license: mit
task_categories:
- feature-extraction
tags:
- brain-alignment
- fmri
- rsa
- developmental
- null-result
Lytle et al. (2020) — LM brain-alignment results
Language-model / fMRI representational-alignment results for Lytle et al. (2020): orthographic, phonological and semantic word processing in school-aged children (8.7-15.5), auditory and visual
OpenNeuro accession ds002236 · cohort: school-aged children, ages 8.7-15.5 · modality: auditory and visual
· tasks: Phon, Sem · cells: 6 · rows: 924
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: 0 of 30, against 3.0 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.