BrainAlign/brain-lm-alignment-ds006239

Brain–language-model alignment: ds006239 (whole-brain)

Wang et al. 2025 — word-level phonological and semantic reading tasks in children and adolescents aged 10–17.

• —Paper: https://www.sciencedirect.com/science/article/pii/S2352340925009692
• —Data: https://openneuro.org/datasets/ds006239/versions/1.0.5
• —Generated: 2026-09-21
• —Pipeline: https://github.com/suchirsalhan/cdl-representations-brains-babylms

Read this first: does the measurement work?

Every alignment number in this dataset is only as meaningful as the brain RDMs it was computed against. So before any model result, the same pipeline is asked whether anything stimulus-driven correlates with those RDMs — stimulus duration, intensity, word length, frequency, phoneme and syllable counts, an acoustic model of the audio where the stimuli are audio, and the study's own condition contrast — each tested by a permutation test that shuffles stimulus identity.

GATE: FAILED. 0/38 stimulus tests are significant after Holm correction — not the acoustic model of the audio the children actually heard, not the study's own experimental contrast.

The alignment numbers below are therefore uninterpretable as evidence about language models. They measure a representational geometry that does not demonstrably encode the stimuli. They are published for completeness and for whoever fixes the estimator, not as a result. Do not cite them as evidence that models fail to align with the developing brain.

Measured cause, from control/:

• —RDM effective rank: 61 of 84 stimuli

Note that this is NOT ds003604's failure mode. There, the RDM effective rank was ~3 of 40-48 stimuli -- near-degenerate betas that could not express stimulus-level structure at all. The rank recorded above is a large fraction of the stimulus count, so these RDMs do carry stimulus structure and the control failing here means the specific controls tested did not reach significance, not that the measurement is uninterpretable. Check control/ for which controls ran: an acoustic or visual control needs the dataset's stimulus files present, and reports zero features if they are not.

What was built

168 task × session cells, each an RDM over the stimuli shared by that cell's subjects, with voxel patterns z-scored within run before aggregation (without that, the RDM measures scanner drift rather than language) and an inter-subject noise ceiling.

Model grid: 15 families, 44016 alignment rows across 8 cells.

Per family

Dataset-specific notes

Contains LocalSem, the only genuinely run/stimulus-CROSSED language cell across all four datasets in this project: its stimuli recur across runs, so run identity and stimulus identity are separable and the scanner-run confound that invalidated the first ds003604 analysis cannot arise. Per-subject age is NOT recoverable from the release — participants.tsv has birthdate but no scan date and there are no *_scans.tsv files — so this dataset is cohort-level only and cannot carry the developmental axis as published.

Files

Method

Representational similarity analysis. For each cell, a brain RDM over stimuli (correlation distance between per-stimulus GLM beta patterns, within-run z-scored, aggregated across subjects) is compared by Spearman correlation with a model RDM over the same stimuli, taken from each checkpoint's hidden states. Alignment is reported raw and as a fraction of the inter-subject noise ceiling, and judged against a null built from the PARC suite — 18 models differing only by random seed, which is what 'no effect' looks like on this measurement.

Null and fixation trials are excluded from the stimulus set. For paired designs the stimulus identity is the pair, not either word alone.