Title: value: Alignment with human representations supports robust few-shot learning
Authors: value: Ilia Sucholutsky
Thomas L. Griffiths
Venue: NeurIPS 2023
Keywords: value: representation learning
supervised learning
human alignment
few-shot learning

Abstract: value: Should we care whether AI systems have representations of the world that are similar to those of humans? We provide an information-theoretic analysis that suggests that there should be a U-shaped relationship between the degree of representational alignment with humans and performance on few-shot learning tasks. We confirm this prediction empirically, finding such a relationship in an analysis of the performance of 491 computer vision models. We also show that highly-aligned models are more robust to both natural adversarial attacks and domain shifts. Our results suggest that human-alignment is often a sufficient, but not necessary, condition for models to make effective use of limited data, be robust, and generalize well.


Review discussion:
* The authors use existing ideas of triplets from cognitive science and combine information theory to show why human alignment may not always lead to better performance, especially for few-shot downstream learning. More importantly, the authors give an intuition and theoretical backing for why some models can have low alignment with humans and still perform well in downstream learning.
* The authors highlight the significance of ensuring human alignment of neural network representations by studying the role of alignment on two critical characteristics: adversarial robustness and domain shift robustness. They further show how human alignment plays a role in downstream learning, mainly when limited data is available.
* Using a significantly large number of models and large-scale existing human-annotated data, the authors empirically validated the theoretical claims made in the paper, showing the benefit of very high alignment with robustness while also revealing the U-shaped relationship between few-shot learning and alignment. The authors performed critical statistical testing to highlight the results.
* The interaction of architecture model choices and alignment is insightful and could be helpful for future work.
* The contribution seems limited, the main contribution seems to be the showing of the U-shaped relationship. The primary notion of triplets is derived from prior work.
* The importance of triplets seems limited. Pearson and Spearman also clearly provide similar takeaways. Why are triplets needed? Not clear from the evaluation section.
* Evaluation is also limited in some aspects, although the paper explores more than 400 models. Most transfer learning results are shown for CIFAR after ImageNet pre-training. The result on MNIST and FMNIST show quite weak results, meaning that strong domain shift during transfer learning makes the metric less impactful.
But transfer learning is usually most important in these settings (e.g., medical domain, face classification, etc.).
* There is a lack of clarity in the writing of the paper. Section 3 is difficult to follow, and it is not always clear how the different definitions flow, and how the pipeline works. E.g., not clear what is the significance of Def. 3.4, Discussion section mentions "quadruplets", but they are never mentioned in the paper. Not clear at all.
* Adversarial robustness is measured for real-world adversarial images. More common adversarial examples used in literature, e.g., $\ell_p$ ball perturbations are not explored.
* Evaluation section compares the triplets with pairwise measures like Pearson and Spearman correlations.
But it is not clear from Section 3 as to why it is interesting to compare triplets to pairwise. Do triplets give benefits over Pearson/Spearman? Are there any tradeoffs?
* Results shown in the tables are not well discussed
* Transfer learning results only analyzed when we have "head" networks. Not clear if the method works for full network fine-tuning or not, something that is often used in practice.
* No justification given on the choice of the heads, e.g., dropout rate, etc. Were they hyper parameter-tuned?
* Not clear what and how the ImageNet accuracy correction is performed. It seems important, so more intuition should be provided.
* Why does correlation with test accuracy reduce with more training examples (Fig. 3 and 4 right)?
* How are the classifier heads parameters chosen?
* What is meant by supervised learning in Section 3? Does it mean standard classification task? It is not clear.
* What are quadruplets mentioned in Discussion?
* Large datasets like ImageNet and CIFAR have a lot of "noisy" and misclassified examples on which the deep models are trained on. Do the different alignment metrics reveal any interesting facts on the model's alignment or non-alignment on these "outlier" examples?
* Most transfer learning results are shown for transfer from ImageNet to CIFAR, which is limiting.
* Exact contribution of triplets is not clear from the paper, as existing correlation metrics can already show the U-shaped relation and performance relations.
No ethics review needed.
Thank you for your valuable feedback! We are really glad you appreciated the scale of our experiments and the architectural insights. We are especially grateful for your question about “outlier” examples as this led to an exciting new insight detailed below.
**1: Contribution seems limited to U-shaped relationship.**
Thank you for bringing up this concern! Please see the 2nd paragraph in the general response to all reviewers for clarification on the motivation/contribution.
**2: Why are triplets needed?**
As mentioned, the theoretical aspects of this work are a key contribution. The information-theoretic argument we make in the main theorem leverages the fact that we can convert triplets into bits which is necessary to prove the link between representational alignment and downstream properties. Knowing this link exists, we can then study several ways to measure the representational alignment between two agents in practice.
For example, Pearson & Spearman similarity analysis is close to what neuroscientists use to compare brain data with DNN activations (one of the reasons why we submitted with ‘neuroscience and cognitive science’ as the primary area). As some reviewers mentioned, our findings being supported experimentally by both triplet analysis and more common methods provides strong confirmation for the theory. We have updated the Introduction to reflect this.
**3: Most transfer results shown for CIFAR. MNIST and FMNIST show weak results, meaning strong domain shift makes the metric less impactful.**
That strong domain-shift weakens the relationship between alignment and transfer learning is an excellent point and we see it as a key insight of the experimental results. A key question in representational learning is whether human-like representations are ‘good’, with conflicting findings in the literature on whether they lead to better performance. Our discovery of the U-shaped curve relieves this tension, but our experiments furthermore show that representational alignment is also susceptible to domain-shift (e.g., just because we are very aligned on animals we should not assume we are equally aligned on fruits) and thus if we want our models to learn human-like representations, we should ensure they are human-like globally. This result is similarly of interest to the AI alignment community as it suggests we should study whether other kinds of alignment are also susceptible to domain-shift. We have updated the Discussion to emphasize this insight from the (F)MNIST experiments.
**4: Lack of clarity: Def. 3.4,  "quadruplets", “supervised learning”**
Thank you for pointing this out! By supervised learning, we do mean classification. This is also the source of confusion in Def 3.4 since that defines the task setup, and we have updated it accordingly. We also added a definition of quadruplets to this section, by which we meant queries of the form “X is closer to Y than A is to B” ($|X-Y|<|A-B|$). With this definition in place, it is also much clearer why triplets form a subset of quadruplets.
**5: Adversarial robustness is measured for real-world adversarial images.**
We have updated the text to refer to robustness to “natural adversarial examples” as also suggested by some of the other reviewers.
**6: Results in tables are not well discussed.**
We have added additional discussion of both tables, including a clarification suggested by reviewer kVdE.
**7: Transfer learning results only with head networks. How are the classifier head parameters chosen?**
Fine-tuning all models would be prohibitively expensive. Recent work [1] has shown that linear probing usually performs on par with fine-tuning and is a more accurate measure of the quality of internal representations which fine-tuning distorts. The heads are provided in the supplement (FSL_exps.py) and fixed hyperparameter values (selected based on exploratory experiments to confirm stable convergence) were used for every dataset/model for consistency.
For example, as mentioned in section 4 paragraph “Few-shot learning”, all NN heads had dropout set to 0.8. We have updated the paragraph accordingly.
[1] Kumar, Ananya, et al. "Fine-tuning can distort pretrained features and underperform out-of-distribution" arXiv:2202.10054 (2022)
**8: Not clear what and how the ImageNet accuracy correction is performed.**
As described in section 4 paragraph “Correcting correlation for ImageNet performance”, we computed partial correlation with ImageNet accuracy as a covariate. Partial correlation is a popular statistical technique for measuring how associated two random variables are after removing the effect of confounding covariates. We have updated the paragraph.
**9: Why does correlation reduce with more training examples (Fig. 3 and 4 right)?**
Information theory predicts exactly this kind of effect since passing additional redundant bits over a noisy channel (i.e. more examples per class) increases the probability that all information is successfully recovered. We have added this insight to the Discussion.
**10: What do alignment metrics reveal on "outlier” examples?**
Based on your suggestion, we compared performance on the original ‘noisy’ ImageNet and relabeled ImageNet-ReaL [2]. All models had better performance on ReaL, but the improvement has a weak inverted-U relationship with alignment. This may seem surprising, but the ReaL dataset was collected by relabelling images on which models (pre-2020) disagreed with the original labels. Models with low performance by today’s standards (and alignment near 0.5) were used to select which images to relabel, so it is unsurprising that models with alignment near 0.5 benefit most from this relabeling. This is exciting as it suggests that ImageNet labels need to be revisited once again as ReaL may be biased! We have added this to the Results and Discussion. Thank you for the suggestion!
[2] Beyer, Lucas, et al. "Are we done with ImageNet?" arXiv:2006.07159 (2020)
I thank the authors for the detailed rebuttal and comments addressing all the reviewers' concerns. The authors have clarified the doubts and addressed my concerns adequately.
Hence I improve my prior rating and suggest acceptance of the paper.
The work is interesting, potentially showing the reason behind some prior conflicting results, and opens the avenue to further research in this direction. I especially find the results on the ImageNet-ReaL dataset fascinating and show a need to revisit ImageNet to study the representations and their alignment.
- Alignment between humans and ML models has been studied considerably from the perspective of interpretability and safety. Understanding how and whether alignment can improve sample-efficiency and robustness is a less explored, but important research question which this work makes progress towards.
- The paper theoretically and empirically shows that alignment is not monotonic with respect to robustness/sample efficiency which may explain why previous works have found conflicting results regarding the benefits of alignments. I think this finding would be useful to the community.
Also the information-theoretic framework proposed by the authors for analyzing representational alignment would likely be useful to future work.
- The experiments are thorough and adequately support the claims.
- The paper is well organized and the tables and plots clearly illustrate the main empirical findings.
Other works [1][2] have shown that the validation accuracy on an in-distribution (ID) dataset is highly correlated with accuracy on out-of-distribution (OOD) and downstream datasets. For reference, the R-squared value for pretrained models evaluated on ImageNet and ImageNetV2 is .996 and other distribution shifts have similar correlation. Given that in-distribution validation accuracy is almost entirely predictive of out-of-distribution accuracy, I think the current motivation, which is to show that alignment is predictive of downstream performance, is weak. Representational alignment as a predictive metric for robustness and downstream transfer performance is less accurate than standard ID validation accuracy.
Also obtaining validation accuracy is easier since it only requires one-hot labeling, as opposed to similarity matrices.
If the main motivation of the paper is to show that improving alignment will confer benefits such as robustness and few-shot performance, then it should show that alignment measures something more than in-distribution validation accuracy.
Otherwise, robustness and few-shot performance could simply be increased by improving the model on the validation set.
Overall I think the paper is interesting and original. My main concern is for the motivation, but would be happy to change my rating if this concern is adequately addressed.
[1] Accuracy on the Line: On the Strong Correlation Between Out-of-Distribution and In-Distribution Generalization
[2] Do ImageNet Classifiers Generalize to ImageNet?
- It would be helpful to see the distribution of alignment metrics and some analysis on what factors other than validation accuracy affect the alignment metric. Given that validation accuracy is almost entirely predictive of robustness and downstream performance (greater than .9 R-squared), it seems that validation accuracy and alignment would be highly correlated.
- In the abstract it’s stated that highly-aligned models are more robust to adversarial attacks. This claim seems overly strong as the experiments are conducted with natural adversarial images, and not with white or black box attacks.
- Given theorem 3.9, would the relationship between alignment and few-shot performance be v-shaped and not u-shaped?
The authors adequately address the limitations of the method.
No ethics review needed.
Thank you for your valuable feedback! We’re very glad to hear that the key result and information-theoretic framework will be useful for the community!
**Comment 1: Other works [1][2] have shown that the validation accuracy on an in-distribution (ID) dataset is highly correlated with accuracy on out-of-distribution (OOD) and downstream datasets. For reference, the R-squared value for pretrained models evaluated on ImageNet and ImageNetV2 is .996 and other distribution shifts have similar correlation. Given that in-distribution validation accuracy is almost entirely predictive of out-of-distribution accuracy, I think the current motivation, which is to show that alignment is predictive of downstream performance, is weak. Representational alignment as a predictive metric for robustness and downstream transfer performance is less accurate than standard ID validation accuracy.
Also obtaining validation accuracy is easier since it only requires one-hot labeling, as opposed to similarity matrices.
If the main motivation of the paper is to show that improving alignment will confer benefits such as robustness and few-shot performance, then it should show that alignment measures something more than in-distribution validation accuracy.
Otherwise, robustness and few-shot performance could simply be increased by improving the model on the validation set.
Overall I think the paper is interesting and original. My main concern is for the motivation, but would be happy to change my rating if this concern is adequately addressed.**
**Response**: We are very glad you brought up this concern as it’s very important to us that we correctly communicate the motivation for this study. In representation learning research, two of the major goals are 1) to figure out what makes a representation “good” and 2) to figure out how to learn these “good” representations from data.
However, as we mention in the paper, there is much disagreement in the literature, and even diverging experimental results, about whether human-like representations are actually “good”.
Meanwhile, for alignment researchers, it is important to know what the downstream effects are of aligning models with humans and whether this affects model behavior in positive (or negative) ways.
Finally, cognitive scientists and neuroscientists often study representational similarity between humans and other humans as well as between humans and models to try to understand human cognition and communication. We believe our results and framework are important for all three of these communities. We definitely do not see our main motivation as simply being a way to improve or predict few-shot learning performance, for which we agree there are other, better metrics.
Rather, our goals are  1) establishing a clear link between alignment and downstream properties, 2) explaining/resolving previous disagreement about the value of human-like representations and 3) providing these three communities with a framework they can all use in future studies. This is also why we submitted the paper with cognitive science as the primary area rather than supervised learning or computer vision. We have now updated the introduction and the discussion to clarify these key motivations.
**Comment 2: It would be helpful to see the distribution of alignment metrics and some analysis on what factors other than validation accuracy affect the alignment metric. Given that validation accuracy is almost entirely predictive of robustness and downstream performance (greater than .9 R-squared), it seems that validation accuracy and alignment would be highly correlated.**
**Response**: We include a scatterplot in the rebuttal pdf of Pearson alignment plotted against ImageNet validation accuracy and find a slight U-shaped curve and a weak positive correlation ($\rho=0.235$). To clarify, we believe that the causal link goes the other way: alignment affects validation accuracy, robustness, and few-shot learning performance.
We also note the following correlations between ImageNet validation accuracy and other properties which show that validation accuracy does not explain all the variance in downstream properties:
- ImageNet-A: 0.81
- ImageNet-S: 0.86
- ImageNet-R: 0.82
- CIFAR10 1-shot learning: 0.68
- FMNIST 1-shot learning: 0.10
- MNIST 1-shot learning: 0.07
We have now included this additional figure and insights in the Results section.
**Comment 3: In the abstract it’s stated that highly-aligned models are more robust to adversarial attacks. This claim seems overly strong as the experiments are conducted with natural adversarial images, and not with white or black box attacks.**
**Response**: We have updated the abstract and main text to instead refer to robustness to natural adversarial examples.
**Comment 4: Given theorem 3.9, would the relationship between alignment and few-shot performance be v-shaped and not u-shaped?**
**Response**: Thank you for bringing this up! For binary symmetric channels, the channel capacity is equal to $1-H(p)$ where $H(p):=-p\log_2(p)-(1-p)\log_2(1-p)$ is the binary entropy function and $p$ is the probability of a bit flip.
As a result, capacity (and hence the relationship between alignment and few-shot performance) is U-shaped. We have added a short clarification about this to the theory section.
I thank the authors for their response. My concerns have been addressed, in particular the motivation for the work and the correlation between validation accuracy and robustness. I have raised my score accordingly.
This paper has a large number of strengths. It is well-written and well-presented. The theory is novel and valuable, providing insights for future researchers and developers. The experiments are exhaustive and well-motivated. The depth of discussion around justifying the choices in the experiments is excellent. The experiments also largely follow the theory, which suggests useful takeaways to a broad audience.
This is a strong paper, but there's a couple of weaknesses which dampen my enthusiasm somewhat.
The largest of these weaknesses is that while the authors repeatedly refer to a U-shaped effect, the results actually suggest a J-shaped effect. The low alignment models consistently demonstrate a significantly worse performance than the high-alignment models Despite this, there's no discussion of this point throughout the paper. The reasoning seems intuitively straightforward, despite note following from the theory.
While it is true that a model could "realize" to interpret signals in an "opposite" manner (to use the paper's term) with low alignment, this realization is still extra effort. In a model trained on backprop, it is not always possible to "flip" the use of features in a large, multidimensional space. This or similar discussion accounting for this issue should be brought up in the paper.
Second, while a large number of experiments are run, the choice of only a top 5, bottom 5, and mean 5 is somewhat odd. This represents less than 3% of the models used in the experiments. This is understandable in some cases, like presenting individual values in tables.
But in Figures like Figure 3 a more continual effect could have been demonstrated with a typical standard deviation off the lines. If the authors chose to only use 15 models, some experiment helping to demonstrate that the results weren't a fluke would be beneficial.
For example, ranking the 491 models in term of alignment and visualizing some performance metric across all 491. As it is, readers need to look through a dense CSV file for this information. There's notably no readMe or similar to explain the labels for the CSV file making it difficult to understand what's going on here without digging through code. Some clarity on this would be appreciated and would benefit the paper.
1. How do the authors account for the effect being more J-shaped than U-shaped as predicted?
2. Why did the authors choose to only look at high-5/meanest-5/lowest-5 models?
The authors identify the issues around the choice of alignment, otherwise there are no major potential negative societal issues.
No ethics review needed.
Thank you for your valuable feedback! We’re grateful that you enjoyed the paper and expressed such enthusiasm for the theory and experiments!
**Comment 1: The largest of these weaknesses is that while the authors repeatedly refer to a U-shaped effect, the results actually suggest a J-shaped effect. The low alignment models consistently demonstrate a significantly worse performance than the high-alignment models Despite this, there's no discussion of this point throughout the paper. The reasoning seems intuitively straightforward, despite note following from the theory.
While it is true that a model could "realize" to interpret signals in an "opposite" manner (to use the paper's term) with low alignment, this realization is still extra effort. In a model trained on backprop, it is not always possible to "flip" the use of features in a large, multidimensional space. This or similar discussion accounting for this issue should be brought up in the paper. (Question 1: How do the authors account for the effect being more J-shaped than U-shaped as predicted?)**
**Response**: This is a great point and the asymmetry of the empirical U is something we’ve been thinking about a lot since writing the paper! As you can see in Figure 4 (left), the left arm of the U does indeed extend as high as the right arm with a few models having alignment at around 20 but few-shot learning performance at around 60. Those models have the following architectures: 	beit_base_patch16_384 & beit_base_patch16_224. Both of these models also maintain their top-left positions when evaluating alignment against performance on ImageNet-A, -R, & -S and one-shot performance on MNIST and FMNIST.
To check whether this is a general property of BEiT models [1], we tried out 3 additional architectures from the Pytorch Image Models zoo: beit_large_patch16_512, beit_large_patch16_384, beit_large_patch16_224 and find that they also land in the top-left corner. It appears that bi-directional pre-training of image models leads to very non-human-like but effective representations.
However, the left arm of the U is indeed still much sparser than the right arm. This suggests that, while it is clearly possible for models to have very low alignment, and some indeed do, generic computer vision training tends to produce more aligned models. This supports the findings from Moschella et al. (2022), who find that a very large swath of computer-vision models trained on the same data have similarly structured relative representations. We have added this discussion and additional results to the Results and Discussion sections.
[1] Bao, Hangbo, Li Dong, Songhao Piao, and Furu Wei. "Beit: Bert pre-training of image transformers." arXiv preprint arXiv:2106.08254 (2021).
[2] Moschella, Luca, et al. "Relative representations enable zero-shot latent space communication." The Eleventh International Conference on Learning Representations. 2022.
**Comment 2: Second, while a large number of experiments are run, the choice of only a top 5, bottom 5, and mean 5 is somewhat odd. This represents less than 3% of the models used in the experiments. This is understandable in some cases, like presenting individual values in tables.
But in Figures like Figure 3 a more continual effect could have been demonstrated with a typical standard deviation off the lines. If the authors chose to only use 15 models, some experiment helping to demonstrate that the results weren't a fluke would be beneficial.
For example, ranking the 491 models in term of alignment and visualizing some performance metric across all 491. As it is, readers need to look through a dense CSV file for this information. There's notably no readMe or similar to explain the labels for the CSV file making it difficult to understand what's going on here without digging through code. Some clarity on this would be appreciated and would benefit the paper. (Question 2: Why did the authors choose to only look at high-5/meanest-5/lowest-5 models?)**
**Response**: Thank you for bringing this up! We initially used the 5-5-5 format for Figure 3 (left) and Table 1 because we thought it would be a simpler, more intuitive format for readers to visualize and interpret the data.
However, the key results and claims are based on the analysis of the **full set of models** as can be seen in **Figure 3 (right), Figure 4, and Table 2** of the paper.
For example, Figure 4 (left) directly compares one-shot learning performance with alignment as you suggest. We have now updated the captions and in-text descriptions to clarify that the results are based on all models rather than the subset of 15 and have added additional figures like Figure 4 that visualize other downstream properties for all 491 models to the appendix.
Additionally, we apologize for the dense supplemental information. We have prepped a README file that describes the columns of full_results.csv and have added additional comments in the associated iPython notebook that explains how to use the notebook to analyze the results file and reproduce all the figures in the paper as well as additional figures that compare various performance metrics against our three alignment metrics.
Thanks to the authors for their comprehensive response!
C1: This is an excellent explanation and definitely fits better than my rough intuition. I think the inclusion of it greatly improves the paper.
C2: I apologize for misunderstanding those later figures and tables! I appreciate the clarification.
Based on the improved discussion and my improved understanding of the work I have upgraded my score to an Accept.
- The U-shaped results are novel and interesting phenomenon. With both information theoretical analysis and detailed empirical analysis, the results seem credible, and provide new perspective on the previously conflicting findings as to whether improving alignment increases performance.
- They study three different alignment metrics.
- The degree of alignment not only explains the few-shot performance, but also predicts robustness to distributional shift (e.g. ImageNet-A, -R, -S) which intuitively make sense.
- Minor:
- I believe ImageNet-A is just a subset of ImageNet-test set that happens to be difficult, but the term "adversarial robustness" often refers to images with actual adversarial noise, so I think it should be clarified.
- Figure 4 Left: Curious to see if there is any characteristics of models that are in the top left of the graph (the ones with test accuracy around 60% and alignment being less than 20), as these points seem to be rather outliers.
- The analysis seems to be limited to ImageNet-based models. Recently there are more capable vision-language models such as BLIP-2, which combines a vision model with Large Language Model.
While the current paper conducts detailed analysis, it would be a more interesting work if they also study these recent vision-language models.
No ethics review needed.
Thank you for your valuable feedback! We’re really glad that you appreciated the U-shaped results and both the theory and experiments that support them!
**Comment 1: I believe ImageNet-A is just a subset of ImageNet-test set that happens to be difficult, but the term "adversarial robustness" often refers to images with actual adversarial noise, so I think it should be clarified.**
**Response**: Thanks for pointing this out! ImageNet-A (Hendrycks et al., 2021) is actually **not** a subset of ImageNet-test but was rather new images collected from scratch from websites like iNaturalist and Flickr that match 200 of the classes in ImageNet-1k.
However, unlike typical adversarial examples (which are modifications of existing images), these are so-called “natural adversarial examples” that are real images that fool the vast majority of image classifiers. We have updated the introduction, experiments, and discussion sections to clarify these two points and now instead use the term “natural adversarial examples” as suggested by the ImageNet-A authors.
**Comment 2: Figure 4 Left: Curious to see if there is any characteristics of models that are in the top left of the graph (the ones with test accuracy around 60% and alignment being less than 20), as these points seem to be rather outliers.**
**Response**: Great question! Those models have the following architectures: 	beit_base_patch16_384 & beit_base_patch16_224. Both of these models also maintain their top-left positions when evaluating performance on ImageNet-A, -R, & -S and one-shot performance on MNIST and FMNIST. To check whether this is a general property of BEiT models [1], we tried out 3 additional architectures from the Pytorch Image Models zoo: beit_large_patch16_512, beit_large_patch16_384, beit_large_patch16_224 and find that they also land in the top-left corner. It appears that bi-directional pre-training of image models leads to very non-human-like but effective representations. We have updated the architecture discussion section to include this insight. Thank you for the suggestion!
[1] Bao, Hangbo, Li Dong, Songhao Piao, and Furu Wei. "Beit: Bert pre-training of image transformers." arXiv preprint arXiv:2106.08254 (2021).
**Comment 3: The analysis seems to be limited to ImageNet-based models. Recently there are more capable vision-language models such as BLIP-2, which combines a vision model with Large Language Model.
While the current paper conducts detailed analysis, it would be a more interesting work if they also study these recent vision-language models.**
**Response**:
We are currently running additional experiments with the new models that have been added to the Pytorch Image Models (timm) package in the months that have passed since we conducted the experiments with the 491 models currently listed in the paper, including vision-language models.  Unfortunately, this is pretty time-consuming since our GPU access is limited and the additional experiments involve running 600+ additional large models. We will update the camera-ready version of the paper once the new experiments finish running.
Additionally, we note that we included all our code in the supplemental information so that readers can reproduce our experiments with new or custom models.
Thank you for pointing out my misunderstanding / clarifying the figure.
The authors have addressed my questions and suggestions. I think this paper will be a great contribution to the community. I keep my score (7: Accept).
**We thank all the reviewers for their valuable feedback! We are encouraged that the reviewers found the paper to be “well-written”, “well-presented”, and “well-organized”; the theory to be “novel”, “interesting”, “valuable”, and “useful” for the community; and the experiments to be “detailed”, “exhaustive”, “well-motivated”, “thorough”, and “insightful”. The constructive feedback from the reviewers generally consisted of 1) clarity suggestions; and 2) insight/analysis suggestions. We have implemented all suggestions provided by the reviewers and responded with details of those changes to each reviewer individually. We are grateful for the suggestions as not only did they help us improve clarity but they also 1) helped us distill the motivation into a much stronger, more concise statement; 2) made our theory section smoother and more accessible to a broader audience; and 3) resulted in exciting new insights from the experimental results.**
Since two reviewers asked about the motivation, we provide a shared response here. In representation learning research, two of the major goals are 1) to figure out what makes a representation “good” and 2) to figure out how to learn these “good” representations from data.
However, as we mention in the paper, there is much disagreement in the literature, and even diverging experimental results, about whether human-like representations are actually “good”.
Meanwhile, for alignment researchers, it is important to know what the downstream effects are of aligning models with humans and whether this affects behavior in positive (or negative) ways.
Finally, cognitive scientists and neuroscientists often study representational similarity between humans & other humans as well as between humans & models to try to understand human cognition and communication. We believe our results and framework are important for all three of these communities. We definitely do not see our main contribution as simply being able to predict few-shot learning performance based on analysis of triplets, but rather as 1) establishing a clear link between alignment and downstream properties, 2) explaining/resolving previous disagreement about the value of human-like representations in the literature, and 3) providing these three communities with a novel information-theoretic framework they can all use in future studies. This is also why we submitted the paper with cognitive science as the primary area rather than supervised learning or computer vision. We have now updated the introduction and the discussion to clarify these key motivations and contributions.
Finally, we note that the figure in the attached PDF is intended for **Reviewer wjPt**.
**Please let us know if you have any more questions or suggestions!**

Meta-review:
role: AC
data: comment: value: Today begins the Author-Reviewer Discussion phase, which lasts 1 week (**Aug 10-16**).

I ask the reviewers to please **carefully read all other reviews and the author responses
and (if appropriate) respond to author responses promptly.**   If you've read the author response, please take the time to leave a comment, even if you have nothing to add. 

I also encourage both authors and reviewers to monitor OpenReview for further comments in order to enable as much back-and-forth as possible during this short period.


As the only reviewer recommending rejection, I especially encourage Reviewer 225s to engage during this period.
role: PC
data: comment: value: This work provides significant empirical and theoretical contributions which help support the intuition that representations more aligned with humans’ lead to better (e.g. more robust) behavior.  This is an important finding for science and AI (preference) alignment, as this intuition has often been assumed without support.  The experiments demonstrate that this is in fact the case in computer vision in the context of robustness to (natural) adversarial robustness and domain shift (for few shot learning, the result is more subtle; moderately aligned models are the worst, in line with the authors’ theoretical results).  Reviewers found their concerns were adequately addressed in rebuttals and all favor acceptance and I am happy to concur.

Decision: Accept (spotlight)