| --- |
| 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/`](./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. |
|
|
| ### 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`](https://github.com/suchirsalhan/cdl-representations-brains-babylms), |
| tiers 0–3, 2026-08-19. Brain-side session RDMs are cached separately at |
| [`BrainAlign/ds003604-session-rdms`](https://huggingface.co/datasets/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. |
|
|
