license: mit
task_categories:
- feature-extraction
tags:
- brain-alignment
- fmri
- rsa
- developmental
- null-result
Wang et al. (2025) — LM brain-alignment results
Language-model / fMRI representational-alignment results for Wang et al. (2025): word-level phonological and semantic reading tasks in children and adolescents aged 10-17
OpenNeuro accession ds006239 · cohort: children and adolescents, ages 10-17 · modality: reading (visual)
· tasks: Orth, Phon, Sem, SemLocal · cells: 8 · rows: 1232
Layout follows BrainAlign/cdl-devai-results:
by-model/<family>/{brain_alignment,checkpoints}.csv, overall/,
ceiling-analysis/, provenance_tier_ledger.json.
Result: alignment is not distinguishable from a random seed
5 trained families (pythia-1.4b-full, pythia-160m-full, pythia-1b-full, pythia-410m-full, pythia-70m-full) and 9 PARC noise-seed baselines, scored identically. Cells where a real family exceeds all 9 noise seeds: 5 of 40, against 4.0 expected by chance.
Pooled across all three developmental datasets: 9/130 observed vs 13.0 expected (p = 0.91); per-cell means of real families vs noise seeds correlate at r = +0.863; 83.9% of variance is cell identity and 3.2% model family.
Interpretation
These RDMs have real inter-subject reliability (noise ceilings in
ceiling-analysis/ceilings.csv), but the upstream pipeline's positive controls
fail on this dataset. The claim supported here is "no LM alignment is
detectable by this measurement" — not "language models do not align with the
developing brain." Read this as a result about the benchmark.
Negative results are published here deliberately: the per-cell nulls and the noise-seed baselines are the reusable part.