Original: T RAINING LANGUAGE MODELS FOR DEEPER UNDERSTANDING IMPROVES BRAIN ALIGNMENT
Revised: T RAINING LANGUAGE MODELS FOR DEEPER UNDERSTANDING IMPROVES BRAIN ALIGNMENT

Original: Anonymous authors
Revised: Anonymous authors

Original: Paper under double-blind review
Revised: Paper under double-blind review

Original: A BSTRACT
Revised: A BSTRACT

Original: Building systems that achieve a deeper understanding of language is one of the central goals of natural language processing NLP)
Revised: Building systems that achieve a deeper understanding of language is one of the central goals of natural language processing (NLP).

Original: Towards this goal, recent works have begun to train language models on narrative datasets which require extracting the most critical information by integrating across long contexts
Revised: Towards this goal, recent works have begun to train language models on narrative datasets which require extracting the most critical information by integrating across long contexts.

Original: However, it is still an open question whether these models are learning a deeper understanding of the text or if the models are simply learning a heuristic to complete the task.
Revised: However, it is still an open question whether these models are learning a deeper understanding of the text, or if the models are simply learning a heuristic to complete the task.

Original: This work investigates this further by turning to the one language processing system that truly understands complex language: the human brain.
Revised: This work investigates this further by turning to the one language processing system that truly understands complex language: the human brain.

Original: We show that training language models for deeper narrative understanding results in richer representations that have improved alignment to human brain activity.
Revised: We show that training language models for deeper narrative understanding results in richer representations that have improved alignment to human brain activity.

Original: We further find that the improvements in brain alignment are larger for character names than for other discourse features, which indicates that these models are learning important narrative elements.
Revised: We further find that the improvements in brain alignment are larger for character names than for other discourse features, which indicates that these models are learning important narrative elements.

Original: Taken together, these results suggest that this type of training can indeed lead to deeper language understanding.
Revised: Taken together, these results suggest that this type of training can indeed lead to deeper language understanding.

Original: These findings have consequences both for cognitive neuroscience by revealing some of the significant factors behind brain-NLP alignment and for NLP by highlighting that understanding of long-range context can be improved beyond language modeling.
Revised: These findings have consequences both for cognitive neuroscience by revealing some of the significant factors behind brain-NLP alignment, and for NLP by highlighting that understanding of long-range context can be improved beyond language modeling.

Original:  I NTRODUCTION
Revised:  I NTRODUCTION

Original: Language models that have been trained to predict the next word over millions of text documents have led to large improvements on a range of benchmark tasks in natural language processing (NLP).
Revised: Language models that have been trained to predict the next word over millions of text documents have led to large improvements on a range of benchmarks in natural language processing (NLP).

Original: However, researchers have shown that  models may be learning to rely on shallow heuristics to perform these tasks rather than learning a deeper language understanding (McCoy et al., 2019;
Revised: However, researchers have shown that NLP models may be relying on shallow heuristics to perform these tasks rather than learning a deeper language understanding (McCoy et al., 2019;

Original: Min et al., 2020; Linzen, 2020).
Revised: Min et al., 2020; Linzen, 2020).

Original: To build systems with deeper language understanding, recent workhas proposed to train language models on narrative datasets which require extracting the most critical information by integrating across long contexts (Kryscinski et al., 2021; Sang et al., 2022;
Revised: To build systems with deeper language understanding, recent work proposed to train language models on narrative datasets which require extracting the most critical information by integrating across long contexts (Kryscinski et al., 2021; Sang et al., 2022;

Original: Ko ˇ cisk ` y et al., 2018).
Revised: Ko ˇ cisk ` y et al., 2018).

Original: Does this approach truly lead to deeper understanding of language?
Revised: Does this approach truly lead to deeper understanding of language?

Original: We investigate thisfurther by turning to the one language processing system that truly understands complex language: the human brain.
Revised: We investigate this by turning to the one language processing system that truly understands complex language: the human brain.

Original: Researchers have previously used human brain recordings to interpret representations learned by pretrained language models (LMs)
Revised: Prior work has used human brain recordings to interpret representations of pretrained language models (LMs)

Original: (Søgaard, 2016; Toneva & Wehbe, 2019; Abdou et al., 2021).
Revised: (Søgaard, 2016; Toneva & Wehbe, 2019; Abdou et al., 2021).

Original: This is done by evaluating how well representations obtained from a language model can predict representations sampled from the human brain during language comprehension via a brain imaging device, such as functional magnetic resonance imaging (fMRI).
Revised: They evaluate how well representations obtained from a language model can predict representations sampled from the human brain during language comprehension via a brain imaging device, such as functional magnetic resonance imaging (fMRI).

Original: If this prediction performance, also known as brain alignment, is determined to be significant via a statistical test, then the LM and specific brain location or timepoint are thought to significantly align in their representations of language.
Revised: If this prediction performance, also known as brain alignment, is determined to be significant via a statistical test, then the LM and specific brain location or timepoint are thought to significantly align in their representations of language.

Original: Using these methods, researchers have shown that pretrained language models significantly predict large parts of the brain regions that are thought to underlie language comprehension (Wehbe et al., 2014b; Jain & Huth, 2018; Toneva & Wehbe, 2019; Caucheteux & King, 2020; Schrimpf et al., 2021; Goldstein et al., 2022).
Revised: Using these methods, researchers have shown that pretrained language models significantly predict large parts of the brain regions that are thought to underlie language comprehension (Wehbe et al., 2014b; Jain & Huth, 2018; Toneva & Wehbe, 2019; Caucheteux & King, 2020; Schrimpf et al., 2021; Goldstein et al., 2022).

Original: In this work, we draw insights from the human brainin order to investigate whether language models are truly learning deeper language understanding.
Revised: In this work, we draw insights from the human brain to study whether language models are truly learning deeper language understanding.

Original: We do this by analyzing the effect of training an LM for deeper language understanding on its alignment with fMRI recordings of participants reading a chapter of a popular book.
Revised: We analyze the effect of training LMs for deeper language understanding on their alignment with fMRI recordings of human subjects reading a book chapter.

Original: A significant improvement in brain alignment would indicate that the model has learned additional brain-relevant information, which can be then analyzed further to reveal the specific reasons for this improvement.

Original: We specifically investigate 4 pretrained language models (i.e. “base models”) and 4 corresponding models which are obtained by training the base models on the BookSum dataset (Kryscinski et al., 2021) to improve the base language model’s narrative understanding (i.e. “booksum models”).
Revised: We specifically investigate 4 pretrained language models (i.e. “base models”) and 4 corresponding models which are obtained by training the base models on the BookSum dataset (Kryscinski et al., 2021) to improve the base language model’s narrative understanding (i.e. “booksum models”).

Original: The
Revised: The

Original: BookSum dataset was selected because it is a summarization dataset that requires understanding complex interactions across long narratives, and the 4 models were selected because their architectures were designed to integrate information across long contexts.
Revised: BookSum dataset was selected because it is a summarization dataset that requires understanding complex interactions across long narratives, and the 4 models were selected because their architectures were designed to integrate information across long contexts.

Original: We evaluate the alignment of the base and booksum models with fMRI recordings of 8 participants reading a chapter of a popular book word-by-word, made publicly available by Wehbe et al.
Revised: We evaluate the alignment of the base and booksum models with fMRI recordings of 8 participants reading a chapter of a popular book word-by-word, made publicly available by Wehbe et al.

Original: (2014a).
Revised: (2014a).

Original: This dataset was chosen because it is one of the largest datasets of participants processing a narrative story (5176 words which corresponds to approximately 1300 samples of fMRI recordings per participant).
Revised: This dataset was chosen because it is one of the largest datasets of participants processing a narrative story (5176 words which corresponds to approximately 1300 samples of fMRI recordings per participant).

Original: Our main contributions are as follows:
Revised: Our main contributions are as follows:

Original: In Section 4, we show that training language models for deeper narrative understanding improves alignment to human brain activity.
Revised: In Section 4, we show that training language models for deeper narrative understanding improves alignment to human brain activity.

Original: Also, when increasing the number of words fed to the models, up to 500 words, brain alignment increases.
Revised: Also, when increasing the number of words fed to the models, up to 500 words, brain alignment increases.

Original: Lastly, for each model, we identify the layers where these improvements in brain alignment occur.
Revised: Lastly, for each model, we identify the layers where these improvements in brain alignment occur.

Original: In Section 5, we show that improved brain alignment in Section 4 is not due to improved language modeling (LM) ability, a possible confounding factor.
Revised: In Section 5, we show that improved brain alignment in Section 4 is not due to improved language modeling (LM) ability, a possible confounding factor.

Original: By disentangling LM ability’s contribution to brain alignment, we present evidence that BookSum-trained models develop deeper language understanding.
Revised: By disentangling LM ability’s contribution to brain alignment, we present evidence that BookSum-trained models develop deeper language understanding.

Original: In Section 6, we present a simple interpretability approach to study what brain-relevant information is gained by language models after training for deeper language understanding.
Revised: In Section 6, we present a simple interpretability approach to study what brain-relevant information is gained by language models after training for deeper language understanding.

Original: Our results reveal that these models are learning richer representations across all tested discourse features (Characters, Emotions, Motions).
Revised: Our results reveal that these models are learning richer representations across all tested discourse features (Characters, Emotions, Motions).

Original: Furthermore, they learn more about Characters than Emotions and Motions.
Revised: Furthermore, they learn more about Characters than Emotions and Motions.

Original: This indicates that discourse features are a promising dimension to study brain alignment and deep language understanding.
Revised: This indicates that discourse features are a promising dimension to study brain alignment and deep language understanding.

Original: Combining our contributions from Sections 4, 5, and 6, we present evidence that these NLP models are indeed developing deeper language understanding.
Revised: Combined, our contributions from Sections 4, 5, and 6 present evidence that these NLP models are indeed developing deeper language understanding.

Revised: The first reason is that improved alignment to human brains’ deep understanding of characters, emotions and motions suggests that the model has developed richer representations of these entities and concepts.

Revised: Second, we focus on brain regions suggested by previous research to underlie language comprehension in humans.

Revised: Hence, improved brain alignment is not spuriously related to non-language brain activities.

Revised: Third, we show that brain alignment does not improve when we only provide short input contexts (1 to 5 words) to the LMs.

Revised: It improves only for longer contexts (20 to 1000 words), which is more likely where deep contextual understanding is needed.

Original:  R ELATED WORK ON BRAINS AND LANGUAGE
Revised:  R ELATED WORK ON BRAINS AND LANGUAGE

Original: Our work relates to a growing body of research on disentangling the contributions of different types of information to the alignment between brain recordings and language models.
Revised: Our work relates to a growing body of research on disentangling the contributions of different types of information to the alignment between brain recordings and language models.

Original: Toneva et al.
Revised: Toneva et al.

Original: (2020) present an approach to disentangle supra-word meaning from lexical meaning in language models and show that the supra-word meaning is predictive of fMRI recordings in two language regions (anterior and posterior temporal lobes).
Revised: (2020) present an approach to disentangle supra-word meaning from lexical meaning in language models and show that the supra-word meaning is predictive of fMRI recordings in two language regions (anterior and posterior temporal lobes).

Original: Caucheteux et al.
Revised: Caucheteux et al.

Original: and Reddy & Wehbe (2021) aim to disentangle alignment due to syntactic and semantic processing.
Revised: and Reddy & Wehbe (2021) aim to disentangle alignment due to syntactic and semantic processing.

Original: Toneva et al.
Revised: Toneva et al.

Original: (2022) examine whether a representations obtained from a language model aligns with different language processing regions in a similar or different ways.
Revised: (2022) examine whether a representations obtained from a language model aligns with different language processing regions in a similar or different ways.

Original: Additionally, researchers have suggested that one contributor to the alignment is the language model’s ability to predict the next word, with a positive relationship between lower perplexity and higher brain alignment across NLP models (Schrimpf et al., 2021;
Revised: Additionally, researchers have suggested that one contributor to the alignment is the language model’s ability to predict the next word, with a positive relationship between lower perplexity and higher brain alignment across NLP models (Schrimpf et al., 2021;

Original: Goldstein et al., 2022).
Revised: Goldstein et al., 2022).

Original: On the other hand, more recent work shows that no simple relationship exists, and language modeling loss is not a good predictor of brain alignment (Pasquiou et al., 2022).
Revised: On the other hand, more recent work shows that no simple relationship exists, and language modeling loss is not a good predictor of brain alignment (Pasquiou et al., 2022).

Original: Our work contributes to this research area in two ways: (1) We disentangle the contributions of LM ability from deep language understanding towards brain-NLP alignment.
Revised: Our work contributes to this research area in two ways: (1) We disentangle the contributions of LM ability from deep language understanding towards brain-NLP alignment.

Original: (2) We show that deep language understanding is another factor that contributes to brain-NLP alignment.
Revised: (2) We show that deep language understanding is another factor that contributes to brain-NLP alignment.

Original: Some works have also investigated the alignment with brain recordings of fine-tuned language models.
Revised: Some works have also investigated the alignment with brain recordings of fine-tuned language models.

Original: Schwartz et al.
Revised: Schwartz et al.

Original: (2019) finetuned a pretrained BERT to predict fMRI and MEG recordings of people reading a chapter of a book.
Revised: (2019) finetuned a pretrained BERT to predict fMRI and MEG recordings of people reading a chapter of a book.

Original: The resulting fine-tuned model leads to improved prediction of previously unseen brain recordings, specifically in regions that are known to support language processing.
Revised: The resulting fine-tuned model leads to improved prediction of previously unseen brain recordings, specifically in regions that are known to support language processing.

Original: However, it is not clear what type of information has been induced in the fine-tuned
Revised: However, it is not clear what type of information has been induced in the fine-tuned

Original: BERT model that has contributed to the improved alignment with brain recordings.
Revised: BERT model that has contributed to the improved alignment with brain recordings.

Original: Oota et al.
Revised: Oota et al.

Original: (2022) investigate the alignment between fMRI recordings of people comprehending language and
Revised: (2022) investigate the alignment between fMRI recordings of people comprehending language and

Original: BERT that has been fine-tuned on a set of NLP tasks, such as co-reference resolution and questionanswering.
Revised: BERT that has been fine-tuned on a set of NLP tasks, such as co-reference resolution and questionanswering.

Original: The authors find that the best aligning task-tuned BERT varies according to whether the brain recordings correspond to reading or listening to the language stimuli.
Revised: The authors find that the best aligning task-tuned BERT varies according to whether the brain recordings correspond to reading or listening to the language stimuli.

Original: Our approach and research questions are complementary to these previous works.
Revised: Our approach and research questions are complementary to these previous works.

Original: Additionally, previous works which study the brain alignment of language models use models not specifically designed to integrate rich information across long contexts.
Revised: Additionally, previous works which study the brain alignment of language models use models not specifically designed to integrate rich information across long contexts.

Original: Therefore previous work observed that brain alignment worsened as sequence length was increased for many models (Toneva & Wehbe, 2019), or investigate relatively short contexts.
Revised: Therefore previous work observed that brain alignment worsened as sequence length was increased for many models (Toneva & Wehbe, 2019), or investigate relatively short contexts.

Original: On the other hand, our work uses models trained to integrate rich information across long contexts.
Revised: On the other hand, our work uses models trained to integrate rich information across long contexts.

Original: We show that brain alignment remains high even when supplying sequence lengths of up to 500 words, a finding that adds to prior works.
Revised: We show that brain alignment remains high even when supplying sequence lengths of up to 500 words, a finding that adds to prior works.

Original:  M ETHODS 3. B RAIN R EPRESENTATIONS
Revised:  M ETHODS 3. B RAIN R EPRESENTATIONS

Original: We use a publicly available brain dataset (Wehbe et al., 2014a) consisting of fMRI recordings ofparticipants reading chapter 9 of the book Harry Potter and the Sorceror’s Stone (Rowling et al., 1998).
Revised: We use a publicly available brain dataset (Wehbe et al., 2014a) consisting of fMRI recordings ofparticipants reading chapter 9 of the book Harry Potter and the Sorceror’s Stone (Rowling et al., 1998).

Original: The 5176 words in the chapter are presented to participants at a fixed interval of 0.5 seconds, one word at a time.
Revised: The 5176 words in the chapter are presented to participants at a fixed interval of 0.5 seconds, one word at a time.

Original: The chapter was divided into four runs of approximately equal length, and participants were allowed a short break at the end of each run.
Revised: The chapter was divided into four runs of approximately equal length, and participants were allowed a short break at the end of each run.

Original: We use this specific dataset for two main reasons: 1) the story is situated within a rich narrative world with characters, emotions and relationships, and 2) it is one of the largest publicly available datasets in terms of samples per participant which improves the reliability of the brain alignment estimate.
Revised: We use this specific dataset for two main reasons: 1) the story is situated within a rich narrative world with characters, emotions and relationships, and 2) it is one of the largest publicly available datasets in terms of samples per participant which improves the reliability of the brain alignment estimate.

Original: The fMRI recordings are sampled at a consistent interval, or repetition time (TR), of 2 seconds.
Revised: The fMRI recordings are sampled at a consistent interval, or repetition time (TR), of 2 seconds.

Original: At each TR, the activity level is recorded in each volume pixel (i.e. voxel) in the participant’s brain.
Revised: At each TR, the activity level is recorded in each volume pixel (i.e. voxel) in the participant’s brain.

Original: Hence, the brain representation for each participant is a matrix Y i ∈ R n × v i , where n is the number of TRs and v i is the number of voxels in the brain of participant i .
Revised: Hence, the brain representation for each participant is a matrix Y i ∈ R n × v i , where n is the number of TRs and v i is the number of voxels in the brain of participant i .

Original: The number of voxels differs for each human subject, ranging from 25 , 000 to 31 , 000 .
Revised: The number of voxels differs for each human subject, ranging from 25 , 000 to 31 , 000 .

Original: 3. NLP REPRESENTATIONS
Revised: 3. NLP REPRESENTATIONS

Original: We extract NLP representations from a total of 8 models.
Revised: We extract NLP representations from a total of 8 models.

Original: We refer to 4 of them as “base models”:
Revised: We refer to 4 of them as “base models”:

Original: BART-base, LED-base, BigBird-base and LongT5-base.
Revised: BART-base, LED-base, BigBird-base and LongT5-base.

Original: We refer to the remaining 4 as “booksum models”: BART-booksum, LED-booksum, BigBird-booksum and LongT5-booksum.
Revised: We refer to the remaining 4 as “booksum models”: BART-booksum, LED-booksum, BigBird-booksum and LongT5-booksum.

Original: Base models.
Revised: Base models.

Original: Our 4 “base models” were trained with a language modeling (LM) objective.
Revised: Our 4 “base models” were trained with a language modeling (LM) objective.

Original: We usethe corresponding pretrained models provided by Hugging Face (Wolf et al., 2020).
Revised: We use pretrained models from Hugging Face (Wolf et al., 2020).

Original: We providesome important details for each of the 4 pretrained models below and more details in Appendix A.
Revised: We provide more details in Appendix A.

Original: • BART (Lewis et al., 2019) uses a standard Transformer-based sequence-to-sequence architecture with 12 layers and 768-dimensional representations.
Revised: • BART (Lewis et al., 2019) uses a standard Transformer-based sequence-to-sequence architecture with 12 layers and 768-dimensional representations.

Original: It can process sequence lengths up to 1024 tokens.
Revised: It can process sequence lengths up totokens.

Original: BART was pretrained by corrupting documents and learning to reconstruct the original text, on a combination of books (narratives) and Wikipedia data.
Revised: BART was pretrained by corrupting documents and learning to reconstruct the original text, on a combination of books (narratives) and Wikipedia data.

Original: BigBird (Zaheer et al., 2020) is a Transformer-based model with 32 layers and 1024dimensional representations.
Revised: BigBird (Zaheer et al., 2020) is a Transformer-based model with 32 layers and 1024-dimensional representations.

Original: It uses a sparse-attention mechanism that allows it to process sequence lengths up to 4096 tokens.
Revised: It uses a sparse-attention mechanism that allows it to process sequence lengths up to 4096 tokens.

Original: We use the version of BigBird that was pretrained with Masked Language Modeling on books and summarization on the BigPatent dataset, which contains U S. patent documents alongside human-written summaries.
Revised: We use the version of BigBird that was pretrained with Masked Language Modeling on books and summarization of U.S. patent documents (BigPatent dataset).

Original: • Longformer Encoder Decoder (LED)
Revised: • Longformer Encoder Decoder (LED)

Original: (Beltagy et al., 2020) has an encoder-decoder Transformer architecture with 12 layers and 768-dimensional representations.
Revised: (Beltagy et al., 2020) has an encoder-decoder Transformer architecture with 12 layers and 768-dimensional representations.

Original: It uses an efficient local+global attention based on the Longformer, and can process up to 16384 tokens.
Revised: It uses an efficient local+global attention based on the Longformer, and can process up to 16384 tokens.

Original: LED was pretrained with the same procedure as BART above.
Revised: LED was pretrained with the same procedure as BART above.

Original: • LongT5 (Guo et al., 2021) is an encoder-decoder Transformer with 12 layers and 768dimensional representations.
Revised: • LongT5 (Guo et al., 2021) is an encoder-decoder Transformer with 12 layers and 768-dimensional representations.

Original: It can handle long input sequences up to 16384 tokens.
Revised: It can handle long input sequences up to 16384 tokens.

Original: We use the version of LongT5 with Transient Global Attention (TGlobal).
Revised: We use the version of LongT5 with Transient Global Attention (TGlobal).

Original: LongT5 was pretrained by masking and generating sentences on the C4 dataset
Revised: LongT5 was pretrained by masking and generating sentences on the C4 dataset, a collection of roughly 750 GB of English texts sourced from the public

Original: Booksum models.
Revised: Booksum models.

Original: Our 4 “booksum models” are first initialized from one of the ”base models”.
Revised: Our 4 “booksum models” are first initialized from one of the ”base models”.

Original: All of their parameters are then trained using the BookSum dataset (Kryscinski et al., 2021).
Revised: All of their parameters are then trained using the BookSum dataset (Kryscinski et al., 2021).

Original: The
Revised: The

Original: BookSum dataset is described in the paragraph below.
Revised: BookSum dataset is described in the paragraph below.

Original: We chose base and booksum models that would be useful for investigating our hypothesis.
Revised: We chose base and booksum models that would be useful for investigating our hypothesis.

Original: (1) Their architectures are designed to take advantage of long contexts, which is useful for deep understanding.
Revised: (1) Their architectures are designed to take advantage of long contexts, which is useful for deep understanding.

Original: The booksum models have good performance on BookSum, and the base models have good performance on other long-context benchmarks.
Revised: The booksum models have good performance on BookSum, and the base models have good performance on other long-context benchmarks.

Original: (3) The base models, except LongT5, were pretrained with language modeling on large narrative datasets, which ensures that base and booksum models were trained on datasets from similar domains, to allow fair comparisons.
Revised: (3) The base models, except LongT5, were pretrained with language modeling on large narrative datasets, which ensures that base and booksum models were trained on datasets from similar domains, to allow fair comparisons.

Original: (4) Our models are sufficiently varied, and we studypairs of models in total, which improves reliability of our results.
Revised: (4) Our models are sufficiently varied, and we studypairs of models in total, which improves reliability of our results.

Original: BookSum dataset for long-range summarization.
Revised: BookSum dataset for long-range summarization.

Original: The BookSum dataset (Kryscinski et al., 2021) is a challenging dataset for summarizing long narratives.
Revised: The BookSum dataset (Kryscinski et al., 2021) is a challenging dataset for summarizing long narratives.

Original: For training, the inputs are chapters from books and the outputs are human-written summaries.
Revised: For training, the inputs are chapters from books and the outputs are human-written summaries.

Original: The input documents are relatively long, with an average of more than 5000 words, and contain long-range causal and temporal dependencies.
Revised: The input documents are relatively long, with an average of more than 5000 words, and contain long-range causal and temporal dependencies.

Original: The ground-truth output summaries have an average of roughly 500 words.
Revised: The ground-truth output summaries have an average of roughly 500 words.

Original: To generate these summaries, NLP models must capture the most critical information by understanding complex interactions across long-range contexts.
Revised: To generate these summaries, NLP models must capture the most critical information by understanding complex interactions across long-range contexts.

Original: Some examples from the BookSum dataset are provided in
Revised: Some examples from the BookSum dataset are provided in

Original: Appendix B. Furthermore, the dataset belongs to the narrative domain, which captures rich discourse structures for characters, emotions and relationships, which is also the case for the Harry
Revised: Appendix B. Furthermore, the dataset belongs to the narrative domain, which captures rich discourse structures for characters, emotions and relationships, which is also the case for the Harry

Original: Potter stimulus dataset that we use to evaluate brain alignment.
Revised: Potter stimulus dataset that we use to evaluate brain alignment.

Original: The BookSum Dataset also does not include any books from the Harry Potter series, thus preventing train-test overlap.
Revised: The BookSum Dataset also does not include any books from the Harry Potter series, thus preventing train-test overlap.

Original: Extracting NLP representations.
Revised: Extracting NLP representations.

Original: Our goal is to extract NLP representations for each of thewords in our Harry Potter fMRI dataset.
Revised: Our goal is to extract NLP representations for each of thewords in our Harry Potter fMRI dataset.

Original: For each word, we feed the word and its preceding context to a model, and extract its outputs at every layer for the word.
Revised: For each word, we feed the word and its preceding context to a model, and extract its outputs at every layer for the word.

Original: We extract NLP representations for all 8 NLP models, all layers for each model, and vary the input sequence lengths between 20 towords.
Revised: We extract NLP representations for all 8 NLP models, all layers for each model, and vary the input sequence lengths between 20 towords.

Original: Each unique combination of (model, layer number, input sequence length) will produce a different set of NLP representations, X ml,s ∈ R 5176 × d , where d is the embedding size for model m , layer l , sequence length s .
Revised: Each unique combination of (model, layer number, input sequence length) will produce a different set of NLP representations, X ml,s ∈ R 5176 × d , where d is the embedding size for model m , layer l , sequence length s .

Original: 3. A LIGNING B RAIN AND NLP REPRESENTATIONS .
Revised: 3. A LIGNING B RAIN AND NLP REPRESENTATIONS .

Original: We follow a general alignment approach that has been previously used in several works (Jain &
Revised: We follow a general alignment approach that has been previously used in several works (Jain &

Original: Huth, 2018; Toneva & Wehbe, 2019; Schrimpf et al., 2021).
Revised: Huth, 2018; Toneva & Wehbe, 2019; Schrimpf et al., 2021).

Original: This approach learns a function to predict the fMRI recordings at every voxel of each participant i using the NLP representations that correspond to the same text read by the participant.
Revised: This approach learns a function to predict the fMRI recordings at every voxel of each participant i using the NLP representations that correspond to the same text read by the participant.

Original: Similarly to previous work, we parameterize this function as a linear function, regularized using the ridge penalty.
Revised: Similarly to previous work, we parameterize this function as a linear function, regularized using the ridge penalty.

Original: We train the function in a cross-validated way and test its performance on the data that was heldout during training.
Revised: We train the function in a cross-validated way and test its performance on the data that was heldout during training.

Original: We select the ridge parameter via nested cross-validation.
Revised: We select the ridge parameter via nested cross-validation.

Original: Since the fMRI data was collected in 4 runs of approximately equal length, we use 4 -fold cross validation where each fold corresponds to holding out one run of fMRI data for testing.
Revised: Since the fMRI data was collected in 4 runs of approximately equal length, we use 4 -fold cross validation where each fold corresponds to holding out one run of fMRI data for testing.

Original: We also remove 10 TRs of fMRI data and the corresponding
Revised: We also remove 10 TRs of fMRI data and the corresponding

Original: NLP representations from the ends of each test run to avoid possible train-test overlap.
Revised: NLP representations from the ends of each test run to avoid possible train-test overlap.

Original: Other details about the alignment approach are provided in Appendix C.
Revised: Other details about the alignment approach are provided in Appendix C.

Original: Evaluate brain alignment.
Revised: Evaluate brain alignment.

Original: We evaluatethe brain alignment using two evaluation metrics, computed between the predictions of heldout fMRI recordings ˆ Y i ∈ R n × v i and the true corresponding fMRI data Y i ∈ R n × v i .
Revised: We evaluate brain alignment using two evaluation metrics, computed between the predictions of heldout fMRI recordings ˆ Y i ∈ R n × v i and the true corresponding fMRI data Y i ∈ R n × v i .

Original: Both evaluation metrics are used by prior works and each has its own advantages.
Revised: Both evaluation metrics are used by prior works and each has its own advantages.

Original: • 20v20 classification accuracy.
Revised: • 20v20 classification accuracy.

Original: This metric evaluates the fMRI predictions by using them in a classification task on held-out data in the cross-validation setting.
Revised: This metric evaluates the fMRI predictions by using them in a classification task on held-out data in the cross-validation setting.

Original: The classification task is to predict which of two sets of words was being read, based on the corresponding NLP representations of these words (Mitchell et al., 2008; Wehbe et al., 2014b).
Revised: The classification task is to predict which of two sets of words was being read, based on the corresponding NLP representations of these words (Mitchell et al., 2008; Wehbe et al., 2014b).

Original: The sets consist of 20 consecutive TRs and the classification is 
Revised: The sets consist of 20 consecutive TRs and the classification is repeated

Original: repeated 1000 times within each fold.
Revised: times within each fold.

Original: The final 20v20 accuracy is the average across the folds.
Revised: The final 20v20 accuracy is the average across the folds.

Original: This metric was proposed as a way to boost the signal-to-noise ratio in estimating the brain alignment for single-trial data.
Revised: This metric was proposed as a way to boost the signal-to-noise ratio in estimating the brain alignment for single-trial data.

Original: The Harry Potter data is entirely single-trial (i.e. the participants read the book chapter only once and so each word appears only once in its context) and increasing the number of TRs that are classified together up to 20 improves the classification accuracy for this dataset (Wehbe et al., 2014a;b).
Revised: The Harry Potter data is entirely singletrial (i.e. the participants read the book chapter only once and so each word appears only once in its context) and increasing the number of TRs that are classified together up to 20 improves the classification accuracy for this dataset (Wehbe et al., 2014a;b).

Original: • Pearson correlation.
Revised: • Pearson correlation

Original: This metric evaluates the similarity between the fMRI predictions and the corresponding true fMRI data by computing the Pearson correlation between ˆ y i,j and y i,j for participant i and each voxel j ∈ { 1 , . . .
Revised: This metric evaluates the similarity between the fMRI predictions and the corresponding true fMRI data by computing the Pearson correlation between ˆ y i,j and y i,j for participant i and each voxel j ∈ { 1 , . . .

Original: v i } .
Revised: v i } .

Original: This produces a vector ∈ R v i .
Revised: This produces a vector ∈ R v i .

Original: This metric can be computed efficiently and is widely used in cognitive neuroscience (Huth et al., 2016; Jain & Huth, 2018;
Revised: This metric can be computed efficiently and is widely used in cognitive neuroscience (Huth et al., 2016; Jain & Huth, 2018;

Original: Caucheteux et al., 2021; Schrimpf et al., 2021; Goldstein et al., 2022). 
Revised: Caucheteux et al., 2021; Schrimpf et al., 2021; Goldstein et al., 2022). significantly

Revised: G long range and deep understanding is an important contributor to brain alignment

Original:  T RAINING NLP MODELS FOR D EEP N ARRATIVE U NDERSTANDING
Revised:  T RAINING NLP MODELS FOR D EEP N ARRATIVE U NDERSTANDING

Original: Do NLP models trained for deeper understanding of long narratives have improved brain alignment over those trained for language modeling (LM) with shallow understanding?
Revised: Do NLP models trained for deeper understanding of long narratives have improved brain alignment over those trained for language modeling (LM)?

Original: To investigate this question, we evaluate the 20v20 brain alignment scores of 4 booksum models (deeper understanding) against 4 base models, as described in Section 3.
Revised: To investigate this, we evaluate the 20v20 brain alignment scores of 4 booksum models (deeper understanding) against 4 base models (LM), as described in Section 3.

Original: We use all 8 NLP models, layers 1 to 12, sequence lengths 20 to 1000, and all 8 human subjects.
Revised: We use all 8 NLP models, layers 1 to 12, sequence lengths 1 to 1000, and all 8 human subjects.

Original: The results for one of the 4 models for a representative set of layers are presented in Figure 1.
Revised: In Figure 1, we present average results for all 4 pairs of models, as well as results for a representative set of layers for one of the models.

Original: The resultsfor the remaining models arepresented in Appendix F.
Revised: Other results are in Appendix F and G.

Original: Booksum models are significantly better aligned to brain representations.
Revised: Booksum models are significantly better aligned to brain representations.

Original: We suggest that this is due to deeper understanding of text, because of two key facts.
Revised: We suggest that this is due to deeper understanding of text, because of two key facts.

Original: The booksum models were trained on the BookSum dataset, which is designed with challenging tasks to learn deeper narrative understanding.
Revised: The booksum models were trained on the BookSum dataset, which is designed with challenging tasks to learn deeper narrative understanding.

Original: It requires understanding complex interactions across long-range contexts and generating summaries that capture the most critical information.
Revised: It requires understanding complex interactions across long-range contexts and generating summaries that capture the most critical information.

Original: Furthermore, these models have architectures that were designed to integrate information across long context lengths.
Revised: Furthermore, these models have architectures that were designed to integrate information across long context lengths.

Original: This allowed them to develop deeper understanding when trained on BookSum.
Revised: This allowed them to develop deeper understanding when trained on BookSum.

Original: Could the improved brain alignment of booksum models be due to other confounding variables and not deeper language understanding?
Revised: Could the improved brain alignment of booksum models be due to other confounding variables and not deeper language understanding?

Original: In this paragraph, we describe how we tried to eliminate possible confounding variables.
Revised: In this paragraph, we describe how we tried to eliminate possible confounding variables.

Original: (1) Both base and booksum models were trained with similar data: large narrative datasets.
Revised: (1) Both base and booksum models were trained with similar data: large narrative datasets.

Original: Hence, improved brain alignment is not simply because booksum models were trained on data from a more similar domain to the brain alignment dataset (Harry Potter).
Revised: Hence, improved brain alignment is not simply because booksum models were trained on data from a more similar domain to the brain alignment dataset (Harry Potter).

Revised: We also verified this by comparing booksum models against base models trained on BookSum with a language modeling objective in Appendix L. (2)

Original: (2) The booksum models and their corresponding base versions support identical sequence lengths, and were also trained with similar input lengths: all the base models were trained with long input sequences between 1024 and 4096 tokens.
Revised: The booksum models and their corresponding base versions support identical sequence lengths, and were also trained with similar input lengths: all the base models were trained with long input sequences between 1024 and 4096 tokens.

Original: Hence, improved brain alignment is not because booksum models were trained on longer input lengths.
Revised: Hence, improved brain alignment is not because booksum models were trained on longer input lengths.

Original: In Section 5, we show that training NLP models for deep understanding improves brain alignment, but does not improve LM ability.
Revised: In Section 5, we show that training NLP models for deep understanding improves brain alignment, but does not improve LM ability.

Original: Hence, improved brain alignment is not because booksum models developed better language modeling ability through 
Revised: Hence, improved brain alignment is not because booksum models developed better language modeling ability through BookSum-training.

Original: BookSum-training.
Revised: In Appendix K, we show that the brain alignment of BART trained to summarize CNN news articles (BART-cnn) is not substantially higher than BART-base. Hence, the improved brain alignment is not simply due to the summarization training objective.

Original: Therefore, we argue that improved brain alignment for booksum models is likely not due to these confounding variables.
Revised: Therefore, we argue that improved brain alignment for booksum models is likely not due to these confounding variables.

Original: Brain alignment across models peaks around context length of 500 words.
Revised: Brain alignment across models peaks around context length of 500 words.

Revised: We further studied how brain alignment is affected by input context length to the models, which we varied from 1 to 1000 words.

Revised: If brain alignment continues to increase as we increase context length, it would suggest that these models are indeed able to maintain information across long contexts.

Original: Prior work that used earlier language models such as BERT and Transformer-XL found that brain alignment peaked around 50 words (Toneva & Wehbe, 2019).
Revised: Prior work that used earlier language models such as BERT and Transformer-XL found that brain alignment peaked around 50 words (Toneva & Wehbe, 2019).

Original: We find a 10-fold increase in the sequence length that results in the peak of brain alignment, across both the base and booksum models.
Revised: We find a 10-fold increase in the sequence length that results in the peak of brain alignment, across both the base and booksum models.

Original: Because this peak is consistent for both types of models, we attribute the 10-fold increase to the fact that the models that we evaluate in this work are specifically designed to integrate longer context than previous models.
Revised: Because this peak is consistent for both types of models, we attribute the 10-fold increase to the fact that the models that we evaluate in this work are specifically designed to integrate longer context than previous models.

Original: However, we do not observe an additional benefit beyond 500 words of context, even though the models that we analyze are supposed to incorporate more than 500 words of context (up totokens for LED and LongT5). 
Revised: However, we do not observe an additional benefit beyond 500 words of context, even though the models that we analyze are supposed to incorporate more than 500 words of context (up totokens for LED and LongT5). Brain alignment improves only for longer input contexts (20 to 1000 words), but not short contexts (1 to 5 words).

Revised: Importantly, this suggests: (1) improved brain alignment is likely due to deeper language understanding (which benefits from longer contexts), and (2) these models trained for deeper understanding have developed better ability to make use of long contextual information.

Original: Booksum finetuning improves brain alignment for different layers across models.
Revised: Booksum finetuning improves brain alignment for different layers across models.

Original: Finetuning has been shown to result in greater changes in deeper layers than earlier layers of models (Mosbach et al., 2020).
Revised: Finetuning has been shown to result in greater changes in deeper layers than earlier layers of models (Mosbach et al., 2020).

Original: Therefore one might expect that changes in brain alignment that are due to finetuning may emerge primarily in the deeper layers.
Revised: Therefore one might expect that changes in brain alignment that are due to finetuning may emerge primarily in the deeper layers.

Original: However, we find that the brain alignment is significantly improved at different layer depths across the 4 models that we test.
Revised: However, we find that the brain alignment is significantly improved at different layer depths across the 4 models that we test.

Original: BigBird and LED improve their alignment with the brain significantly in early to mid layers (layers 6-14 out of 32 in BigBird, and layers 2-8 out of 12 in LED), whereas LongT5 shows significant increases only in the deep layers (layers 8-12 out of 12).
Revised: BigBird and LED improve their alignment with the brain significantly in early to mid layers (layers 6-14 out of 32 in BigBird, and layers 2-8 out of 12 in LED), whereas LongT5 shows significant increases only in the deep layers (layers 8-12 out of 12).

Original: BART shows a consistent significant difference throughout most of its layers (layers 2-11 out of 12).
Revised: BART shows a consistent significant difference throughout most of its layers (layers 2-11 out of 12).

Original: These results suggest that the brain-relevant changes may be distributed differently across different models.
Revised: These results suggest that the brain-relevant changes may be distributed differently across different models.

Original:  R ELATIONSHIP BETWEEN B RAIN ALIGNMENT , L ANGUAGE MODELING AND D EEP UNDERSTANDING
Revised:  R ELATIONSHIP BETWEEN B RAIN ALIGNMENT , L ANGUAGE MODELING AND D EEP UNDERSTANDING

Original: Why does training language models on BookSum lead to improved brain alignment, as our results show in Section 4?
Revised: Why does training language models on BookSum lead to improved brain alignment, as our results show in Section 4?

Original: Is it due to the models developing deeper language understanding, or are there other confounding factors?
Revised: Is it due to the models developing deeper language understanding, or are there other confounding factors?

Original: One possible confounding factor is language modeling (LM) ability, as some works suggest that LM ability contributes to brain-NLP alignment (Schrimpf et al., 2021;
Revised: One possible confounding factor is language modeling (LM) ability, as some works suggest that LM ability contributes to brain-NLP alignment (Schrimpf et al., 2021;

Original: Goldstein et al., 2022).
Revised: Goldstein et al., 2022).

Original: This raises the possibility that booksum models have greater brain alignment simply because they developed better language modeling ability through BookSum-training.
Revised: This raises the possibility that booksum models have greater brain alignment simply because they developed better language modeling ability through BookSum-training.

Original: We conduct experiments to further investigate this possibility.
Revised: We conduct experiments to further investigate this possibility.

Original: Our goal is to show that the improved brain alignment is related to deeper understanding, rather than improved LM ability.
Revised: Our goal is to show that the improved brain alignment is related to deeper understanding, rather than improved LM ability.

Original: We show this by demonstrating that training LM models for deep understanding (booksum models) improves brain alignment, but simultaneously does not improve LM ability for 3 of the 4 models.
Revised: We show this by demonstrating that training LM models for deep understanding (booksum models) improves brain alignment, but simultaneously does not improve LM ability for 3 of the 4 models.

Original: We evaluate the LM ability of all 8 NLP models (4 base, 4 booksum) on the text corresponding to the same Harry Potter chapter which was used to obtain the brain recordings.
Revised: We evaluate the LM ability of all 8 NLP models (4 base, 4 booksum) on the text corresponding to the same Harry Potter chapter which was used to obtain the brain recordings.

Original: First, we split our
Revised: First, we split our

Original: Harry Potter text dataset into a train and test set (75% and 25% of the text respectively).
Revised: Harry Potter text dataset into a train and test set (75% and 25% of the text respectively).

Original: Next, for each model, we freeze all layers, add a LM head, and train only the head to predict masked words in the train set.
Revised: Next, for each model, we freeze all layers, add a LM head, and train only the head to predict masked words in the train set.

Original: Finally, we evaluate each model’s cross-entropy loss (LM ability) on the heldout test set.
Revised: Finally, we evaluate each model’s cross-entropy loss (LM ability) on the heldout test set.

Original: We froze all model weights (other than LM head) to ensure that the representations are preserved, identical to the ones used to evaluate brain alignment.
Revised: We froze all model weights (other than LM head) to ensure that the representations are preserved, identical to the ones used to evaluate brain alignment.

Original: We used the Harry Potter text dataset to ensure that the same dataset is used for measuring both brain alignment and LM ability, to allow for a consistent comparison.
Revised: We used the Harry Potter text dataset to ensure that the same dataset is used for measuring both brain alignment and LM ability, to allow for a consistent comparison.

Original: We perform 4-fold cross validation to improve reliability, where each fold corresponds to consecutive 25% of the text.
Revised: We perform 4-fold cross validation to improve reliability, where each fold corresponds to consecutive 25% of the text.

Original: The results are presented in Figure 2.
Revised: The results are presented in Figure 2.

Original: Improved brain alignment is not due to better language modeling (LM) ability.
Revised: Improved brain alignment is not due to better language modeling (LM) ability.

Original: For BigBird and
Revised: For BigBird and

Original: LED, the booksum model achieves significantly higher brain alignment but significantly worse LM performance than the base model.
Revised: LED, the booksum model achieves significantly higher brain alignment but significantly worse LM performance than the base model.

Original: For BART, the booksum model achieves greater brain alignment but does not significantly differ in LM performance.
Revised: For BART, the booksum model achieves greater brain alignment but does not significantly differ in LM performance.

Original: For LongT5, the booksum model has both higher brain and LM performance.
Revised: For LongT5, the booksum model has both higher brain and LM performance.

Original: Hence, while all 4 booksum models have significantly better brain alignment, the majority of them do not have a better LM performance.
Revised: Hence, while all 4 booksum models have significantly better brain alignment, the majority of them do not have a better LM performance.

Original: This shows that the improved brain alignment for at least 3 of the 4 models is due to deeper understanding and not their ability to predict the next word.
Revised: This shows that the improved brain alignment for at least 3 of the 4 models is due to deeper understanding and not their ability to predict the next word.

Original: How does brain alignment differ across brain regions?
Revised: How does brain alignment differ across brain regions?

Original: We specifically investigate alignment within the brain regions in the language network, which are thought to underlie language comprehension
Revised: We specifically investigate alignment within the brain regions in the language network, which are thought to underlie language comprehension

Original: Fedorenko et al.
Revised: Fedorenko et al.

Original: ; Fedorenko & Thompson-Schill (2014).
Revised: ; Fedorenko & Thompson-Schill (2014).

Original: For each region in the brain language network, we compare the average brain alignment for booksum versus base models.
Revised: For each region in the brain language network, we compare the average brain alignment for booksum versus base models.

Original: To compute these averages per model, we use all layers, sequence lengths, and participants.
Revised: To compute these averages per model, we use all layers, sequence lengths, and participants.

Original: The results for BigBird are presented in Figure 2, and the results for the remaining models areincluded in Appendix G.
Revised: The results for BigBird are presented in Figure 2, and the results for the remaining models are in Appendix H.

Original: Across all brain language ROIs, brain alignment is significantly greater for booksum models.
Revised: Across all brain language ROIs, brain alignment is significantly greater for booksum models.

Original: This suggests that the brain-relevant information imparted by the training towards deep understanding is not localized to any one particular region in the brain, and is instead distributed across language regions.
Revised: This suggests that the brain-relevant information imparted by the training towards deep understanding is not localized to any one particular region in the brain, and is instead distributed across language regions.

Original: Furthermore, this shows that models that have significantly better language modeling performance (base ≫ booksum for BigBird and LED) do not achieve better alignment in any language region.
Revised: Furthermore, this shows that models that have significantly better language modeling performance (base ≫ booksum for BigBird and LED) do not achieve better alignment in any language region.

Original: This further supports the need to complement language modeling with deep understanding for NLP training.
Revised: This further supports the need to complement language modeling with deep understanding for NLP training.

Original:  I NTERPRETING BRAIN ALIGNMENT ACROSS DISCOURSE FEATURES
Revised:  I NTERPRETING BRAIN ALIGNMENT ACROSS DISCOURSE FEATURES

Original: What are NLP models learning when trained for deeper understanding?
Revised: What are NLP models learning when trained for deeper understanding?

Original: Are they learning a deeper understanding of the important narrative elements, such as Characters, Emotions, and Motions in the text?
Revised: Are they learning a deeper understanding of the important discourse features in the text, such as Characters, Emotions, and Motions?

Original: In this section, we present a simple interpretability approach to dive deeper into studying the brain alignment for various discourse features (Characters, Emotions, Motions). We apply this method to understand how the representations  of NLP models are changing when they are trained for deeper understanding.
Revised: These discourse features have representations that go beyond the meaning of their individual words. Characters develop over the course of the entire book through their thoughts, actions, and

Original: 1: Input: fMRI predictions, fMRI activity 2: for all discourse features do 3: label words with discourse feature 4: map labeled words to fMRI TRs 5: extract NLP predictions for discourse TRs 6: extract brain activity for discourse TRs 7: compute Pearson corr.
Revised: 1: Input: fMRI predictions, fMRI activity 2: for all discourse features do 3: label words with discourse feature 4: map labeled words to fMRI TRs 5: extract NLP predictions for discourse TRs 6: extract brain activity for discourse TRs 7: compute Pearson corr.

Original: for discourse TRs 8:
Revised: for discourse TRs 8:

Original: Result: Pearson corr.
Revised: Result: Pearson corr.

Original: for discourse feature 9: end for 10: Result: Pearson corr.
Revised: for discourse feature 9: end for 10: Result: Pearson corr.

Original: for all discourse features 
Revised: for all discourse features interactions with other characters.

Original: When training NLP models for deep understanding, brain alignment significantly improves for all discourse features.
Revised: Emotions depend on who is experiencing them, why, and what situation they are in.

Original: However, it improves more for Characters than other discourse features. Note that the correlations reported in this figure are low inmagnitude because they are an average that includes a large number of brain voxels that may not be significantly involved in brain-NLP alignment or language processing.
Revised: Motions (actions) are situated in a scene, contextualized by the actor, target, rationale of the action, and other people and objects in the environment. Hence, richer representations of these discourse features suggest a deeper linguistic understanding of the text.

Original: Interpretability approach to compare brain alignment acrossvarious discourse features.
Revised: Our simple interpretability approach to compare brain alignment across discourse features.

Original: For each discourse feature, we compute an average Pearson correlationscore for booksum models, and anothercorrelation for base models.
Revised: For each discourse feature, we compute an average Pearson correlation for booksum models, and another for base models.

Original: This allows us to compare the brain alignment between base and booksum models, separately for each discourse feature.

Original: Different discourse features may be present at different frequency in the text.
Revised: Different discourse features may be present at different frequency in the text.

Original: Hence, to enable fair comparisons, we use the same number of samples to compute the correlation for each discourse feature.
Revised: Hence, to enable fair comparisons, we use the same number of samples to compute the correlation for each discourse feature.

Original: We present the results and the approach’s pseudocode in Figure 3.
Revised: We present the results and the approach’s pseudocode in Figure 3.

Original: To compute these averages, we use all 8 models, layers 1 to 12, sequence lengths from 20 to 1000, and all 8 human subjects.
Revised: To compute these averages, we use all 8 models, layers 1 to 12, sequence lengths from 20 to 1000, and all 8 human subjects.

Original: We elaborate the details in Appendix D.
Revised: We provide more details in Appendix D.

Original: When training NLP models for deep understanding, brain alignment improves significantly for all discourse features.

Original: This suggests that deep narrative understanding results in richer representations for all discourse features.

Original: NLP models align more to the brain for Characters than other discourse features, and also improve the most for Characters when training for deeper understanding.
Revised: NLP models align more to the brain for Characters than other discourse features, and also improve the most for Characters when training for deeper understanding.

Original: For Characters,
Revised: For Characters,

Original: Pearson correlation averaged across all voxels improves by 0.011, which is higher than for other discourse features.
Revised: Pearson correlation averaged across all voxels improves more than for other discourse features for most models (Appendix I, significance level 0.05).

Original: Note that the correlations reported in Figure 3 are low in magnitude because they are an average that includes a large number of brain voxels that may not be significantly involved in brain-NLP alignment or language comprehension.
Revised: Note that the correlations reported in Figureare low in magnitude because they are an average that includes a large number of brain voxels that may not be significantly involved in brain-NLP alignment or language comprehension.

Original: Next, we compare the brain plots in rows D and E of Figure 4.
Revised: Next, we compare the brain plots in rows D and E of Figure 4.

Original: Character TRs improve in more voxels (orange regions) than the random TR sample.
Revised: Character TRs improve in more voxels (orange regions) than the random TR sample.

Original: It is also surprising that booksum-training actually results in significantly worse alignment for some brain voxels (blue regions).
Revised: It is also surprising that booksum-training actually results in significantly worse alignment for some brain voxels (blue regions).

Original: One possible reason is that these specific regions may participate in prediction of future words, and as we saw in Section 5, the language modeling performance of BigBird has declined as a consequence of the BookSum-training.
Revised: One possible reason is that these specific regions may participate in prediction of future words, and as we saw in Section 5, the language modeling performance of BigBird has declined as a consequence of the BookSum-training.

Original: The results for significance tests can be found in Appendix H.
Revised: The results for significance tests can be found in Appendix I.

Original: Discourse features are a promising dimension to study brain alignment.
Revised: Discourse features are a promising dimension to study brain alignment.

Revised: Training NLP models for deeper understanding resulted in improved brain alignment (richer representations) for all discourse features.

Original: Our work contributes towards understanding what language models are learning when trained for deeper understanding.
Revised: Our work contributes towards understanding what language models are learning when trained for deeper understanding.

Original: We show that brain alignment is higher for Characters than other discourse features.
Revised: We show that brain alignment is higher for Characters than other discourse features.

Original: We hypothesize that, for Characters, language models integrate richer information from surrounding context, producing representations with greater alignment to brain representations.
Revised: We hypothesize that, for Characters, language models integrate richer information from surrounding context, producing representations with greater alignment to brain representations.

Original: On the other hand, NLP representations for other discourse features may be more shallow and less context-dependent.
Revised: On the other hand, NLP representations for other discourse features may be more shallow and less context-dependent.

Original: In the future, we hope to dive deep into investigating this hypothesis, which will ultimately generate insights for a better understanding into NLP representations.
Revised: In the future, we hope to dive deep into investigating this hypothesis, which will ultimately generate insights for a better understanding into NLP representations.

Original: Furthermore, future work can explore additional discourse features.
Revised: Furthermore, future work can explore additional discourse features.

Original:  C ONCLUSIONS AND F UTURE W ORK
Revised:  C ONCLUSIONS AND F UTURE W ORK

Original: Conclusions for brain-NLP interdisciplinary field.
Revised: Conclusions for brain-NLP interdisciplinary field.

Original: How and why do brain and NLP representations align?
Revised: How and why do brain and NLP representations align?

Original: This work contributes the following findings towards this question.
Revised: This work contributes the following findings towards this question.

Original: (1) Understanding of characters and other discourse features is a significant factor in brain-NLP alignment.
Revised: (1) Understanding of characters and other discourse features is a significant factor in brain-NLP alignment.

Original: (2) Deeper understanding of a text contributes to brain-NLP alignment independently from language modeling (LM) ability.
Revised: (2) Deeper understanding of a text contributes to brain-NLP alignment independently from language modeling (LM) ability.

Original: Overall, our work helps to fill in a knowledge gap in the brain-NLP field.
Revised: Overall, our work helps to fill in a knowledge gap in the brain-NLP field.

Original: Conclusions for NLP.
Revised: Conclusions for NLP.

Original: Language modeling (LM) has achieved great success and popularity.
Revised: Language modeling (LM) has achieved great success and popularity.

Original: However, is LM sufficient to develop NLP models with language understanding?
Revised: However, is LM sufficient to develop NLP models with language understanding?

Original: We present a unique approach to answer this question by drawing insights from human brains.
Revised: We present a unique approach to answer this question by drawing insights from human brains.

Original: Our results reveal that: (1)
Revised: Our results reveal that: (1)

Original: LM models achieve poorer representations for Characters and other discourse features, compared to deeper understanding models.
Revised: LM models achieve poorer representations for Characters and other discourse features, compared to deeper understanding models.

Original: Existing training methods for narrative understanding are a first step towards developing language models with deep language understanding.
Revised: Existing training methods for narrative understanding are a first step towards developing language models with deep language understanding.

Original: By using results from brain-NLP alignment, we argue for the need to train NLP models with deeper understanding of long-range context, going beyond language modeling.
Revised: By using results from brain-NLP alignment, we argue for the need to train NLP models with deeper understanding of long-range context, going beyond language modeling.

Original: Future work.
Revised: Future work.

Original: There are multiple exciting avenues to build on our work.
Revised: There are multiple exciting avenues to build on our work.

Original: (1) We showed that brain alignment differs across discourse features.
Revised: (1) We showed that brain alignment differs across discourse features.

Original: Future work can investigate why these differences exist.
Revised: Future work can investigate why these differences exist.

Original: (2) We showed that deeper understanding in NLP models improves brain alignment.
Revised: (2) We showed that deeper understanding in NLP models improves brain alignment.

Original: Future work can explore the mechanisms behind deeper understanding of texts in NLP models.
Revised: Future work can explore the mechanisms behind deeper understanding of texts in NLP models.

Original:  R EPRODUCIBILITY S TATEMENT
Revised:  R EPRODUCIBILITY S TATEMENT

Original: Code available at: https://github.com/brain-nlp/brain_language_deeper .
Revised: Code available at: https://github.com/brain-nlp/brain_language_deeper .

Original: In this anonymized repository, we provide the following: (1) Code used to reproduce the results in our paper.
Revised: In this anonymized repository, we provide the following: (1) Code used to reproduce the results in our paper.

Original: (2) README explaining how to install packages, preprocess data, run experiments, and plot visualizations of results.
Revised: (2) README explaining how to install packages, preprocess data, run experiments, and plot visualizations of results.

Original: In our paper, we run a large number of experiments (many models, layers, sequence lengths, subjects, discourse features, brain ROIs, etc).
Revised: In our paper, we run a large number of experiments (many models, layers, sequence lengths, subjects, discourse features, brain ROIs, etc).

Original: Hence, we provide scripts to automate the process of running experiments across the various models, layers, etc.
Revised: Hence, we provide scripts to automate the process of running experiments across the various models, layers, etc.

Original: We hope this will make it easy for others to use our code efficiently.
Revised: We hope this will make it easy for others to use our code efficiently.

Original: (4) After running experiments to obtain data, you can also use our code for plotting the figures in our paper, as well as additional graphs to visualize results.
Revised: (4) After running experiments to obtain data, you can also use our code for plotting the figures in our paper, as well as additional graphs to visualize results.

Original: We hope this will allow others to use our code for future projects
Revised: We hope this will allow others to use our code for future projects

Original: A NLP MODELS USED IN OUR WORK
Revised: A NLP MODELS USED IN OUR WORK

Original: For all 4 base models and 4 booksum models, we use pretrained models provided by Hugging Face (Wolf et al., 2020).
Revised: For all 4 base models and 4 booksum models, we use pretrained models provided by Hugging Face (Wolf et al., 2020).

Original: • BART (Lewis et al., 2019) – Base model: https://huggingface.co/facebook/bart-base – Booksum model: https://huggingface.co/KamilAin/bart-base-booksum • LED (Beltagy et al., 2020) – Base model: https://huggingface.co/allenai/led-base- – Booksum model: https://huggingface.co/pszemraj/led-base-book-summary • BigBird (Zaheer et al., 2020) – Base model: https://huggingface.co/google/bigbird-pegasus-large-bigpatent – Booksum model: https://huggingface.co/pszemraj/bigbird-pegasus-large-K-booksum • LongT5 (Guo et al., 2021) – Base model: https://huggingface.co/google/long-t5-tglobal-base – Booksum model: https://huggingface.co/pszemraj/long-t5-tglobal-base-16384-book- summary
Revised: • BART (Lewis et al., 2019) – Base model: https://huggingface.co/facebook/bart-base – Booksum model: https://huggingface.co/KamilAin/bart-base-booksum • LED (Beltagy et al., 2020) – Base model: https://huggingface.co/allenai/led-base- – Booksum model: https://huggingface.co/pszemraj/led-base-book-summary • BigBird (Zaheer et al., 2020) – Base model: https://huggingface.co/google/bigbird-pegasus-large-bigpatent – Booksum model: https://huggingface.co/pszemraj/bigbird-pegasus-large-K-booksum • LongT5 (Guo et al., 2021) – Base model: https://huggingface.co/google/long-t5-tglobal-base – Booksum model: https://huggingface.co/pszemraj/long-t5-tglobal-base-16384-book-summary

Original: B E XAMPLES FROM B OOK S UM DATASET
Revised: B E XAMPLES FROM B OOK S UM DATASET

Original: Here, we describe the inputs and outputs used for training models on BookSum (Kryscinski et al., 2021).
Revised: Here, we describe the inputs and outputs used for training models on BookSum (Kryscinski et al., 2021).

Original: The input documents are chapters from books.
Revised: The input documents are chapters from books.

Original: The output summaries are human-written, obtained from various sources such as CliffNotes, SparkNotes, GradeSaver, etc.
Revised: The output summaries are human-written, obtained from various sources such as CliffNotes, SparkNotes, GradeSaver, etc.

Original: Below are examples of summaries from BookSum.
Revised: Below are examples of summaries from BookSum.

Original:  of Chapter 18 of “Sense and Sensibility” by Jane Austen.
Revised: Summary of Chapter 18 of “Sense and Sensibility” by Jane Austen.

Original: Edward’s reticence became more noticeable as his visit continued.
Revised: Edward’s reticence became more noticeable as his visit continued.

Original: On one occasion Marianne attempted to leave the couple alone together and met with the speedy withdrawal of Edward from the room where she had left them.
Revised: On one occasion Marianne attempted to leave the couple alone together and met with the speedy withdrawal of Edward from the room where she had left them.

Original: One day when the sisters and Edward were having breakfast, Marianne noticed ”a ring, with a plait of hair in the centre,” on one of Edward’s fingers.
Revised: One day when the sisters and Edward were having breakfast, Marianne noticed ”a ring, with a plait of hair in the centre,” on one of Edward’s fingers.

Original: She asked him if it was Fanny’s, and Edward answered in the affirmative.
Revised: She asked him if it was Fanny’s, and Edward answered in the affirmative.

Original: Elinor and Marianne, however, believed it was Elinor’s, although while Marianne thought it was freely given, Elinor was ”conscious it must have been procured by some theft or contrivance unknown to herself.”
Revised: Elinor and Marianne, however, believed it was Elinor’s, although while Marianne thought it was freely given, Elinor was ”conscious it must have been procured by some theft or contrivance unknown to herself.”

Original: At noon, Sir John and Mrs.
Revised: At noon, Sir John and Mrs.

Original: Jennings arrived, curious about the guest at the cottage.
Revised: Jennings arrived, curious about the guest at the cottage.

Original: Realizing that he was the mysterious man with a name beginning with ”F,” Sir John insisted that they must ”drink tea” at Barton Park that night and dine there the following day.
Revised: Realizing that he was the mysterious man with a name beginning with ”F,” Sir John insisted that they must ”drink tea” at Barton Park that night and dine there the following day.

Original: Summary of Chapter 4 of

Original:  “The Picture of Dorian Gray” by Oscar Wilde.
Revised: Summary of Chapter 4 of “The Picture of Dorian Gray” by Oscar Wilde.

Original: Over the next several years, Dorian becomes obsessed with the book given to him by Lord Henry.
Revised: Over the next several years, Dorian becomes obsessed with the book given to him by Lord Henry.

Original: He buys multiple copies of the ”first edition, and them bound in different colors so that they might suit his moods.”
Revised: He buys multiple copies of the ”first edition, and them bound in different colors so that they might suit his moods.”

Original: To Dorian, ”the whole book...seemed to contain the story of his own life, written before he had lived it.”
Revised: To Dorian, ”the whole book...seemed to contain the story of his own life, written before he had lived it.”

Original: Like the book’s young hero, Dorian begins immersing himself in varied interests, including religion, mysticism, music, jewels, ancient tapestries, and the study of his own ancestors.
Revised: Like the book’s young hero, Dorian begins immersing himself in varied interests, including religion, mysticism, music, jewels, ancient tapestries, and the study of his own ancestors.

Original: Dorian is, however, quick to change obsessions once they no longer interest him, following the whims of his desire with the passion of an artist.
Revised: Dorian is, however, quick to change obsessions once they no longer interest him, following the whims of his desire with the passion of an artist.

Original: He clings to each current obsession fervently, studying it and acquiring as many fanciful examples of it as he can find.
Revised: He clings to each current obsession fervently, studying it and acquiring as many fanciful examples of it as he can find.

Original: He buys extravagent gowns covered in hundreds of pearls to feed his interest in jewels, and ancient, golden-threaded tapestries to nourish his curiosity about embroidery.
Revised: He buys extravagent gowns covered in hundreds of pearls to feed his interest in jewels, and ancient, golden-threaded tapestries to nourish his curiosity about embroidery.

Original: As soon as a given subject has exhausted itself in his mind, however, he drops it in favor of his next interest.
Revised: As soon as a given subject has exhausted itself in his mind, however, he drops it in favor of his next interest.

Original: For the next 18 years, capriciousness is a way of life for Dorian...
Revised: For the next 18 years, capriciousness is a way of life for Dorian...

Original: Summary of Chapter 6 of “The Brothers Karamazov, Volume 3” by Fyodor Dostoevsky.
Revised: Summary of Chapter 6 of “The Brothers Karamazov, Volume 3” by Fyodor Dostoevsky.

Original: When Alyosha enters his father’s home, his father and brother Ivan are finishing dinner and the servants attend them in the dining room.
Revised: When Alyosha enters his father’s home, his father and brother Ivan are finishing dinner and the servants attend them in the dining room.

Original: Here we’re given a little background on Smerdyakov,
Revised: Here we’re given a little background on Smerdyakov,

Original: Stinking Lizaveta’s son.
Revised: Stinking Lizaveta’s son.

Original: A quiet, sullen child, Smerdyakov was discovered to have the ”falling sickness” ; he would fall into fits every month or so.
Revised: A quiet, sullen child, Smerdyakov was discovered to have the ”falling sickness” ; he would fall into fits every month or so.

Original: For some reason Fyodor grew quite fond of the boy after his illness was discovered.
Revised: For some reason Fyodor grew quite fond of the boy after his illness was discovered.

Original: One day, Smerdyakov was found picking through his soup, and
Revised: One day, Smerdyakov was found picking through his soup, and

Original: Fyodor decided that Smerdyakov should be trained as a chef.
Revised: Fyodor decided that Smerdyakov should be trained as a chef.

Original: After training in Moscow, Smerdyakov came back to be Fyodor’s cook.
Revised: After training in Moscow, Smerdyakov came back to be Fyodor’s cook.

Original: Now 24, he is just as sullen and silent as ever.
Revised: Now 24, he is just as sullen and silent as ever.

Original: C F URTHER DETAILS ABOUT BRAIN ALIGNMENT APPROACH
Revised: C F URTHER DETAILS ABOUT BRAIN ALIGNMENT APPROACH

Original: In this section, we describe additional details for aligning the word-level NLP representations to human brain activity.
Revised: In this section, we describe additional details for aligning the word-level NLP representations to human brain activity.

Original: We follow a general alignment approach that has been previously used in several works (Jain & Huth, 2018; Toneva & Wehbe, 2019; Schrimpf et al., 2021).
Revised: We follow a general alignment approach that has been previously used in several works (Jain & Huth, 2018; Toneva & Wehbe, 2019; Schrimpf et al., 2021).

Original: We start with the following variables.
Revised: We start with the following variables.

Original: NLP representations ∈ R 5176 ×
Revised: NLP representations ∈ R 5176 ×

Original: d , where where d is the embedding size.
Revised: d , where where d is the embedding size.

Original: Brain activity ∈
Revised: Brain activity ∈

Original: R n × v i , where n is the number of TRs and v i is the number of voxels in the brain of participant i .
Revised: R n × v i , where n is the number of TRs and v i is the number of voxels in the brain of participant i .

Original: First, we reduce the dimensionality of the word-level NLP representations ∈ R 5176 ×
Revised: First, we reduce the dimensionality of the word-level NLP representations ∈ R 5176 ×

Original: d using PCA and retain the top 10 principle components (more than 75% of the variance) to result in a matrix ∈ R 5176 × 10 .
Revised: d using PCA and retain the top 10 principle components (more than 75% of the variance) to result in a matrix ∈ R 5176 × 10 .

Original: Next, we next construct TR-level NLP representations by down-sampling the word embeddings (words presented at 0.5 seconds) to the TR rate (2 seconds) by averaging to corresponding word-level NLP representations to result in a matrix ∈ R n × 10 , where n is the number of TRs.
Revised: Next, we next construct TR-level NLP representations by down-sampling the word embeddings (words presented at 0.5 seconds) to the TR rate (2 seconds) by averaging to corresponding word-level NLP representations to result in a matrix ∈ R n × 10 , where n is the number of TRs.

Original: The final NLP representations are constructed by concatenating the TR-level NLP representations corresponding to the previous 4 TRs.
Revised: The final NLP representations are constructed by concatenating the TR-level NLP representations corresponding to the previous 4 TRs.

Original: The previous TR-level embeddings are included in order to account for the lag in the hemodynamic response that fMRI records.
Revised: The previous TR-level embeddings are included in order to account for the lag in the hemodynamic response that fMRI records.

Original: Because the response measured by fMRI is an indirect consequence of brain activity that peaks about 6 seconds after stimulus onset, predictive methods commonly include preceding timepoints (Nishimoto et al., 2011;
Revised: Because the response measured by fMRI is an indirect consequence of brain activity that peaks about 6 seconds after stimulus onset, predictive methods commonly include preceding timepoints (Nishimoto et al., 2011;

Original: Wehbe et al., 2014a;
Revised: Wehbe et al., 2014a;

Original: Huth et al., 2016).
Revised: Huth et al., 2016).

Original: This allows for a data-driven estimation of the hemodynamic response functions (HRFs) for each voxel, which is preferable to assuming one because different voxels may exhibit different HRFs.
Revised: This allows for a data-driven estimation of the hemodynamic response functions (HRFs) for each voxel, which is preferable to assuming one because different voxels may exhibit different HRFs.

Original: These steps produce the final NLP inputs to the brain alignment stage X ml,s ∈ R n × 40 , for model m , layer l , sequence length s .
Revised: These steps produce the final NLP inputs to the brain alignment stage X ml,s ∈ R n × 40 , for model m , layer l , sequence length s .

Original: After reducing the dimensionality of NLP representations, we learn a function that uses NLP representations X ml,s ∈ R n × 40 to predict brain activity ∈ R n × v i .
Revised: After reducing the dimensionality of NLP representations, we learn a function that uses NLP representations X ml,s ∈ R n × 40 to predict brain activity ∈ R n × v i .

Original: Similarly to previous work, we parameterize this function as a linear function, regularized using the ridge penalty.
Revised: Similarly to previous work, we parameterize this function as a linear function, regularized using the ridge penalty.

Original: We train the function in a cross-validated way and test its performance on the data that was heldout during training.
Revised: We train the function in a cross-validated way and test its performance on the data that was heldout during training.

Original: We select the ridge parameter via nested cross-validation.
Revised: We select the ridge parameter via nested cross-validation.

Original: Since the fMRI data was collected in 4 runs of approximately equal length, we use 4 -fold cross validation where each fold corresponds to holding out one run of fMRI data for testing.
Revised: Since the fMRI data was collected in 4 runs of approximately equal length, we use 4 -fold cross validation where each fold corresponds to holding out one run of fMRI data for testing.

Original: We also remove 10 TRs of fMRI data and the corresponding
Revised: We also remove 10 TRs of fMRI data and the corresponding

Original: NLP representations from the ends of each test run to avoid possible train-test overlap.
Revised: NLP representations from the ends of each test run to avoid possible train-test overlap.

Original: Finally, we evaluate the alignment between the NLP predictions and the true fMRI activity, as described in the main paper.
Revised: Finally, we evaluate the alignment between the NLP predictions and the true fMRI activity, as described in the main paper.

Original: D I NTERPRETABILITY APPROACH TO COMPARE BRAIN ALIGNMENT ACROSS VARIOUS DISCOURSE FEATURES
Revised: D I NTERPRETABILITY APPROACH TO COMPARE BRAIN ALIGNMENT ACROSS VARIOUS DISCOURSE FEATURES

Original: Algorithm 2 Interpretability approach to compare brain alignment across discourse features 1: Input: NLP encoding predictions, fMRI activity ▷ Both ∈ R n TRs × v i voxels 2: for all discourse features do 3: label words with discourse feature ▷ One-hot vector ∈ R 5176 words 4: map labeled words to fMRI TRs labeled with discourse feature ▷ One-hot vector ∈ R n 5: extract NLP encoding predictions for TRs with the discourse feature ▷ R 160 × v i 6: extract actual brain activity for TRs with the discourse feature ▷ R 160 × v i 7: compute Pearson correlation between extracted NLP predictions and brain activity ▷ R v i 8:
Revised: Algorithm 2 Interpretability approach to compare brain alignment across discourse features 1: Input: NLP encoding predictions, fMRI activity ▷ Both ∈ R n TRs × v i voxels 2: for all discourse features do 3: label words with discourse feature ▷ One-hot vector ∈ R 5176 words 4: map labeled words to fMRI TRs labeled with discourse feature ▷ One-hot vector ∈ R n 5: extract NLP encoding predictions for TRs with the discourse feature ▷ R 160 × v i 6: extract actual brain activity for TRs with the discourse feature ▷ R 160 × v i 7: compute Pearson correlation between extracted NLP predictions and brain activity ▷ R v i 8:

Original: Result: Pearson correlation score for discourse feature, for each voxel
Revised: Result: Pearson correlation score for discourse feature, for each voxel

Original: ▷ R v i 9: end for 10: Result: 
Revised: ▷ R v i 9: end for 10: Result: Pearson score for all discourse features

Original: We present the pseudocode for our interpretability approach in Algorithm 2, and elaborate here.
Revised: We present the pseudocode for our interpretability approach in Algorithm 2, and elaborate here.

Original: The Pearson correlation score is computed between a pair of NLP encoding predictions and actual brain activity.
Revised: The Pearson correlation score is computed between a pair of NLP encoding predictions and actual brain activity.

Original: Both ∈ R n × v i , where n is the number of TRs and v i is the number of voxels in the brain of participant i .
Revised: Both ∈ R n × v i , where n is the number of TRs and v i is the number of voxels in the brain of participant i .

Original: (Line 1)
Revised: (Line 1)

Original: For each discourse feature (Line 2), we first label the words in the Harry Potter chapter that correspond to the discourse feature, producing a one-hot vector ∈ R 5176 .
Revised: For each discourse feature (Line 2), we first label the words in the Harry Potter chapter that correspond to the discourse feature, producing a one-hot vector ∈ R 5176 .

Original: (Line 3)
Revised: (Line 3)

Original: Then, we apply the mapping from the words to fMRI TRs, because fMRI values are recorded once for every few words.
Revised: Then, we apply the mapping from the words to fMRI TRs, because fMRI values are recorded once for every few words.

Original: This produces a one-hot vector ∈ R n , which labels the TRs which contain the discourse feature.
Revised: This produces a one-hot vector ∈ R n , which labels the TRs which contain the discourse feature.

Original: (Line 4)
Revised: (Line 4)

Original: Different discourse features will have different number of TRs labeled, e.g. Characters has 236 TRs labeled, whereas Emotions has 170 TRs labeled.
Revised: Different discourse features will have different number of TRs labeled, e.g. Characters has 236 TRs labeled, whereas Emotions has 170 TRs labeled.

Original: For each discourse feature, we sample 160 TRs out of the TRs labeled.
Revised: For each discourse feature, we sample 160 TRs out of the TRs labeled.

Original: We do this to ensure that the same number of TRs are used for each discourse feature, which will allow a fair comparison of Pearson correlation scores between discourse features.
Revised: We do this to ensure that the same number of TRs are used for each discourse feature, which will allow a fair comparison of Pearson correlation scores between discourse features.

Original: Hence, we have a one-hot vector ∈ R 160 .
Revised: Hence, we have a one-hot vector ∈ R 160 .

Original: We extract the NLP encoding predictions and brain activity for only the TRs containing Characters, both R 160 × v i (Line 5 and 6).
Revised: We extract the NLP encoding predictions and brain activity for only the TRs containing Characters, both R 160 × v i (Line 5 and 6).

Original: Finally, we compute their Pearson correlation, producing a vector R v i .
Revised: Finally, we compute their Pearson correlation, producing a vector R v i .

Original: This produces one Pearson correlation score for each brain voxel (Line 7).
Revised: This produces one Pearson correlation score for each brain voxel (Line 7).

Original: To get the Pearson score for the Characters discourse feature, we take the average across all v i voxels.
Revised: To get the Pearson score for the Characters discourse feature, we take the average across all v i voxels.

Original: Earlier, we mentioned that for each discourse feature, words in the Harry Potter chapter have been labeled.
Revised: Earlier, we mentioned that for each discourse feature, words in the Harry Potter chapter have been labeled.

Original: For example, the word ”Draco” is marked with the Character feature, and the word ”climbed” with the Motion feature.
Revised: For example, the word ”Draco” is marked with the Character feature, and the word ”climbed” with the Motion feature.

Original: We used discourse feature labels from Wehbe et al.
Revised: We used discourse feature labels from Wehbe et al.

Original: (2014a).
Revised: (2014a).

Original: For each discourse feature, we computed the average Pearson correlation for booksum and base models.
Revised: For each discourse feature, we computed the average Pearson correlation for booksum and base models.

Original: To compute these averages, we use all 8 models, layers 1 to 12, sequence lengths fromto 1000, and all 8 human subjects.
Revised: To compute these averages, we use all 8 models, layers 1 to 12, sequence lengths fromto 1000, and all 8 human subjects.

Original: The results are reported in 15.
Revised: The results are reported in 16.

Original: [Equation] TATISTICAL TESTS AND FALSE DISCOVERY RATE (FDR) CORRECTION USING THE B ENJAMINI –H OCHBERG (BH) PROCEDURE
Revised: E S TATISTICAL TESTS AND FALSE DISCOVERY RATE (FDR) CORRECTION USING THE B ENJAMINI –H OCHBERG (BH) PROCEDURE

Original: We perform t-tests to identify which results are statistically significant (and not simply due to chance).
Revised: We perform t-tests to identify which results are statistically significant (and not simply due to chance).

Original: As our paper performs a large number of statistical tests, we obtain a large number of p-values.
Revised: As our paper performs a large number of statistical tests, we obtain a large number of p-values.

Original: Thus, some will have p-values below 0.05 purely by chance.
Revised: Thus, some will have p-values below 0.05 purely by chance.

Original: To correct for this problem of false discovery, we adjust the false discovery rate (FDR) using the Benjamini-Hochberg (BH) procedure.
Revised: To correct for this problem of false discovery, we adjust the false discovery rate (FDR) using the Benjamini-Hochberg (BH) procedure.

Original: For each original p-value, this produces an new, adjusted p-value.
Revised: For each original p-value, this produces an new, adjusted p-value.

Original: Finally, we compare the adjusted p-value against a significance level of 0.05 to identify results that are statistically significant.
Revised: Finally, we compare the adjusted p-value against a significance level of 0.05 to identify results that are statistically significant.

Original: For our paper, we report only statistically significant results after FDR-BH correction.
Revised: For our paper, we report only statistically significant results after FDR-BH correction.

Original: F R ESULTS FOR 20 V 20ACCURACY ACROSS SEQUENCE LENGTHS AND LAYERS
Revised: F A GGREGATED 20 V 20 AND P EARSON CORRELATION RESULTS FOR ALLPAIRS OF MODELS

Revised: G R ESULTS FOR 20 V 20 ACCURACY ACROSS SEQUENCE LENGTHS AND LAYERS

Original: G R ESULTS FOR B RAIN R EGIONS OF I NTEREST (ROI S )
Revised: H R ESULTS FOR B RAIN R EGIONS OF I NTEREST (ROI S )

Original: H R ESULTS FOR D ISCOURSE F EATURES
Revised: I R ESULTS FOR D ISCOURSE F EATURES

Revised: K R ESULTS FOR S UMMARIZATION MODELS ON N ON - NARRATIVE DATASETS

Revised: In Appendix L, we show that improved brain alignment is not simply due to the first possibility: domain similarity.

Revised: In this appendix, we show that improved brain alignment is not simply due to the second possibility: summarization learning objective.

Original: [Figure]
Revised: We compared the brain alignment for 3 models: BART-base, BART-booksum and BART-cnn. BART-base was pretrained by corrupting documents and learning to reconstruct the original text, on a combination of books (narratives) and Wikipedia data (see Appendix A). BART-booksum initializes weights of BART-base and was further trained to summarize long narratives on the BookSum summarization dataset. BART-cnn initializes weights of BART-base and was further trained to summarize news articles from the CNN Dailymail dataset.

Revised: The model trained to summarize long narratives (BART-booksum) has significantly greater brain alignment than the model pretrained with a language modeling objective (BART-base), as well as the model trained to summarize news articles (BART-cnn).

Revised: Furthermore, BART-cnn only performs on par with the pretrained BART-base at brain alignment.

Revised: This shows that improved brain alignment in BART-booksum over BART-base is not simply due to the summarization objective.

Revised: Both the input documents and output summaries in the BookSum dataset are substantially longer than those in the CNN Dailymail dataset, which promotes deeper long-range understanding.

Revised: For the BookSum dataset, the input documents have an average of more than 5000 words, and output summaries have roughly 500 words on average.

Revised: For the CNN Dailymail dataset, the input documents have 766 words on average while the summaries have 53 words on average.

Revised: The texts from the BookSum dataset contain richer discourse features.

Revised: For example, characters develop over the course of the entire book through their thoughts, actions, and interactions with other characters.

Revised: We show in Section 6 that the booksum models are developing richer representations of these discourse features.

Revised: L R ESULTS FOR TRAINING ON B OOK S UM DATASET WITH A LANGUAGE MODELING OBJECTIVE

Revised: For 2 models (BART, LongT5), the booksum variant has substantially better brain alignment than the

Revised: LM-booksum counterpart.

Revised: For the other 2 models (LED, BigBird), there is similar brain alignment for the booksum and LM-booksum variants.

Revised: However, the LM-booksum variants have substantially better LM performance on the fMRI dataset than the booksum models.

Revised: As LM performance has been shown to contribute to brain alignment (Schrimpf et al., 2021; Goldstein et al., 2022), it is difficult to disentangle the brain alignment contributions of “deeper understanding” and LM performance in these models.

Revised: The LM-booksum models have greater LM performance than both the base and booksum models, whereas the base and booksum models have similar LM performance.

Revised: Hence, we believe that the LM-booksum models may not be a better baseline than base models.

Revised: Combined, these results suggest that the improved brain alignment is not simply due to domain similarity.

Revised: M R ESULTS FOR GPT-2 - BOTH 20 V 20 AND P EARSON CORRELATION

Revised: We ran experiments to compute the aggregated 20v20 and Pearson correlation for GPT-2.

Revised: For 20v20,

Revised: LongT5-booksum has greater brain alignment than all other models, including GPT-2.

Revised: For Pearson correlation, LongT5-booksum and GPT-2 have similar performance, and are both greater than the other models.

Revised: Our work is focused on investigating how the training objective of summarizing narrative texts leads to improved brain alignment, hence we compared 4 pairs of base and booksum models.

Revised: Attaining a model with SOTA brain alignment is not the goal of our work, hence we did not finetune GPT-2 on the booksum dataset.

Revised: That is outside the scope of our project, but it may be an interesting direction for future research to pursue.

Revised: N R ESULTS FOR TRAINING ON B ALANCED DISCOURSE FEATURES

Revised: BART-base and BART-booksum achieve greater brain alignment for Characters than other discourse features.

Revised: When training BART for deep understanding, brain alignment significantly improves for all discourse features.

Revised: However, it improves more for Characters than other discourse features.

Revised: There are roughly 1200 fMRI TRs in the Harry Potter dataset where predictions are generated.

Revised: Among this set, there are 242 Characters TRs, 174 Emotion TRs, 165 Motion TRs, and the remaining are TRs labeled as “Others”.

Revised: For each of the 3 discourse features, their TRs are relatively evenly distributed across the 1200 TRs.

Revised: All 3 discourse features’ TRs are also distributed in similar ways.

Revised: In Section 6, our results showed that Characters improved more (booksum minus base) than other discourse features when training models for deeper understanding.

Revised: However, another possible cause of this result is that there are a different number of TRs for each discourse feature in the training set (242 for Characters, 174 for Emotion, 165 for Motion).

Revised: Hence, in this section, we ran additional experiments where we trained the linear encoding models on only the last 500 TRs, where the number of TRs for each discourse feature was more balanced (97 for Characters, 99 for Emotion, 72 for Motion).

Revised: We then sampled an equal number of TRs (74) for each discourse feature during testing on the first 700 TRs.

Revised: After controlling for an equal number of TRs in the training and test set across all discourse features, we still see the result that

Revised: Characters improved more (booksum minus base) than other discourse features during training for deeper understanding.

Revised: O R ESULTS FOR TRAINING LARGER MODELS

Revised: We ran experiments to evaluate the brain alignment for two model variants of LongT5-large withlayers.

Revised: The first model (long-t5-large-base) was pretrained by masking and generating sentences on the C4 dataset, a collection of roughly 750 GB of English texts sourced from the public Common

Revised: Crawl open repository.

Revised: The second model (long-t5-large-booksum) initializes weights from the first model, and further trains the model to generate summaries for the BookSum narrative dataset.

Revised: Our results show that the booksum model has greater brain alignment than the base model, and the brain alignment improvements occur in the middle and late layers (from layers 12 to 24).

Revised: In BigBird, the brain alignment improvements occur in the middle layers.

Revised: On the other hand, for both the 12-layer and 24-layer variants of LongT5, the brain alignment improvements occur in the middle and late layers.

Revised: For different models (BART, LED, BigBird, LongT5), we observed a different pattern of brain alignment improvements across layers and sequence lengths.

Revised: We find this observation fascinating, and we hope to explore in a future work the reasons why different models improve brain alignment differently even though they are all trained for deeper understanding.

Revised: P N OISE CEILING ESTIMATES FOR 20 V 20 METRIC

Revised: We ran experiments to compute the noise ceiling estimates for all 8 subjects, for the 20v20 metric.

Revised: To do this, we followed previous work on computing a noise ceiling for naturalistic data with only 1 repetition of the stimulus (Schrimpf et al., 2021).

Revised: We point the reader to this previous work for a detailed description of the steps.

Revised: Briefly, this noise ceiling estimation is based on the ability to predict data from one participant using data from a different participant.

Revised: The prediction is done by training linear encoding models that use one source subject’s fMRI response to predict a different target subject’s fMRI response, in a cross-validated manner.

Revised: The average noise ceiling across participants is 0.61 +/-

Revised: 0.016 (sem) for 20v20.

Revised: For sequence length 500, all BookSum models reach 98% of this noise ceiling (20v20 values of 0.6, for different layers across models).