Paper: BrainSCUBA: Fine-Grained Natural Language Captions of Visual Cortex Selectivity (ICLR-2024)

Reviewer comment (Novelty):
Not much a weakness but more like a c**omment.** I think it seems common in this domain (e.g. neuron guided image generation), to pipe together several large-scale pre-trained multi-modal models with brain data and then train linear adaptors between them and then it will work. So not quite sure how technical challenging this work is comparing to previous ones.

Author response:
We appreciate the helpful and concrete suggestions by G8Co. We gladly welcome any questions and have provided our responses to your specific comments below.

> **Q1) Difference between our work and (linear) decoding**

We wanted to quickly summarize some conceptual differences between our work and the traditional decoding tasks -- which seek to reconstruct a stimulus from recorded brain activations. Recent decoding models indeed typically map from brain activation to a latent embedding using a linear model, and the result is coupled to a large image generative model. 

In contrast, our work seeks to understand the semantic selectivity of regions of the brain by generating natural language captions. Our work uses a linear probe which takes as input a CLIP image embedding and maps to voxel-wise brain activations. By constraining the encoding backbone to be unit-norm, the voxel-wise weight can be efficiently used to derive the optimal CLIP image embedding. 

A problem with this particular embedding is that is does not necessarily lie in the space of CLIP image embeddings for natural images. To that end, we propose an efficient parameter free method to project this embedding via a convex operation. 

Our method falls under the broad umbrella of work like [1,2,3,4,5,6]. These methods without exception rely on expensive gradient based optimization. An example includes BrainDiVE which requires **20~30 seconds per-sample**, compared to **2~3 seconds per-sample** for our method (including both caption and image generation) on the same hardware. This restricts these methods to analyzing broad swathes of the brain (typically ROIs on the order of hundreds to thousands of voxels).

Our gradient free approach to selectivity analysis differentiates our work from previous gradient based models. **This huge speed improvement can facilitate efficient data-driven exploration of the broader visual cortex**, and we demonstrate this in section 4.3 **Figure 8**, where we perform voxel-wise caption generation and tagging, backed up by additional human studies on ground truth top images (1600 responses in **Table 3** and 2560 responses in **Table S.3/S.4**).

[**1**] Inception loops discover what excites neurons most using deep predictive models (2019)

[**2**] Neural population control via deep image synthesis(2019)

[**3**] Computational models of category-selective brain regions enable high-throughput tests of selectivity (2021)

[**4**] NeuroGen: Activation optimized image synthesis for discovery neuroscience (2022)

[**5**] Brain Diffusion for Visual Exploration: Cortical Discovery using Large Scale Generative Models (2023)

[**6**] Energy Guided Diffusion for Generating Neurally Exciting Images (2023)

Reviewer comment (Theory):
In Eq. 4 why do you choose to use score to weighted average norm and direction separately, instead of averaging the vectors themselves? I can see arguments for both ways, but why do you choose this one?

Author response:
> **Q2) Decoupled norm and direction weighting**

This is an excellent question that highlights a subtle aspect of our proposed method.

Our goal in the projection step is to map the encoder weights to the domain of CLIP embedding of natural images. We decouple the norm and direction weighting because if we performed simultaneous weighted sum, the vectors could cancel out and end up with a different norm.

Consider the following scenario, where we have 2 natural images, and their respective vectors are $a=[0.5, -0.5]$ and $b=[0.5, +0.5]$, both of norm ~$0.707$. Consider the case where the weights on a and b are $w_a=0.5$ and $w_b=0.5$, in this case, the resulting vector if we do not decouple norm/direction is $[0.5, 0]$ with norm $0.5$.

Instead, if we perform decoupled norm and direction weighting, the result is $[0.5,0]/0.5 * 0.707 = [0.707,0]$, which would match the norm of the two input vectors.

We further illustrate this effect in **Figure S.19 on page 38 of the revision**, we plot a histogram of the norms of 1 million natural CLIP image embeddings, the norms of encoder weights projected without decoupled norm, and the norms of encoder weights projected with decoupled norm. This figure shows that decoupled projection yields a better aligned norm distribution.

We hope this clarifies our approach.

Reviewer comment (Novelty):
The scientific contribution and significance of this work are unclear. The paper didn't provide much new insights into the neuroscience findings.

Author response:
> **Q1) Neuroscience contributions**

Our paper presents four key experiments, detailed in **sections 4.2, 4.3, and 4.4**, along with supplemental results. The first two experiments confirm existing knowledge (**4.2 and 4.3**), while the third and fourth (**4.4** and **supplemental**) offer **new insights into the neuroscience of person representation in the brain.** **Following your suggestion, we have clarified this in the revision**.

There is no doubt that visual representations of the "person" class are extremely important in the human visual cortex, with representations of people emerging very early in infancy. In these two experiments, we seek to investigate the finer grained distribution of person representations in the brain.

Using voxel-wise caption generation and part-of-speech tagging, we distinguish between person-related and non-person-related nouns. Our regions of interest (ROIs) were identified using NSD functional localizer experiments with an independent stimulus set.

Experiment three visualizes the presence of the "person" class in different ROIs in **Figure 7** and **Table 2**, revealing a clear distinction between regions traditionally associated with person perception (like EBA and FFA) and those not (such as RSC/PPA/OPA). We also note increased selectivity in the word region, aligning with previous studies on the visual word form area (VWFA). Significantly, we find a high density of person representations in the precuneus visual area (PCV) and the temporoparietal junction (TPJ), as shown in **Figure 7** and quantitatively evaluated in **Table 2**, suggesting a role for these areas in processing people. The PCV has been implicated in first- versus third-person perspective judgements, while the TPJ has been implicated to play a role in theory of mind. **We believe we are the first to provide evidence for PCV and TPJ's role in person representations using a natural image viewing dataset, in contrast to experimenter-designed stimulus datasets.**

Experiment four focuses on the extrastriate body area (EBA). We identify two distinct clusters in EBA related to single and multiple person representations, which have not been reported in past work. **A human study with 1600 responses (Tables 3) and 5120 responses (Table S.3 and S.4) supports our computational findings, indicating that EBA-1 is more selective for social images, while EBA-2 favors single person images. We believe this is a novel neuroscientific insight that has not been observed previously.**

These findings, particularly regarding the EBA, offer novel insights and lay groundwork for future hypothesis-driven neuroscience research.

Reviewer comment (Evaluation):
The method of this paper is not sufficiently evaluated. There are multiple steps in the analysis, including training the encoding model, projecting weights, and transforming them into text or images, each of these steps (except encoding model accuracy) lacks a clear ground truth for comparison. Thus, we can hardly know how much the result deviated from the original voxel representation.

Author response:
> **Q2) Evaluation setup**

Thank you for you suggestion!

In the paper we perform the following validations:
* In the encoder training step, we evaluate the $R^2$ on a **held out test set** which offers evidence that our encoder has strong performance. This test set is the ~$1000$ images viewed by all subjects. We find that we can achieve high explained variance across the higher order visual areas comparable to other works [1] using the NSD dataset, reaching 0.75 $R^2$.
* In the projection step, we visualize the cosine similarity of the pre- and post-projection embeddings, and use this result to select the softmax temperature vector where cosine similarity plateaus in **Figure 2**. **In response to your suggestion, we have further added the standard deviation of cosine similarity in Figure 2 of the revision**

We would like to further clarify how we perform evaluation of our entire system end-to-end. Key to our evaluation is the use of ground truth region of interest (ROI) masks. These masks ***are not*** derived from the stimulus set (10000 images), and instead they are derived from a functional localizer task. This task uses images with the presence of a semantic category (faces, places, words, bodies) using the Stanford VPNL fLoc dataset. This yields ground truth ROIs.
We evaluate against these ROIs in a few ways:
1. UMAP dimensionality reduction shows that we recover the category selective regions. **In response to your suggestion, we have added visualization of the ground truth t-statistics for the fLoc experiments in Figure S.17 on page 36** This provides a further point of reference to compare **Figure 3** against.
2. We perform caption conditioned image synthesis for each ROI. Using CLIP for classification like BrainDiVE, we find our synthesized images have high semantic specificity matching the region's true semantic selectivity in **Table 1**.
3. (see next response)

Reviewer comment (Experiments):
How does the encoding weight vector $W_i$ change regarding to the model's training procedure? For example, if different splits of training and testing datasets are used, to what extent does the $W_i$ fluctuate? This is concerned because all the following analyses depend on a robust and accurate $W_i$. And for a voxel that has poor prediction accuracy (e.g., correlation=0.2), we don't know how well $W_i$ can be trusted as the representative embedding of that voxel.

Author response:
> **Q3) Stability of W***

**In response to your suggestion, we have added two plots evaluating the stability of encoder weights in 10-fold cross validation in Figure S.15 in page 34** and the semantic stability of post-projection encoder weights in **Figure S.16 in page 35**. We find that our encoder is very stable across different training sets and initializations.

Reviewer comment (Experiments):
The projection method is intuitive and smart, but there's no direct validation of its effectiveness. Is there a specific reason for using cosine similarity as the distance metric?

Author response:
> **Q4) Use of cosine similarity**

To clarify, cosine similarity (dot product) is the officially recommended [text-image retrieval metric (link to github)](https://github.com/openai/CLIP/blob/main/README.md?plain=1#L117) for CLIP [2]. This is because CLIP embeddings naturally lie on the surface of the sphere.

Reviewer comment (Evaluation):
The generated words in Fig. 4 mostly align with expectations. However, certain words, like "close" in the "Faces" and "Food" categories, are strange. Do the authors have an explanation for that?

Author response:
> **Q5) "close" in part-of-speech tagging**

This is due to the nature of the Spacy part-of-speech tagging engine, we checked the generated captions and "close" always occurs in the form of "A **close up** photo/picture of ...", where "close" is tagged as a noun. We believe this is reflective of how faces/food are depicted in our datasets.

Reviewer comment (Evaluation):
The findings in Fig.7 are interesting. However, I am concerned that the identified regions don't necessarily represent the concept of "people". A region that represents another semantic category (for example, "home") that often co-occurs with "people" words in natural language captions might also be highlighted with this method.

Author response:
> **Q6) Co-occurrence statistics**

We agree that identifying pure semantic selectivity is an important question. A significant advantage of our method compared to traditional functional localizer experiments is that our method processes naturalistic images, and does not need the images with a singular isolated concept. 

We perform a quantitative study of the density of "person" representation in different ROIs, and find that it is indeed much lower in voxels that do not code for person. Note for the word region has been demonstrated to exhibit person selectivity in prior work, in line with the higher person density shown in **Table 7**.

We also extensively validate our results using a human study on the ground truth top fMRI images. In addition to the two-alternative forced choice human study to pick among clusters (**Table 3**, 1600 responses total), we also perform a human study where each attribute is judged to be present or not in a group of images (**Table S.3, S.4**, 2560 responses total). We believe this is a robust evaluation as it directly evaluates attributes. We find the human study provides additional evidence for the computational results in **Figure 8**.

Reviewer comment (Presentation):
Minor: In Figure 3, color code used is not shown in the legend but is there in text in page 6. I recommend to add legend in the Figure also for clarity.

Author response:
We appreciate the suggestion! Indeed, we should provide additional context in the caption of Figure 3 using the color code.

To clarify, the color code is derived from data without explicit access to the region of interest (ROI) masks. The purpose of **Figure 3** is to show that our method can recover the semantic selectivity across the broader higher order visual regions by aligning the embedding of a brain encoder (which uses a contrastive vision-language CLIP image backbone) with text embeddings from a CLIP text encoder.

In **Figure 3**, the trace of ROIs are derived from an independent functional localizer task (stanford VPNL fLoc) provided in NSD for different semantic categories, including faces (FFA/aTL-faces)/places (RSC/OPA/PPA)/bodies (EBA).

**We have clarified the ROIs using the color codes in the revision following your suggestions.**

Reviewer comment (Experiments):
I believe this paper does not fully leverage the potential of BrainSCUBA. The captions generated are currently restricted to Nouns. Semantic selectivity using images is limiting as we have to find natural images that consist of only one concept without confounds. BrainSCUBA can allow a deeper investigation of social interaction through verbs, subject-object pairs and finding which regions are selective for specific interactions/emotions. I emphasize that I mentioned this in the weakness section as I would have loved to see more new results of semantic specificity (other than confirmations).

Author response:
We want to clarify that our system generates full natural language captions. These captions include nouns, adjectives, and subject/object interactions. We include examples of these captions in **Figure 1c**. 

Our purpose for focusing on noun based analyses in experiment 1 **(section 4.2)** and experiment 3 **(section 4.4 Table 7)** is primarily because nouns correspond to semantic categories, and enables a "sanity check" on the broader results given the broad consensus on category selectivity in higher order visual areas. 

Indeed our UMAP results, show that our methods can independently recover regions of food selectivity (yellow) corresponding to the food area (discovered in 2022 by three groups independently in [1,2,3]) in humans without using food specific stimulus.

We further analyze the occurrence frequency of multiple-people and a single person in Figure 8B, and perform human behavioral study in **Table 3 and Supplemental section A.6** to show that our method corresponds well with human perceptual judgements. **We believe this observation of disjoint semantic coding in the EBA region is a novel discovery.**

In response to your suggestion, **we have added additional plots of adjective frequency and example sentences in Figure S.14 on page 33 of the revision**, extracted from the BrainSCUBA generated captions using the Spacy part-of-speech engine. We hope this provides additional insight into the power of our method.

Reviewer comment (Experiments):
Is the semantic selectivity found here limited by training set (NSD)? Can we expect to see semantic specificity that is present in CLIP training data but not in NSD images?

Author response:
This is a very interesting question. NSD is the largest 7T fMRI visual dataset, with 10000 images shown to each of 8 subjects. However 10000 is probably small relative to human visual experience. 

In theory it is possible that the CLIP embeddings could reveal selectivity to concepts not present in the NSD stimulus set, even when the encoder is fit on only NSD images. 

Consider the case where a voxel is perfectly monotonic w.r.t. orthodromic distance in CLIP space, and that the CLIP embeddings of all natural images form a (geodesic) convex set. Given the monotonic condition, it is possible that an image on the boundary of this set is predicted to be highly activating, even when this image is not present in the stimulus set. However, the monotonicity assumption is very strong and not true in practice, as it basically assumes the human brain reacts to visual stimuli exactly the same as a CLIP neural network. The language model itself is also an area of potential improvement. So to summarize, while it is possible in theory, the assumptions needed are strong.

We believe collection of larger fMRI datasets and use of more powerful language models would help reveal additional selectivity.

Reviewer comment (Experiments):
What was the intuition behind the choice “A photo of a/an [NOUN]”. Did you consider other prompts?

Author response:
Our prompts used for Figure 3 follow the practice established by [1] (see section **3.1.4** of their paper), which popularized the use of this prompt format for zero-shot ImageNet classification. Recent work by CuPL [2] has established more sophisticated prompt formats which leverage GPT-3 augmentation. The original format is more commonly used, and we stuck with it in our work.

[**1**] Learning Transferable Visual Models From Natural Language Supervision

[**2**] What does a platypus look like? Generating customized prompts for zero-shot image classification

Reviewer comment (Novelty):
I don't believe that this submission is well-suited for a machine-learning-focused conference such as ICLR. It uses off-the-shelf, pretrained models to find information about visual cortex, which would be primarily of interest to neuroscientists and cognitive scientists. I cannot find substantial methodological advances here that would be of general interest to a conference aimed at machine learning researchers.

Author response:
We thank Reviewer aznk for the constructive review. We appreciate your recognition of the paper's technical soundness and potential interest to the fields of neuroscience and cognitive science - we address our paper's domain appropriateness for ICLR below. We also value your specific suggestions, which we have carefully considered in our responses below and in revisions to our paper.

> **Q1) Suitability for ICLR**

While we acknowledge your assessment regarding the suitability of our paper for ICLR, we argue that consistent with prior presentations at ICLR, BrainSCUBA contributes to the field by showcasing an *innovative application* of pre-trained vision-language models in field of neuroscience. 


Our paper was submitted under the **applications to neuroscience & cognitive science** primary area on OpenReview, one of the **primary areas officially recognized by ICLR 2024 in the [call for papers](https://iclr.cc/Conferences/2024/CallForPapers#:~:text=applications%20to%20neuroscience%20%26%20cognitive%20science)**. Since its inception in 2013, ICLR has included applications in neuroscience in the [topics of interest](https://iclr.cc/archive/2013/call-for-papers.html). The increasing prominence of computational neuroscience and cognitive science at ICLR is evidenced by a growing body of neuroscience papers published at ICLR ([see this anonymous list of ICLR 2020~2023 papers](https://github.com/anon-0518/ICLR_neuro_papers/tree/main)).


In 2023, the following applications in neuroscience paper appeared at ICLR as an "oral": Aligning Model and Macaque Inferior Temporal Cortex Representations Improves Model-to-Human Behavioral Alignment and Adversarial Robustness. It is important to note that this paper's primary contribution was in the field of computational neuroscience and, methodologically, relied on straightforward fine-tuning of a pre-trained model.



Key contributions of our paper include:
* By using an encoder trained on a large dataset, we recover a *less noisy* estimate of neural semantic alignment (see **Figure 5**). As the encoder is parameterized as a linear probe of a unit-norm embedding, we can derive the optimal CLIP embedding efficiently with a normalization operation, bypassing the slow gradient-based optimization used in BrainDiVE.
* We demonstrate that these embeddings diverge from natural CLIP image embeddings using UMAP. This motivates us to use a parameter-free method for aligning encoder weights with natural embeddings and generating voxel-specific captions. We show that our method can yield high categorical specificity, high predicted activations, and yield novel insights.
* Our work pioneers voxel-wise semantic explanations in the brain setting, moving beyond traditional region-based approaches like NeuroGen/BrainDiVE. We have adapted and applied vision-language models in a way that has not been done before, particularly in the context of understanding the human brain's visual cortex.

We do note with appreciation your willingness to reconsider based on broader reviewer feedback. As the other reviewers have not disagreed with our paper's suitability for ICLR, and two reviewers are positive about our work's contribution, **we hope you will reconsider the alignment of our work with ICLR's commitment to interdisciplinary research and the explicit call for papers on "applications to neuroscience & cognitive science"**.

Reviewer comment (Novelty):
My concerns are not about the soundness of the work–which is fine–but about the appropriateness for publication in a machine learning conference. I don't think that there is much extra work the authors could do to convince me otherwise. I'm open to re-evaluation of the paper's merits if the other reviewers deem it appropriate for this conference.

Author response:
We thank Reviewer aznk for the constructive review. We appreciate your recognition of the paper's technical soundness and potential interest to the fields of neuroscience and cognitive science - we address our paper's domain appropriateness for ICLR below. We also value your specific suggestions, which we have carefully considered in our responses below and in revisions to our paper.

> **Q1) Suitability for ICLR**

While we acknowledge your assessment regarding the suitability of our paper for ICLR, we argue that consistent with prior presentations at ICLR, BrainSCUBA contributes to the field by showcasing an *innovative application* of pre-trained vision-language models in field of neuroscience. 


Our paper was submitted under the **applications to neuroscience & cognitive science** primary area on OpenReview, one of the **primary areas officially recognized by ICLR 2024 in the [call for papers](https://iclr.cc/Conferences/2024/CallForPapers#:~:text=applications%20to%20neuroscience%20%26%20cognitive%20science)**. Since its inception in 2013, ICLR has included applications in neuroscience in the [topics of interest](https://iclr.cc/archive/2013/call-for-papers.html). The increasing prominence of computational neuroscience and cognitive science at ICLR is evidenced by a growing body of neuroscience papers published at ICLR ([see this anonymous list of ICLR 2020~2023 papers](https://github.com/anon-0518/ICLR_neuro_papers/tree/main)).


In 2023, the following applications in neuroscience paper appeared at ICLR as an "oral": Aligning Model and Macaque Inferior Temporal Cortex Representations Improves Model-to-Human Behavioral Alignment and Adversarial Robustness. It is important to note that this paper's primary contribution was in the field of computational neuroscience and, methodologically, relied on straightforward fine-tuning of a pre-trained model.



Key contributions of our paper include:
* By using an encoder trained on a large dataset, we recover a *less noisy* estimate of neural semantic alignment (see **Figure 5**). As the encoder is parameterized as a linear probe of a unit-norm embedding, we can derive the optimal CLIP embedding efficiently with a normalization operation, bypassing the slow gradient-based optimization used in BrainDiVE.
* We demonstrate that these embeddings diverge from natural CLIP image embeddings using UMAP. This motivates us to use a parameter-free method for aligning encoder weights with natural embeddings and generating voxel-specific captions. We show that our method can yield high categorical specificity, high predicted activations, and yield novel insights.
* Our work pioneers voxel-wise semantic explanations in the brain setting, moving beyond traditional region-based approaches like NeuroGen/BrainDiVE. We have adapted and applied vision-language models in a way that has not been done before, particularly in the context of understanding the human brain's visual cortex.

We do note with appreciation your willingness to reconsider based on broader reviewer feedback. As the other reviewers have not disagreed with our paper's suitability for ICLR, and two reviewers are positive about our work's contribution, **we hope you will reconsider the alignment of our work with ICLR's commitment to interdisciplinary research and the explicit call for papers on "applications to neuroscience & cognitive science"**.

Reviewer comment (Writing):
Page 2: acitvations -> activations.

Author response:
> **Q2) Typos**

Thank you, we have corrected the typos in the revision of our work.

Reviewer comment (Presentation):
Page 3-4: I found Figure 2b and its accompanying justification inscrutable. If the point is that there are no images close to unit sphere of captions, and hence blending (eq. 4) must be used to find something closer to the manifold of natural images, this does a poor job of conveying that, and text would be a better way of communicating that. If there is a different point they're trying to make, the authors should take a few sentences to explain what it is.

Author response:
> **Q3) Clarification of Figure 2b**

**Figure 2b** shows that even when both the encoder weights and CLIP Image embeddings are projected to the *same unit-sphere*, there still exists a gap between the two embeddings. This plot was specifically motivated by [1](NeurIPS 2022).

We aim to be consistent with prior work on identifying CLIP Image & CLIP Text embedding gaps, and use the same technique -- performing UMAP on two sets of embeddings simultaneously -- in showing a brain voxel embedding & CLIP Image embedding gap.

**Following your suggestion, we have further clarified our motivation in the revision text.**

[**1**] Mind the Gap: Understanding the Modality Gap in Multi-modal Contrastive Representation Learning (NeurIPS 2022)