suchirsalhan commited on
Commit
eaed7cf
·
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1 Parent(s): 434b945

ds006239 (whole-brain): alignment results (control gate: fail)

Browse files
README.md CHANGED
@@ -4,7 +4,7 @@ Wang et al. 2025 — word-level phonological and semantic reading tasks in child
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  - Paper: https://www.sciencedirect.com/science/article/pii/S2352340925009692
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  - Data: https://openneuro.org/datasets/ds006239/versions/1.0.5
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- - Generated: 2026-09-20
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  - Pipeline: https://github.com/suchirsalhan/cdl-representations-brains-babylms
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  ## Read this first: does the measurement work?
@@ -221,15 +221,15 @@ rather than language) and an inter-subject noise ceiling.
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  | SemLocal | ses-11+ | 48 | 0.208521 | 0.601744 | 3 |
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  | SemLocal | ses-11 | 48 | 0.216461 | 0.607091 | 3 |
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- Model grid: **15 families**, 39816 alignment rows across 8 cells.
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  | | |
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  |---|---|
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  | mean noise ceiling | 0.413 |
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- | best alignment anywhere | 0.0670 |
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  | as a fraction of ceiling | 25.6% |
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  | families equivalent to zero (TOST ±0.05) | 15/15 |
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- | Pythia scale trend | ρ = -0.288, p = 0.07 |
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  ### Per family
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@@ -245,10 +245,10 @@ Model grid: **15 families**, 39816 alignment rows across 8 cells.
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  | pythia-410m-full | 21 | -0.0046 | 0.0102 | 0.0458 | 0.2198 | 0 |
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  | beetle-fineweb3-eng | 19 | -0.0057 | 0.0119 | 0.0504 | 0.2349 | 0 |
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  | pythia-1b-full | 21 | -0.0098 | 0.0045 | 0.0461 | 0.1779 | 0 |
 
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  | pythia-1.4b-full | 21 | -0.0135 | 0.0044 | 0.0595 | 0.2116 | 0 |
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- | pythia-160m-full | 1 | -0.0174 | 0.0147 | 0.0423 | 0.1954 | 0.0002 |
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- | babylm-gpt2-7 | 4 | -0.024 | 0.0387 | 0.0629 | 0.1654 | 0.0496 |
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  | babylm-gpt2-5 | 9 | -0.0262 | 0.0336 | 0.0663 | 0.1677 | 0.0427 |
 
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  | babylm-gpt2-3 | 9 | -0.027 | 0.0336 | 0.067 | 0.1694 | 0.0471 |
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254
  ## Dataset-specific notes
 
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5
  - Paper: https://www.sciencedirect.com/science/article/pii/S2352340925009692
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  - Data: https://openneuro.org/datasets/ds006239/versions/1.0.5
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+ - Generated: 2026-09-21
8
  - Pipeline: https://github.com/suchirsalhan/cdl-representations-brains-babylms
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10
  ## Read this first: does the measurement work?
 
221
  | SemLocal | ses-11+ | 48 | 0.208521 | 0.601744 | 3 |
222
  | SemLocal | ses-11 | 48 | 0.216461 | 0.607091 | 3 |
223
 
224
+ Model grid: **15 families**, 44016 alignment rows across 8 cells.
225
 
226
  | | |
227
  |---|---|
228
  | mean noise ceiling | 0.413 |
229
+ | best alignment anywhere | 0.0691 |
230
  | as a fraction of ceiling | 25.6% |
231
  | families equivalent to zero (TOST ±0.05) | 15/15 |
232
+ | Pythia scale trend | ρ = -0.348, p = 0.03 |
233
 
234
  ### Per family
235
 
 
245
  | pythia-410m-full | 21 | -0.0046 | 0.0102 | 0.0458 | 0.2198 | 0 |
246
  | beetle-fineweb3-eng | 19 | -0.0057 | 0.0119 | 0.0504 | 0.2349 | 0 |
247
  | pythia-1b-full | 21 | -0.0098 | 0.0045 | 0.0461 | 0.1779 | 0 |
248
+ | pythia-160m-full | 21 | -0.0116 | 0.0058 | 0.0423 | 0.1954 | 0 |
249
  | pythia-1.4b-full | 21 | -0.0135 | 0.0044 | 0.0595 | 0.2116 | 0 |
 
 
250
  | babylm-gpt2-5 | 9 | -0.0262 | 0.0336 | 0.0663 | 0.1677 | 0.0427 |
251
+ | babylm-gpt2-7 | 9 | -0.0265 | 0.034 | 0.0691 | 0.1746 | 0.0457 |
252
  | babylm-gpt2-3 | 9 | -0.027 | 0.0336 | 0.067 | 0.1694 | 0.0471 |
253
 
254
  ## Dataset-specific notes
alignment_by_cell.csv CHANGED
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@@ -87,14 +87,14 @@ pythia-1.4b-full,Sem,ses-11,441,-0.013166712649478253,0.009472168670703766,-0.02
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alignment_by_checkpoint.csv CHANGED
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alignment_by_family.csv CHANGED
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  pythia-1.4b-full,1515311488.0,21,3528,8,-0.013529707048611558,0.004368473859165732,0.017007728717004136,0.0594907030612743,-0.0594907030612743,Orth/ses-11,8000,-0.034429796493913915,0.21160163711481234,,,,,False,3.1031636630886953e-08,True,
 
 
14
  babylm-gpt2-5,134601216.0,9,1512,8,-0.026206196577961242,0.03362753421562339,0.03286152671004933,0.0663433010018333,-0.0663433010018333,Orth/ses-11,1528,-0.05193137486691638,0.16765997951626055,,,,,False,0.042727188468539457,True,
15
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16
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scale_ladder.csv CHANGED
@@ -1,6 +1,6 @@
1
  family,params,n_checkpoints,n_rows,n_cells,rsa_mean,rsa_sd,rsa_sd_all_rows,rsa_abs_max,best_rsa,best_cell,best_step,frac_of_ceiling_mean,frac_of_ceiling_abs_max,seed_null_sd,null_max_p50,null_max_p95,best_percentile_vs_null,beats_null_max,p_equivalence_tost,equivalent_to_zero,mean_in_seed_sd,scale_trend_rho,scale_trend_p,scale_trend_n
2
- pythia-70m-full,95592496.0,21,3528,8,-0.0016184310609294343,0.010302304259638117,0.017592638596470866,0.0381892478191561,-0.0381892478191561,Orth/ses-11+,43000,0.0033361782410387506,0.16992331930509266,,,,,False,1.6036890683094196e-06,True,,-0.28804016412687095,0.07148667897505456,40
3
- pythia-160m-full,212654688.0,1,168,8,-0.01741688878231852,0.014720547422109256,0.013810976696669955,0.0423063352010579,-0.0423063352010579,SemLocal/ses-11,0,-0.0573768786293522,0.1954455353716226,,,,,False,0.00020991899970705303,True,,-0.28804016412687095,0.07148667897505456,40
4
- pythia-410m-full,505997504.0,21,3528,8,-0.004629722353618491,0.010222754669397309,0.01767148368669104,0.0458430479608868,0.0458430479608868,SemLocal/ses-11+,2,-0.006024377256268457,0.2198489832195564,,,,,False,2.3483028743583153e-06,True,,-0.28804016412687095,0.07148667897505456,40
5
- pythia-1b-full,1078891008.0,21,3528,8,-0.009824357400176993,0.00453911445731707,0.01569999233647885,0.046133118202689,-0.046133118202689,Orth/ses-11+,4000,-0.028790633822719115,0.17792981569917005,,,,,False,2.070041782407367e-08,True,,-0.28804016412687095,0.07148667897505456,40
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- pythia-1.4b-full,1515311488.0,21,3528,8,-0.013529707048611558,0.004368473859165732,0.017007728717004136,0.0594907030612743,-0.0594907030612743,Orth/ses-11,8000,-0.034429796493913915,0.21160163711481234,,,,,False,3.1031636630886953e-08,True,,-0.28804016412687095,0.07148667897505456,40
 
1
  family,params,n_checkpoints,n_rows,n_cells,rsa_mean,rsa_sd,rsa_sd_all_rows,rsa_abs_max,best_rsa,best_cell,best_step,frac_of_ceiling_mean,frac_of_ceiling_abs_max,seed_null_sd,null_max_p50,null_max_p95,best_percentile_vs_null,beats_null_max,p_equivalence_tost,equivalent_to_zero,mean_in_seed_sd,scale_trend_rho,scale_trend_p,scale_trend_n
2
+ pythia-70m-full,95592496.0,21,3528,8,-0.0016184310609294343,0.010302304259638117,0.017592638596470866,0.0381892478191561,-0.0381892478191561,Orth/ses-11+,43000,0.0033361782410387506,0.16992331930509266,,,,,False,1.6036890683094196e-06,True,,-0.34779317689787076,0.027874566812770417,40
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4
+ pythia-410m-full,505997504.0,21,3528,8,-0.004629722353618491,0.010222754669397309,0.01767148368669104,0.0458430479608868,0.0458430479608868,SemLocal/ses-11+,2,-0.006024377256268457,0.2198489832195564,,,,,False,2.3483028743583153e-06,True,,-0.34779317689787076,0.027874566812770417,40
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6
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seed_null_comparison.csv CHANGED
@@ -9,8 +9,8 @@ pythia-70m-full,-0.0016184310609294343,0.010302304259638117,,0.0381892478191561,
9
  pythia-410m-full,-0.004629722353618491,0.010222754669397309,,0.0458430479608868,,,,False,0.2198489832195564,2.3483028743583153e-06,True
10
  beetle-fineweb3-eng,-0.00571925756194484,0.011927107244843757,,0.0504311720271701,,,,False,0.2348562597747442,7.743900944666366e-06,True
11
  pythia-1b-full,-0.009824357400176993,0.00453911445731707,,0.046133118202689,,,,False,0.17792981569917005,2.070041782407367e-08,True
 
12
  pythia-1.4b-full,-0.013529707048611558,0.004368473859165732,,0.0594907030612743,,,,False,0.21160163711481234,3.1031636630886953e-08,True
13
- pythia-160m-full,-0.01741688878231852,0.014720547422109256,,0.0423063352010579,,,,False,0.1954455353716226,0.00020991899970705303,True
14
- babylm-gpt2-7,-0.024014363286046055,0.038687038819549005,,0.0629479367791577,,,,False,0.16539540349552803,0.04961600976194912,True
15
  babylm-gpt2-5,-0.026206196577961242,0.03362753421562339,,0.0663433010018333,,,,False,0.16765997951626055,0.042727188468539457,True
 
16
  babylm-gpt2-3,-0.027009846424525894,0.03359839173634999,,0.0670413510933163,,,,False,0.1694240621330741,0.047086074805349175,True
 
9
  pythia-410m-full,-0.004629722353618491,0.010222754669397309,,0.0458430479608868,,,,False,0.2198489832195564,2.3483028743583153e-06,True
10
  beetle-fineweb3-eng,-0.00571925756194484,0.011927107244843757,,0.0504311720271701,,,,False,0.2348562597747442,7.743900944666366e-06,True
11
  pythia-1b-full,-0.009824357400176993,0.00453911445731707,,0.046133118202689,,,,False,0.17792981569917005,2.070041782407367e-08,True
12
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13
  pythia-1.4b-full,-0.013529707048611558,0.004368473859165732,,0.0594907030612743,,,,False,0.21160163711481234,3.1031636630886953e-08,True
 
 
14
  babylm-gpt2-5,-0.026206196577961242,0.03362753421562339,,0.0663433010018333,,,,False,0.16765997951626055,0.042727188468539457,True
15
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16
  babylm-gpt2-3,-0.027009846424525894,0.03359839173634999,,0.0670413510933163,,,,False,0.1694240621330741,0.047086074805349175,True
summary.json CHANGED
@@ -2,25 +2,25 @@
2
  "dataset": "ds003604",
3
  "rdms": "within-run normalised",
4
  "n_families": 15,
5
- "n_rows": 39816,
6
  "n_cells": 8,
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  "ceiling_lower_mean": 0.41321425967386266,
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  "best_frac_of_ceiling": 0.25570031833378853,
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  "null_max_p95_median": null,
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  "families_beating_null_max": 0,
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  "families_equivalent_to_zero": 15,
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  "sesoi": 0.05,
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- "scale_trend_rho": -0.28804016412687095,
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- "scale_trend_p": 0.07148667897505456,
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- "var_pct_cell": 6.789232648684831,
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- "var_pct_family": 15.488458653878133,
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- "var_pct_residual": 77.71979714429256,
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  "untrained_mean": -0.0004713113131062314,
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- "trained_mean": -0.004977801679016215,
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- "untrained_vs_trained_t": 8.908041480258543,
25
- "untrained_vs_trained_p": 1.1199235554859179e-18
26
  }
 
2
  "dataset": "ds003604",
3
  "rdms": "within-run normalised",
4
  "n_families": 15,
5
+ "n_rows": 44016,
6
  "n_cells": 8,
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  "ceiling_lower_mean": 0.41321425967386266,
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  "seed_null_sd_median": null,
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  "families_beating_null_max": 0,
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  "families_equivalent_to_zero": 15,
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  "sesoi": 0.05,
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+ "scale_trend_rho": -0.34779317689787076,
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+ "var_pct_cell": 7.472015642678291,
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+ "var_pct_family": 16.535363578806578,
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+ "var_pct_residual": 75.99034887738824,
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  "untrained_mean": -0.0004713113131062314,
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  "untrained_n": 1848,
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+ "trained_mean": -0.0059473200025642586,
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+ "trained_n": 42168,
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+ "untrained_vs_trained_p": 1.054920226082161e-26
26
  }
training_trend.csv CHANGED
@@ -1,5 +1,6 @@
1
  family,n_cells,trend_rho_mean,trend_rho_sd,t,p
2
  pico-decoder-tiny,8,0.5797077922077922,0.17834873954120123,9.19356788349189,3.713576635563178e-05
 
3
  beetle-fineweb3-eng,8,0.26120426982322076,0.3665172601059781,2.0157229202627396,0.0836565306663773
4
  babylm-gpt2,8,0.22083333333333335,0.7566504088876456,0.8254948159829317,0.436316535725668
5
  babylm-gpt2-3,8,0.12291666666666667,0.594147214668564,0.5851425799872229,0.5768192899719713
@@ -7,6 +8,7 @@ beetle-humanscale-eng,8,0.0877710206986349,0.5884720615813783,0.4218618893198684
7
  pythia-70m-full,8,-0.0006495615802152022,0.3370402938988867,-0.005451091830654687,0.9958027762507843
8
  babylm-gpt2-5,8,-0.012499999999999956,0.7437437474727179,-0.04753698996390442,0.9634130778478115
9
  pythia-1b-full,8,-0.04352062587441752,0.6458059075280298,-0.190606678065717,0.854243899663109
 
10
  pythia-1.4b-full,8,-0.11286132456238859,0.3863319424291648,-0.8262843339327898,0.43589743648595614
11
  pico-decoder-small,8,-0.12987012987012986,0.2590966050866248,-1.4177267891880538,0.1992123167581927
12
  pico-decoder-medium,8,-0.23831168831168834,0.31561228184229845,-2.1356812841068056,0.0700798043177341
 
1
  family,n_cells,trend_rho_mean,trend_rho_sd,t,p
2
  pico-decoder-tiny,8,0.5797077922077922,0.17834873954120123,9.19356788349189,3.713576635563178e-05
3
+ babylm-gpt2-7,8,0.37916666666666665,0.42852182252593907,2.502662007918349,0.04083287051082103
4
  beetle-fineweb3-eng,8,0.26120426982322076,0.3665172601059781,2.0157229202627396,0.0836565306663773
5
  babylm-gpt2,8,0.22083333333333335,0.7566504088876456,0.8254948159829317,0.436316535725668
6
  babylm-gpt2-3,8,0.12291666666666667,0.594147214668564,0.5851425799872229,0.5768192899719713
 
8
  pythia-70m-full,8,-0.0006495615802152022,0.3370402938988867,-0.005451091830654687,0.9958027762507843
9
  babylm-gpt2-5,8,-0.012499999999999956,0.7437437474727179,-0.04753698996390442,0.9634130778478115
10
  pythia-1b-full,8,-0.04352062587441752,0.6458059075280298,-0.190606678065717,0.854243899663109
11
+ pythia-160m-full,8,-0.047742776145816235,0.6743690818335736,-0.20024192493278858,0.8469846760826647
12
  pythia-1.4b-full,8,-0.11286132456238859,0.3863319424291648,-0.8262843339327898,0.43589743648595614
13
  pico-decoder-small,8,-0.12987012987012986,0.2590966050866248,-1.4177267891880538,0.1992123167581927
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  pico-decoder-medium,8,-0.23831168831168834,0.31561228184229845,-2.1356812841068056,0.0700798043177341