SlowGuess/NAACL_2022_OCR huggingface dataset:
- EAC_L [F(9,810) = 3.88,  $p = .00009$ ]
- EAC_R [F(9,810) = 3.34,  $p = .00055$ ]
- AAC_L [F(9,810)=5.37, p=.0000007]
- AAC_R [F(9,810)=6.955, p=.00000]
- PMC_L [F(9,810)=37.21,  $p = .00000$ ]
- PMC_R [F(9,810)=31.62,  $p = .00000$ ]
- TPOJ_L [F(9,810)=9.166,  $p = .00000$ ]
- TPOJ_R [F(9,810)=7.797, p=.00000]
- DFL_L [F(9,810) = 12.445,  $p = .00000$ ]
- DFL_R [F(9,810)=12.27, p=.00000]
Task	HuggingFace Model Name	Dataset	URL
NLI	bert-base-nli-mean-tokens	Stanford Natural Language Inference (SNLI), MultiNLI	https://huggingface.co/sentence-transformers/bert-base-nli-mean-tokens
PD	bert-base-cased-finetuned-mrpc	Microsoft Research Paraphrase Corpus (MRPC)	https://huggingface.co/bert-base-cased-finetuned-mrpc
SS	bert-base-chunl	CoNLL-2003	https://huggingface.co/vblagoje/bert-english-uncased-finetuned-chunk
Sum	bart-base-samsum	SAMSum	https://huggingface.co/lidiya/bart-base-samsum
WSD	bert-base-baseline	English all-words	https://github.com/BPYap/BERT-WSD
CR	bert_coreference_base	OntoNotes and GAP	https://github.com/mandarjoshi90/coref
NER	bert-base-NER	CoNLL-2003	https://huggingface.co/dslim/bert-base-NER
QA	bert-base-qa	SQUAD	https://huggingface.co/docs/transformers/model_doc/bert#bertforquestionanswering
SA	bert-base-sst	Stanford Sentiment Treebank (SST)	https://huggingface.co/barissayil/bert-sentiment-analysis-sst
SRL	bert-base-srl	English PropBank SRL	https://s3-us-west-2.amazon.com/allennlp/models/bert-base-srl-2020.02.10.tar.gz
Table 3: Details of the finetuned models
T1	T2	p-value
CR	QA	0.024
CR	SA	0.015
CR	NLI	0.010
Table 4: Pairwise comparison one-way ANOVA results for Language_LH region
T1	T2	p-value
CR	SS	0.021
CR	SRL	0.0003
CR	Sum	0.003
CR	QA	0.039
CR	SA	0.013
CR	WSD	0.016
Table 5: Pairwise comparison one-way ANOVA results for Language_RH region
T1	T2	p-value
CR	SRL	0.0011
CR	Sum	0.0092
CR	SA	0.039
CR	NLI	0.0061
Table 6: Pairwise comparison one-way ANOVA results for Vision_body region
T1	T2	p-value
CR	SA	0.0404
CR	nli	0.036
Table 7: Pairwise comparison one-way ANOVA results for Vision_face region
T1	T2	p-value
CR	SRL	0.0027
Table 8: Pairwise comparison one-way ANOVA results for Vision_object region
T1	T2	p-value
CR	Sum	0.027
CR	QA	0.0036
CR	SA	0.0022
CR	NLI	0.0010
Table 9: Pairwise comparison one-way ANOVA results for Visionscene region
T1	T2	p-value
CR	SRL	0.0014
CR	Sum	0.0431
CR	NLI	0.0177
Table 10: Pairwise comparison one-way ANOVA results for Vision region
T1	T2	p-value
CR	NLI	0.027
CR	Sum	0.008
CR	PD	0.0147
NLI	SA	0.056
NLI	SS	0.000011
SA	Sum	0.0188
SA	PD	0.032
SS	Sum	0.000002
SS	WSD	0.0059
SS	PD	0.000004
SRL	Sum	0.0545
SRL	PD	0.08876
Table 11: Pairwise comparison one-way Anova results for EAC-L region
T1	T2	p-value
NLI	SS	0.00157
Sum	SS	0.0015
PD	SS	0.002
SA	SS	0.0565
SS	WSD	0.052
Table 12: Pairwise comparison one-way Anova results for EAC-R region
T1	T2	p-value
NLI	SS	0.000007
SA	SS	0.029
SS	SRL	0.0084
SS	PD	0.000023
SS	QA	0.00128
Table 13: Pairwise comparison one-way Anova results for AAC-L region
Figure 9: Narratives-Lucy Dataset: 2V2 Accuracy (top figure) and Pearson correlation coefficient (bottom figure) between predicted and true responses across different brain regions using a variety of NLP tasks. Results are averaged across all participants. NLI, Paraphrase, and Summarisation perform the best.
T1	T2	p-value
CR	NLI	0.0203
CR	PD	0.0072
NER	PD	0.0291
NLI	SS	0.0000013
SA	SS	0.0299
SS	SRL	0.0011
SS	PD	2.97929e-7
SS	WSD	0.0444
SS	QA	0.00099
PD	Sum	0.039
Table 14: Pairwise comparison one-way Anova results for AAC-R region
T1	T2	p-value
CR	NER	1.07034e-10
CR	NLI	0.000001
CR	SRL	0.0014
CR	PD	0.0000047
CR	QA	0.0023
NER	NLI	9.02023e-11
NER	SA	9.02993e-11
NER	SS	0.000157159
NER	SRL	9.02116e-11
NER	PD	9.02023e-11
NER	Sum	9.03064e-11
NER	WSD	9.03172e-11
NER	QA	9.02116e-11
NLI	SA	0.0207013
NLI	SS	9.03255e-11
NLI	Sum	0.0043
NLI	WSD	0.00036
SA	SS	0.0000072
SS	SRL	4.47012e-10
SS	PD	9.04392e-11
SS	Sum	0.00011
SS	WSD	0.00084
SS	QA	6.36666e-10
PD	Sum	0.012
PD	WSD	0.0012
Table 15: Pairwise comparison one-way Anova results for PMC-L region
T1	T2	p-value
CR	NER	1.52787e-9
CR	NLI	0.0000042
CR	SS	0.0039
CR	PD	0.00011
CR	QA	0.0101
NER	NLI	8.86012e-11
NER	SA	8.87732e-11
NER	SRL	8.88714e-11
NER	PD	8.86092e-11
NER	Sum	1.05034e-10
NER	WSD	1.01319e-10
NER	QA	8.86657e-11
NLI	SA	0.0059
NLI	SS	8.87066e-11
NLI	Sum	0.000371
NLI	WSD	0.000191
SA	SS	0.0000021
SS	SRL	0.00000142
SS	PD	8.87554e-11
SS	Sum	0.000126402
SS	WSD	0.000128239
SS	QA	1.31249e-10
PD	Sum	0.00619
PD	WSD	0.0036
Table 16: Pairwise comparison one-way Anova results for PMC-R region
Figure 10: Narratives-Slumlord Dataset: 2V2 Accuracy (top figure) and Pearson correlation coefficient (bottom figure) between predicted and true responses across different brain regions using a variety of NLP tasks. Results are averaged across all participants. NLI, Paraphrase, and Summarisation perform the best.
T1	T2	p-value
CR	NLI	0.00069
CR	PD	0.00395
NER	NLI	0.0051
NER	SS	0.0286244
NER	PD	0.0235
NLI	SS	4.43074e-10
NLI	Sum	0.0068987
NLI	WSD	0.02709
SA	SS	0.0001732
SS	SRL	0.0000530
SS	PD	4.37008e-9
SS	Sum	0.0219850
SS	WSD	0.005447
SS	QA	0.0000016
PD	Sum	0.0306
Table 17: Pairwise comparison one-way Anova results for TPOJ-L region
T1	T2	p-value
CR	NLI	0.000032
CR	PD	0.000019
NER	NLI	0.000619887
NER	SS	0.040
NER	PD	0.000399
NLI	SS	1.61916e-10
NLI	Sum	0.00074
NLI	WSD	0.000462932
SA	SS	0.000221241
SS	SRL	0.0000123345
SS	PD	1.30279e-10
SS	Sum	0.0356814
SS	WSD	0.0496343
SS	QA	0.00000162
PD	Sum	0.0004803
PD	WSD	0.000296713
Table 19: Pairwise comparison one-way Anova results for DFL-L region
T1	T2	p-value
CR	NLI	0.0064
CR	PD	0.0148564
NER	NLI	0.0449
NLI	SS	3.74353e-8
NLI	WSD	0.0321627
SA	SS	0.0036278
SS	SRL	0.001054
SS	PD	1.33146e-7
SS	Sum	0.025420
SS	QA	0.000049
Table 18: Pairwise comparison one-way Anova results for TPOJ-R region
T1	T2	p-value
CR	NLI	0.000191
CR	PD	0.00010
NER	NLI	0.0168115
NER	SS	0.0168115
NER	PD	0.000674
NLI	SS	1.05897e-10
NLI	Sum	0.001194
NLI	WSD	0.003894
SA	SS	0.0000256
SS	SRL	0.00000224
SS	PD	9.81710e-11
SS	Sum	0.0165866
SS	WSD	0.0057237
SS	QA	2.98083e-7
PD	Sum	0.000685
PD	WSD	0.00231873
Table 20: Pairwise comparison one-way Anova results for DFL-R region
T1	T2	p-value
CR	NLI	0.0188070
CR	PD	0.0099703
NER	PD	0.0321010
NLI	SS	6.37802e-7
SA	SS	0.00642888
SS	SRL	0.00051148
SS	PD	2.42829e-7
SS	QA	0.000162
PD	WSD	0.0476
Table 21: Pairwise comparison one-way Anova results for VC-L region
T1	T2	p-value
CR	NLI	0.00498313
CR	PD	0.000298933
NER	NLI	0.024695
NER	PD	0.0020556
NLI	SS	4.16645e-8
NLI	Sum	0.0449825
NLI	WSD	0.0352242
SA	SS	0.00120394
SS	SRL	0.00002939
SS	PD	7.70669e-10
SS	Sum	0.0417081
SS	QA	0.00000742881
PD	Sum	0.00434934
PD	WSD	0.0031
Table 22: Pairwise comparison one-way Anova results for VC-R region
Figure 11: Dendrogram constructed using similarity matrix constructed from the task-wise brain response predictions across 10 tasks for subjects 1, 2 and 7 in Pereira Dataset
Figure 12: Dendrogram constructed using similarity matrix constructed from the task-wise brain response predictions across 10 tasks for subjects 1, 21 and 31 in Narratives Dataset
Figure 13: Pereira Dataset - Pearson correlation coefficient between predicted and true responses across different sub ROIs of the Language Network using SRL task. Results are averaged across all participants.
Figure 14: Pereira Dataset - Pearson correlation coefficient between predicted and true responses across different sub ROIs of the Language Network using CR task. Results are averaged across all participants.
Figure 15: Narratives-Pieman - Pearson correlation coefficient between predicted and true responses across different sub ROIs of 5 brain ROIs using paraphrase task. Results are averaged across all participants.
Figure 16: Narratives-Pieman - Pearson correlation coefficient between predicted and true responses across different sub ROIs of 5 brain ROIs using summarization task. Results are averaged across all participants.
Figure 17: Pereira BrainMaps: Mean absolute error (MAE) between predictive voxels and actual voxels using task features from Taskonomy in one sample subject (subject 1). Predictive regions of different tasks are dissimilar across tasks.
Figure 18: Narratives BrainMaps: Mean absolute error (MAE) between predictive voxels and actual voxels using task features from Taskonomy in one sample subject (subject 1) of PieMan dataset. Predictive regions of various tasks are different across tasks.