0–126k	−0.017	−0.02 (0.80)	0.221	0.204	0.546	0.631
pico-decoder-small	126	0–125k	+0.001	+0.16 (<0.001)	0.184	0.243	0.551	0.682
pico-decoder-medium	21	0–125k	−0.011	−0.10 (0.13)	0.191	0.206	0.544	0.679
pico-decoder-large	21	0–125k	−0.012	+0.01 (0.93)	0.104	0.221	0.539	0.687
beetle-humanscale-eng	18	0–9.5k	−0.016	−0.03 (0.71)	0.300	0.077	0.530	0.510
beetle-fineweb3-eng	19	0–65k	−0.015	−0.05 (0.44)	0.241	0.090	0.558	0.590
babylm-gpt2-3	9	191–1908	−0.021	+0.17 (0.07)	0.065	0.326	0.519	0.693
babylm-gpt2-5	9	191–1908	−0.022	+0.14 (0.14)	0.066	0.324	0.531	0.687
babylm-gpt2-7	9	191–1908	−0.024	+0.12 (0.21)	0.065	0.328	0.520	0.707
babylm-gpt2	9	598–2990	−0.032	−0.03 (0.73)	0.072	0.293	0.530	0.707

pico-decoder-small's trend is p<0.001 only because n = 1,512 stimulus-level rows; ρ = 0.16 on a confounded measure is not a finding. No other family reaches p < 0.05, which across ten tests is what chance looks like. The held-out cross-family predictor scores mean R² = −2.74 — worse than predicting the mean.

How to read the metrics

interp (safe). per = participation ratio, the effective dimensionality of the representation as a fraction of hidden size; lower = more compressed. Here 0.06–0.30, and the babylm models (0.065) are far more compressed than Beetle (0.24–0.30). gini and hoyer are sparsity of activation mass, higher = sparser. condition_number is the spread of the activation covariance spectrum. cka_to_prev is representational similarity to the previous checkpoint — near 1 means training has stopped changing the geometry.

localisation (safe). selectivity_index is how strongly a phenomenon's top units prefer it over other phenomena; ~0.5 across all models here, i.e. moderate and strikingly constant — no model isolates phenomena sharply. mean_overlap_with_others is how much a phenomenon's circuit is shared with other phenomena; high overlap means little specialisation. n_active_layers is how many layers contribute; layer_com is the centre of mass over depth (low = early layers).

behaviour (safe). mp_accuracy is minimal-pair accuracy, chance = 0.5. Beetle humanscale at 0.51 is essentially at chance; the babylm and pico models reach 0.63–0.71.

brain (confounded — see the warning). rsa is Spearman between the LM RDM and the brain RDM over stimulus pairs; rsa_pearson/rsa_kendall are the same with different rank treatments. n_stim is 72 for Sem/Phon and 60 for Gram/Plaus (controls excluded).

Provenance

Produced by suchirsalhan/cdl-representations-brains-babylms, tiers 0–3, 2026-08-19. Brain-side session RDMs are cached separately at BrainAlign/ds003604-session-rdms (12 of 12 task × session cells). The run confound described above applies to those RDMs too, and the recommended fix is to normalise voxel patterns within run before aggregating across runs.