Datasets:
Restructure: per-model / per-checkpoint layout with brain, interp and localisation side by side
d050504 verified | # pico-decoder-tiny | |
| **pico decoder, smallest of the scale ladder** — [`pico-lm/pico-decoder-tiny`](https://huggingface.co/pico-lm/pico-decoder-tiny) · dense commit-history checkpoints. | |
| 21 checkpoints, steps 0–126000. | |
| > ## ⚠️ The brain alignment numbers on this page are confounded | |
| > | |
| > `brain_alignment.csv`, every `brain_*` column in `checkpoints.csv`, and | |
| > `ablation_alignment.csv` are **not interpretable as a result about this model.** | |
| > | |
| > In ds003604 each stimulus appears in exactly one scanner run, so run membership is | |
| > perfectly confounded with stimulus identity. "Different run" predicts brain | |
| > dissimilarity at Spearman **+0.49 to +0.87** across all 12 task × session cells, while | |
| > no stimulus property predicts it at all (trial type ≈ −0.02, length ≈ 0, lexical | |
| > overlap ≈ 0). A language model cannot represent a scanner run, so its RSA against that | |
| > structure is ≈0 by construction. | |
| > | |
| > Ruled out as explanations: **cohort size** (40 vs 98 subjects agree at rho 0.928) and | |
| > **layer choice** (flat across all 12–14 layers). Within-run normalisation removes the | |
| > confound (+0.562 → −0.041) and leaves alignment at **+0.022, not significant**. | |
| > | |
| > **`interp_*`, `localisation_*`, `behaviour` and `ablation_behaviour` on this page are | |
| > unaffected** — they are computed from the model alone and never touch the fMRI data. | |
| > See the [repo README](../../README.md) for the full account. | |
| ## Headline numbers | |
| | metric | value | safe to use? | | |
| |---|---|---| | |
| | brain RSA (mean over checkpoints × 12 cells) | -0.0167 | ❌ confounded | | |
| | brain RSA vs training step | rho -0.016, p 0.799 (n=252) | ❌ confounded | | |
| | interp: participation ratio (effective dim / hidden) | 0.2209 | ✅ | | |
| | interp: gini (activation sparsity) | 0.2042 | ✅ | | |
| | interp: hoyer sparsity | 0.0933 | ✅ | | |
| | interp: CKA to previous checkpoint | 0.9353 | ✅ | | |
| | localisation: selectivity index | 0.5459 | ✅ | | |
| | localisation: mean overlap with other phenomena | 0.0030 | ✅ | | |
| | localisation: active layers | 7.0595 | ✅ | | |
| | localisation: layer centre of mass | 0.6129 | ✅ | | |
| | behaviour: minimal-pair accuracy (chance 0.5) | 0.6311 | ✅ | | |
| ## What this model shows | |
| - Minimal-pair accuracy **0.631**, modestly above chance. | |
| - Participation ratio **0.221**: the representation uses ~22% of its available dimensions. Mid-range for this release. | |
| - Selectivity **0.546** — like every model in this release, phenomena are only moderately isolated. Nothing here shows sharp, dedicated linguistic circuitry. | |
| - CKA to the previous checkpoint reaches 0.9911 by the end: **the representation has stopped changing**, so late checkpoints are near-duplicates. | |
| - Brain alignment: **no claim is made**, see the warning above. | |
| ## Files | |
| | file | contents | confounded? | | |
| |---|---|---| | |
| | `behaviour.csv` | minimal-pair accuracy per phenomenon per checkpoint | no | | |
| | `brain_alignment.csv` | RSA per task × session × checkpoint | **yes — whole file** | | |
| | `checkpoints.csv` | this model's rows of the main per-checkpoint table (all three axes) | brain_* columns only | | |
| | `interp_layerwise.csv` | the same metrics per layer per checkpoint | no | | |
| | `interp_mechanistic.csv` | per-checkpoint representation metrics | no | | |
| | `localisation_isolation.csv` | per-phenomenon circuit localisation | no | | |
| | `localisation_onset.csv` | localisation onset step per phenomenon | no | | |
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