Paper: Speech language models lack important brain-relevant semantics (ICLR-2024)

Reviewer comment (Novelty):
The paper lacks a general discussion about correlated features in natural stimuli, which, in my view, is directly what the authors are trying to test. Citing some past work along these lines would be helpful (for instance, Norman-Haignere et al. 2018, Groen et al. 2018).

Author response:
**W1. The paper lacks a general discussion about correlated features in natural stimuli, which, in my view, is directly what the authors are trying to test. Citing some past work along these lines would be helpful (for instance, Norman-Haignere et al. 2018, Groen et al. 2018).**

Thank you for suggesting relevant works on correlated features in natural stimuli. These citations will be incorporated in the updated draft. Further, we summarize the implications of our findings in the updated manuscript **(see Appendix section H)**.

Reviewer comment (Writing):
In various parts of the paper, there seems to be a bit of a conflation with correlation and causation. For instance, the authors state: “This raises questions about what types of information text-based language models capture that is relevant to early auditory processing.”, but I think the authors mean something like “This raises questions about what types of information text-based language models capture that is **correlated with features** relevant to early auditory processing”. Another instance: “This is possibly because the language models do not process visual features such as edges.” -> “This is possibly because the **features in language models are not correlated with visual stimulus features** such as edges.” There are other places like this in the paper that I encourage the authors to fix.

Author response:
**W2.  In various parts of the paper, there seems to be a bit of a conflation with correlation and causation.**

As per the reviewer's suggestion, we have now addressed the two sentences and other places in the updated draft.

Reviewer comment (Experiments):
The title of the paper seems a bit limited in scope and focuses on a result in the paper that is a bit of a straw man. The speech-based models that were tested (wave2vec2.0 and whisper) are not trained to capture semantic information, so it doesn’t seem surprising that they fail to capture semantic-driven responses.

Author response:
**W3. The title of the paper seems a bit limited in scope and focuses on a result in the paper that is a bit of a straw man. The speech-based models that were tested (wave2vec2.0 and whisper) are not trained to capture semantic information, so it doesn’t seem surprising that they fail to capture semantic-driven responses.**

* All models that we test, both speech-based and text-based, are trained to predict a missing part of the input. 
* Moreover, one of the speech-based models that we test (Whisper) has an encoder-decoder architecture and is trained as a speech-to-text model, so the objective function is quite close to that of a text-based language model. 
* When text-based language models first came out, it was indeed surprising that they were able to perform so many semantic tasks despite being trained with such a simple objective function. 
* However, we continue to see their performance excel and we also show here that their ability to predict many language regions is not due to low-level features. 
* Even though speech-based models are trained in a similar way, we see that their ability to predict late language regions is almost entirely due to low-level features, suggesting that they fail to capture brain-relevant semantics in language regions.

Reviewer comment (Writing):
In the first paragraph of the introduction, which of the cited papers claims that text-trained language models predict early sensory cortex activities? This is currently unclear (many of the cited works listed under “speech” only study models with a waveform or spectrogram input), and seems very important to distinguish as is the main motivation for the study.

Author response:
**Q1: In the first paragraph of the introduction, which of the cited papers claims that text-trained language models predict early sensory cortex activities? This is currently unclear (many of the cited works listed under “speech” only study models with a waveform or spectrogram input), and seems very important to distinguish as is the main motivation for the study.**

Thank you for the question. 
* We want to clarify that all cited papers related to text-based language models predict early sensory activity to an impressive degree. It is important to note that these studies used secondary auditory areas that are also part of language regions. 
* In case of speech-based language models, [Vaidya et al. 2022 ICML, Millet et al. 2022 NeurIPS] have shown that all SSL based models predict the early auditory cortex (A1 region) to an impressive degree and trails behind simple word embeddings across the rest of the cortex. 

*[Vaidya et al. 2022] Self-supervised models of audio effectively explain human cortical responses to speech, ICML-2022*

*[Millet et al. 2022] Toward a realistic model of speech processing in the brain with self-supervised learning. NeurIPS-2022*

Reviewer comment (Experiments):
Are there some baseline models that the authors could include to better contextualize the results? For instance, how well does a random-feature baseline do (useful to see a “lower bound”)? How well does a classic primary-area model such as the motion energy model, or a spectrotemporal filter bank do at capturing the primary area responses? For the classic model comparison, if neither language model is predicting the voxels better than these baseline models, then should we be considering the variance they are explaining as significant at all?

Author response:
**Q2: Are there some baseline models that the authors could include to better contextualize the results?
How well does a classic primary-area model such as the motion energy model, or a spectrotemporal filter bank do at capturing the primary area responses? For the classic model comparison, if neither language model is predicting the voxels better than these baseline models, then should we be considering the variance they are explaining as significant at all?**

* Based on the reviewer’s suggestion, we now plot the average normalized brain alignment for classical models, such as low-level stimulus features (textual, speech, and visual) during both reading and listening in the early sensory areas (early visual and A1), as shown in **Fig. 19 (see section G in Appendix)**. 
* Additionally, we report results for the individual low-level stimulus features as baseline models, including the number of letters, PowSpec, phonological features, and motion energy features, particularly in early sensory processing regions. It appears that both text-based and speech-based language models meet the baselines and show improvement in early sensory processing regions, particularly early visual areas in reading and A1 areas during listening. 
* Among low-level stimulus features, motion energy features have better normalized brain alignment during reading in the early visual area and phonological features have better brain alignment during listening in the A1 region.

* Overall, in the context of classic model comparison, language model representations predict better than baseline models and the variance explained by these models is significant.

Reviewer comment (Experiments):
Is ridge regression necessary when fitting the low-level features to the neural model representations? It seems like there is nearly infinite available data for this fit (I believe the low-level features are automatically extracted) and there is no noise, so I’m just wondering what the intuition is for using a ridge parameter here.

Author response:
**Q3. Is ridge regression necessary when fitting the low-level features to the neural model representations?**

* We fit the removal method using the stimulus text, which is limited by the fMRI dataset. This is why we use ridge regression for the fitting procedure to minimize possible overfitting. 
* The reviewer’s suggestion to fit a more general removal approach using additional text data is very interesting to explore in future work

Reviewer comment (Experiments):
Are the visual words and the onset of auditory words aligned in time in the dataset? That seems particularly important for this comparison, as one of the overall features encoded by both models may be “when a new thing starts”.

Author response:
**Q4. Are the visual words and the onset of auditory words aligned in time in the dataset? That seems particularly important for this comparison, as one of the overall features encoded by both models may be “when a new thing starts”.**

* The same stories from listening sessions were used for reading sessions. Praat’s word representation for each story (W, t) was used for generating the reading stimuli. The words of each story were presented one-by-one at the center of the screen using a rapid serial visual presentation (RSVP) procedure. 
* During reading, each word was presented for a duration precisely equal to the duration of that word in the spoken story. RSVP reading is different from natural reading because during RSVP the reader has no control over which word to read at each point in time. 
* Therefore, to make listening and reading more comparable, the authors matched the timing of the words presented during RSVP to the rate at which the words occurred during listening.

Reviewer comment (Writing):
The ROIs are described as “language relevant” but early visual and early auditory areas are included. It seems non-standard to refer to these as “language relevant”.

Author response:
**Minor Points**

**The ROIs are described as “language relevant” but early visual and early auditory areas are included.**

Thank you for this suggestion. Now, we refer to the regions as: three early sensory and four language-relevant ROIs in the human brain.

We updated these minor points in the revised draft.

Reviewer comment (Novelty):
The conclusion that lower-level features explain a large portion of the variance in lower-level sensory areas is not surprising. The fact that high-level language regions are more robust to lower-level features in the context of text-based language models is also not surprising, since the response is higher-level and the model does not have any visual or auditory information baked into it. The fact that speech models are impacted by including audio and speech features is not as surprising as the authors suggest, since the models are taking audio as input and the representations are only being derived from 2-second stimuli and thus lose much of the higher-level language information that is present in a text-based model. This paper is essentially confirmatory and should be framed as such, in my opinion.

Author response:
**Q. The influence of low-level features on early sensory processing regions and the resilience of high-level language regions in text-based models, are unsurprising. Similarly, the impact on speech models when incorporating audio features aligns with expectations, making the paper essentially confirmatory in nature.**

It’s not clear which results about the speech models align with the reviewer’s expectations. We find the following quite surprising and noteworthy:
* The impressive predictive performance of speech models in the early auditory cortex is NOT entirely accounted for by low-level features. As the reviewer kindly pointed out, we investigate a comprehensive set of low-level features that are thought to relate to processing in the early auditory cortex. We show that, surprisingly, 40-50% of the explainable variance in the early auditory cortex is left to explain after removing the low-level features we consider. This shows that speech-based models capture additional features that are important for early auditory cortex and understanding these further can help us better understand this part of the brain.
* Another surprising result is that, in contrast to the alignment of speech-based models in the early auditory cortex, the alignment in late language regions is almost entirely due to low-level features. The surprise is coming from the fact that the predictive performance of the late language regions is still good and almost on par with text-based models, and also from the fact that several recent works have proposed speech-based models for good models of language in the brain [Yuanning Li et al. 2023 Nature Neuroscience, Chen et al. 2023 Arxiv]. Our work clearly demonstrates the importance of careful consideration of the reasons behind the alignment that is observed between models and human brain recordings.

*[Yuanning Li et al. 2023] Dissecting neural computations in the human auditory pathway using deep neural networks for speech, Nature Neuroscience*

*[Chen et al. 2023] Do self-supervised speech and language models extract similar representations as human brain?*

**Novelty of findings and framing:**

Thanks for taking the time to clarify. 
* We are happy to hear that you find the results we highlighted in our response valuable. 
* We agree that we can further work on highlighting the most surprising findings in the text, and your feedback has been helpful in guiding us in that. We will update the abstract, introduction, and discussion to highlight the two results we discussed in the response.
* In the short discussion period, we gave priority to completing new analyses and addressing the concerns from all reviewers so we were not able to make all changes to the manuscript that we wanted yet. We will continue to update the manuscript in the coming days.

Reviewer comment (Evaluation):
I don’t see the benefit of the “direct” approach compared with measuring the unique variance of different feature sets using some kind of a variance partitioning framework such as that promoted by the Gallant and Huth labs. Conceptually, the main thing one wants to know is what fraction of the neural response variance can be uniquely explained by a particular model and how much is shared with other models. The direct approach seems like an indirect way to address that question.

Author response:
**Q. I don’t see the benefit of the “direct” approach compared with measuring the unique variance of different feature sets using some kind of a variance partitioning framework such as that promoted by the Gallant and Huth labs.**

We hope that the response to the previous question clarified what we mean by a direct approach. The method we use to remove the linear contribution of a feature to a model’s representation is one way to implement such a direct approach. Another method was investigated by previous work [Oota et al. 2023 NeurIPS] and was shown to yield very similar results. Other methods can also be used. One can imagine devising a similar approach based on variance partitioning; however it is not immediately clear to us how to best take into account the estimate of the noise ceiling in the variance partitioning calculation. 

[Oota et al. 2023 NeurIPS] Joint processing of linguistic properties in brains and language models. NeurIPS-2023

Reviewer comment (Writing):
I find the term “direct” unclear. What is direct about it? What would the indirect approach be?

Author response:
**Q. I also find the term “direct” unclear? What is direct about it? What would the indirect approach be?**

We thank the reviewer for this question and we will clarify this in the main paper.

* An indirect approach is one which first relates model representations to the human brain, followed by independent examination of the related model to some task performance or behavioral output. For instance, [Schrimpf et al. 2021 PNAS] test the computations of a language model that may underlie human language understanding. This is accomplished by an independent examination of the relationship between the models’ ability to predict an upcoming word and their brain predictivity. Similarly, [Goldstein et al. 2022 Nature Neuroscience] provides empirical evidence that both the human brain and language model engage in continuous next-word prediction before word onset. 

* In contrast, the approach we use directly estimates the impact of a specific feature on the alignment between the model and the brain recordings by observing the difference in alignment before and after the specific feature is computationally removed from the model representations.  This is why we refer to this approach as direct.

[Schrimpf et al. 2021]  The neural architecture of language: Integrative modeling converges on predictive processing. PNAS, 2021

[Goldstein et al. 2022]  Shared computational principles for language processing in humans and deep language models. Nature neuroscience, 2022

Reviewer comment (Evaluation):
There is no attempt to understand whether text- and speech-based models account for shared or unique variance from each other, which seems important and natural in the context of this paper.

Author response:
**Q. Whether text- and speech-based models account for shared or unique variance from each other?**

* The direct comparison of text-based and speech-based language models in terms of their brain alignment is not straightforward to do in a rigorous way with models that are currently publicly available. 
* There are many differences between these types of models beyond the data modality, such as the amount of their training data and objective function. Instead of comparing these models that differ in many ways directly, we opt for comparing a model only to itself after removal of a particular feature.  This way we know exactly what a difference in brain alignment is due to: the removal of the specific feature. 
* We agree that directly comparing speech- vs text-based models is an exciting avenue for future work and we hope that it will be possible to do in a scientifically rigorous way soon. We summarize this discussion in the updated manuscript (see Appendix section H).

Reviewer comment (Experiments):
Averaging performance across models is suboptimal because some of the models might be performing quite well, which would be valuable to know. For example, Whisper has been trained on a much broader range of tasks than wav2vec2.0 and it would be useful to know whether it performs better as a consequence. A better choice would be to select the best performing model for the main figure and to put the performance of all models in the appendix. Model selection could be done on training or validation data to prevent overfitting.

Author response:
**Q. Averaging performance across models is suboptimal because some of the models might be performing quite well: Wav2Vec2.0 vs. Whisper ?**

We now show the results per model in the updated **Appendix F (see Fig. 17)**. 
* During listening, specifically for the A1 region, we observe that both speech models, Wav2Vec2.0 and Whisper, exhibit high normalized brain alignment. Additionally, the elimination of low-level textual and speech features results in a significant performance decline in both models. However, it is important to note that these language models have additional information other than low-level features that need to be explored to further explain the early auditory region.
* Similar to the A1 region, we observed that both Wav2Vec2.0 and Whisper exhibit similar normalized brain alignment in late language regions. Moreover, the removal of low-level textual and speech features results in a significant performance decline in both models.

Reviewer comment (Experiments):
It is unclear how activations from different layers were handled. Were they all combined together? Typically, one selects the best-performing layer in a model using the training or validation set.

Author response:
**Q. It is unclear how activations from different layers were handled. Were they all combined together?**

Thank you for the question. 
* We want to clarify that each layer’s activations were used independently to build voxel-wise encoding models, and the results presented in the paper depict the average normalized brain alignment across all of the layers for both text-based and speech-based language models and their corresponding residuals. Based on the reviewers’ suggestion, we now plot the layer-wise normalized brain alignment for the Wav2Vec2.0 model during listening, both before and after removal of one important low-level speech property:  phonological features, as shown in **Fig. 18. (see in the updated Appendix G)**. 

* The results indicate a consistent drop in performance across layers, after removal of Phonological features, specifically in both A1 and the late language regions. 
* The key finding from this analysis is that our results that low level features impact the ability to predict both A1 and late language regions hold across individual layers.

Reviewer comment (Experiments):
The authors need more detail about the stimuli. They should specify the total duration of the story(ies) in the listening and reading conditions, how many words there were, how the words were presented, and the rate they were presented at. For example, for listening, was this a natural story with a variable word rate? Or were the words presented artificially using a fixed ISI? If they used a variable word rate, how does this impact how the features were calculated? Downsampling does not seem straightforward in this case. For the reading condition, how was the text presented? Was there a word presented every few hundred milliseconds or was a whole sentence presented at once? Similarly how does this impact the feature design?

Author response:
**Q. The authors need more detail about the stimuli.**

We use the publicly available naturalistic story reading and listening fMRI dataset provided by [Deniz et al. 2019 Journal of Neuroscience]... [full content as per rebuttal 3 above, identical verbatim].

Reviewer comment (Experiments):
The language ROIs include many regions of the STG that I would consider high-level auditory regions (e.g., respond similarly to native and foreign speech). I would recommend repeating the analyses with the language parcels released by the Fedorenko lab, or at least limiting yourself to the STS.

Author response:
**Q. I would recommend repeating the analyses with the language parcels released by the Fedorenko lab**

* We want to clarify that our selection of late language regions is in fact based on language parcels released by the Fedorenko lab, encompassing broader language regions. These regions include: (i) angular gyrus (AG: PFm, PGs, PGi, TPOJ2, TPOJ3), (ii) anterior temporal lobe (ATL: STSda, STSva, STGa, TE1a, TE2a, TGv, and TGd); (iii) posterior temporal lobe (PTL:  A4, A5, STSdp, STSvp, PSL, STV, TPOJ1); (iv) inferior frontal gyrus (IFG: 44, 45, IFJa, IFSp);  and (v) middle frontal gyrus (MFG: 55b). We will provide this clarification on these language parcels in the revised draft.

Reviewer comment (Experiments):
The visual word form area is quite small and challenging to localize. I suspect the results here reflect what one would see from a generic high-level fusiform visual region. The authors could test this by selecting another nearby region and seeing if the results differ. If the results are similar, I think it is misleading to describe the results as specific to the visual word form area, despite the label provided by the atlas.

Author response:
**Q. The visual word form area is quite small and challenging to localize. The authors could test this by selecting another nearby region and seeing if the results differ. If the results are similar, I think it is misleading to describe the results as specific to the visual word form area, despite the label provided by the atlas.**

Thank you for this question. 
* We conducted the experiments as suggested by the reviewer and indeed found similar patterns in nearby regions, according to the atlas we are using. 
* Since we do not have functional localizers for the VWFA region, the obtained results are inconclusive because it may be that the nearby regions in the atlas overlap with the a particular participant's true VWFA. 
* Consequently, we will refer to these regions as fusiform gyrus areas in the paper instead of VWFA and say that VWFA is part of it but disclose that we are not able to localize it specifically.

Reviewer comment (Evaluation):
I could not follow how the noise ceiling is calculated. What is done with the results from all of the different subsamples? Is there some attempt to extend the results to infinite samples? I am skeptical about calculating a noise ceiling in V1 or A1 for the non-preferred modality. I would expect the noise ceiling to be very close to 0. How was this handled? When possible, it would be preferable to plot both the raw scores and the noise ceiling on the same figure so that you can see both. When you average across voxels for ROI analyses, do you average the noise-corrected values or do you separately average the raw and noise ceiling values and then divide these two numbers?

Author response:
R4 + R5: **Q. How the noise ceiling is calculated. What is done with the results from all of the different subsamples? Is there some attempt to extend the results to infinite samples?**

* We follow previous work [Schrimpf et al. 2021 PNAS]... [full text from rebuttal 4 noise ceiling paragraph]  

**Q. I am skeptical about calculating a noise ceiling in V1 or A1 for the non-preferred modality...**

Thank you for stressing this point... [full text from rebuttal 5 first question paragraph].

Reviewer comment (Evaluation):
For Figure 3, it would be preferable to group by the listening/reading as was done in later figures. The performance between the modalities is not really comparable as these are totally different stimuli (and I am skeptical of the noise ceiling calculation).

Author response:
**Q. The performance between the modalities is not really comparable as these are totally different stimuli (and I am skeptical of the noise ceiling calculation).**

* We want to clarify that the naturalistic reading and listening sessions involve exactly the same stories. Therefore, the naturalistic stimuli is the same for both modalities, enabling a direct comparison between reading and listening across different types of models. 
* Moreover, the noise ceiling estimate is computed separately within each condition, and this is useful to measure the normalized brain alignment, particularly in case there is a difference in the signal-to-noise ratio between the two presentation modalities. Our observations reveal that in the late language regions, the noise ceilings are comparable, as shown in Fig2.

Reviewer comment (Reproducibility):
The equations in the section title “Removal of low-level features from language model representations” make it seem like there is a single regularization term for all of the model features. It seems preferable to do what was done for the neural analyses and to fit a banded ridge model separately on every model feature. The different low-level features have very different dimensionality, so there should be some discussion of how this was handled when concatenating the features. If you z-score each feature then features that have higher dimensionality will have much more influence. It was also not clear to me how cross-validation was handled here. Did you train and validate on a subset of stimuli and then remove the predicted response on test? How many folds were there? This information about cross-validation should also be specified in the voxel-wise encoding model section. For the banded ridge regression, were the lambdas specified separately varied for each feature set? How do we know that this range is sufficient? It is highly sensitive to the scale of the features. How fine was the grid search?

Author response:
R5 + R6 + R8: **Q. “Removal of low-level features... regularization term”**  

Thank you for this question... [full paragraph from rebuttal 5 covering regularization term and dimensionality handling].

**Q. It was also not clear to me how cross-validation was handled here...**

Thank you for this question... [full paragraph from rebuttal 6 covering CV scheme].  

**I still don’t totally follow what this means...**  

Yes, precisely. To be sure we’re on the same page... [full breakdown from rebuttal 8 of CV process].  

**You do not answer this question: lambdas...**

* We optimized the lambda parameters... [full paragraph from rebuttal 8 covering lambda grid].

Reviewer comment (Experiments):
What is the reason for not removing the control features from the neural responses as well? How would doing so impact the results?

Author response:
**Q. What is the reason for not removing the control features from the neural responses as well? How would doing so impact the results?**

* The features can be removed from the model representations (as we do), from the brain recordings, or from both (as suggested by the reviewer). 
* Conceptually, the results of these approaches should be the same because the feature is removed completely from either the input or/and the target and cannot further impact the observed alignment. 
* However, practically, brain recordings are noisier than model representations and so estimating the removal regression model will be more difficult especially with a limited sample size.  
* Therefore, we opt to remove the features from the model representations. 

We will clarify this reasoning in the main paper.

Reviewer comment (Novelty):
Lack of argumentation justifying the study.

Author response:
**Contributions and implications of our work**

Our main contributions are the fine-grained analysis approach of the contributions of different types of information to the brain alignment of speech-based and text-based language models, and the scientific findings of this analysis. We summarize the implications of our findings below and include these in the updated manuscript (**see Appendix section H**).

* Our findings have direct implications for both machine learning and cognitive neuroscience. 
* First, we show that even during speech-evoked brain activity (i.e., listening), the alignment of speech-based models trails behind that of text-based models in the late language regions. More importantly, our results demonstrate that the alignment of speech-based models with these regions is almost entirely explained by low-level stimulus features. Since these regions are purported to represent semantic information, this finding implies that contemporary speech-based models lack brain-relevant semantics. This suggests that new machine learning approaches are needed to improve speech-based models. Furthermore, these results imply that observed similarities between speech-based models and brain recordings in the past [Vaidya et al. 2022 ICML, Millet et al. 2022 NeurIPS] are largely due to low-level information and not semantics, which is important to take into account when interpreting the similarity between language representations in speech-based models and the brain.

* Second, we observe that phonological features explain the most variance during listening for speech-based models, whereas ``number of letters" explains the most variance during reading for text-based models. This result offers us a glimpse into the mechanisms underlying language processing in the brain. 

* Third, we demonstrate a direct residual approach to identify the contribution of specific features to brain alignment. To our knowledge, there is no better alternative to selectively remove information from language models to probe their impact on brain alignment. Using this approach, it is possible to investigate how the human brain processes language during both reading and listening at a finer scale than before. 

*[Vaidya et al. 2022] Self-supervised models of audio effectively explain human cortical responses to speech, ICML-2022*

*[Millet et al. 2022] Toward a realistic model of speech processing in the brain with self-supervised learning. NeurIPS-2022*

Reviewer comment (Experiments):
Why is such a small fMRI dataset used?

Author response:
**Size of fMRI dataset**

We thank the reviewer for this suggestion.

* The fMRI dataset we use is a well-known public dataset that has previously been used in many publications in both ML and Neuroscience venues (ML: [Jain & Huth 2018 NeurIPS, Jain et al. 2020 NeurIPS, Antonello et al. 2021 NeurIPS, Lamarre et al. 2022 EMNLP, Vaidya et al. 2022 ICML;], Neuroscience: [Huth et al. 2016 Nature, Huth et al. 2017 Journal of Neuroscience, Deniz et al. 2019 Journal of Neuroscience]).
* For the kind of analyses that we do and that are common in this area of research, the number of samples per participant is more important than the number of subjects because the predictive models are trained independently for each participant. So, having more samples per participant helps us learn a better predictive model.
* This dataset is one of the largest datasets in terms of samples per participant (~4000 samples), which is the main reason for its frequent use.
* Our results also clearly show that this dataset is sufficient to learn a good predictive model, as we show that we can predict up to 75% of the explainable variance for held-out brain recordings that were not used for training (e.g., Fig. 3).

*[Huth et al. 2016], Natural speech reveals the semantic maps that tile human cerebral cortex. In Nature Neuroscience, 2016*

*[De Heer et al. 2017], The hierarchical cortical organization of human speech processing, Journal of Neuroscience, 2017*

*[Jain & Huth, 2018], Incorporating context into language encoding models for fmri. In NIPS-2018*

*[Deniz et al. 2019], The representation of semantic information across human cerebral cortex during listening versus reading is invariant to stimulus modality. Journal of Neuroscience, 2019*

*[Jain et al. 2020], Interpretable multi-timescale models for predicting fMRI responses to continuous natural speech, In NeurIPS-2020*

*[Antonello et al. 2021], Low-Dimensional Structure in the Space of Language Representations is Reflected in Brain Responses, In NeurIPS-2021*

*[Lamarre et al. 2022] Attention weights accurately predict language representations in the brain, EMNLP-2022*

*[Vaidya et al. 2022] Self-supervised models of audio effectively explain human cortical responses to speech, ICML-2022*

Reviewer comment (Novelty):
What are the technical, methodological, and scientific contributions that would interest the ICLR audience?

Author response:
**Contributions and implications of our work**

Our main contributions are the fine-grained analysis approach of the contributions of different types of information to the brain alignment of speech-based and text-based language models, and the scientific findings of this analysis. We summarize the implications of our findings below and include these in the updated manuscript (**see Appendix section H**).

* Our findings have direct implications for both machine learning and cognitive neuroscience. 
* First, we show that even during speech-evoked brain activity (i.e., listening), the alignment of speech-based models trails behind that of text-based models in the late language regions. More importantly, our results demonstrate that the alignment of speech-based models with these regions is almost entirely explained by low-level stimulus features. Since these regions are purported to represent semantic information, this finding implies that contemporary speech-based models lack brain-relevant semantics. This suggests that new machine learning approaches are needed to improve speech-based models. Furthermore, these results imply that observed similarities between speech-based models and brain recordings in the past [Vaidya et al. 2022 ICML, Millet et al. 2022 NeurIPS] are largely due to low-level information and not semantics, which is important to take into account when interpreting the similarity between language representations in speech-based models and the brain.

* Second, we observe that phonological features explain the most variance during listening for speech-based models, whereas ``number of letters" explains the most variance during reading for text-based models. This result offers us a glimpse into the mechanisms underlying language processing in the brain. 

* Third, we demonstrate a direct residual approach to identify the contribution of specific features to brain alignment. To our knowledge, there is no better alternative to selectively remove information from language models to probe their impact on brain alignment. Using this approach, it is possible to investigate how the human brain processes language during both reading and listening at a finer scale than before. 

*[Vaidya et al. 2022] Self-supervised models of audio effectively explain human cortical responses to speech, ICML-2022*

*[Millet et al. 2022] Toward a realistic model of speech processing in the brain with self-supervised learning. NeurIPS-2022*