MindSet: Vision. A toolbox for testing DNNs on key psychological experiments

Authors: Valerio Biscione, Dong Yin, Gaurav Malhotra, Marin Dujmovic, Milton L. Montero, Guillermo Puebla, Federico Adolfi, Rachel F Heaton, John E. Hummel, Benjamin D. Evans, Karim G. Habashy, Jeffrey Bowers
Primary area: datasets and benchmarks
Keywords: Datasets, Visual Perception, Psychology, Human Vision, Deep Neural Networks, Visual Illusion

Abstract:
Multiple benchmarks have been developed to assess the alignment between deep neural networks (DNNs) and human vision. In almost all cases these benchmarks are observational in the sense they are composed of behavioural and brain responses to naturalistic images that have not been manipulated to test hypotheses regarding how DNNs or humans perceive and identify objects. Here we introduce the toolbox MindSet: Vision, consisting of a collection of image datasets and related scripts designed to test DNNs on 30 psychological findings. In all experimental conditions, the stimuli are systematically manipulated to test specific hypotheses regarding human visual perception and object recognition. In addition to providing pre-generated datasets of images, we provide code to regenerate these datasets, offering many configurable parameters which greatly extend the dataset versatility for different research contexts, and code to facilitate the testing of DNNs on these image datasets using three different methods (similarity judgments, out-of-distribution classification, and decoder method), accessible via GitHub. We test ResNet-152 on each of these methods as an example of how the toolbox can be used.

Decision: reject
Reviewer scores: 3, 8, 3, 3, 8

Reviews and discussion:
<thread>
creation date:
1729874380280

thread_type:
official_review

<thread_object>
creator: reviewer_vwbg

<content>
summary:
This paper introduces a toolbox for creating 30 static visual tasks to test specific hypotheses about the capabilities of DNNs in comparison to the human visual system, for example how a DNN performs when subjected to crowding. Like the well-established Brain-Score benchmark, this toolbox provides a collection of visual tasks on which any DNN could be evaluated. Unlike Brain-Score, the authors propose to not simply evaluate models on observational data and aggregate performance to obtain a ranking, but to explicitly test specific hypotheses about the abilities of the models, which should be simplified by the proposed toolbox.

strengths:
The paper is well-written and technically sound, as is the code. The proposed tasks are all sensible (and could be extended to include new tasks, should the need arise) and reflect the field well, so the toolbox could be of great practical utility. Because the toolbox makes evaluating models on these different hypothesis tests easy, there is a good chance that it will indeed be used to evaluate new models, which would be beneficial for the community. The core point that the authors want to raise (about testing specific hypotheses, instead of predicting observational data) is also valid and meaningfully adds to the discourse about human-DNN alignment.

weaknesses:
The main weakness of the paper is the lack of experiments and results. The authors only present the toolbox and superficially evaluate a ResNet-152, but they do not derive any new findings or concretely outline how this could be done, which would greatly increase the perceived utility of the toolbox. Another issue is the lack of human reference data: While it is generally known that humans are e.g. sensitive to crowding, the toolbox is missing human reference performance for the default set of stimuli that the authors provide. However, such data could over time be collected and contributed by the community.

questions:
The work presented here is done well, and the decision to accept or reject ultimately comes down to whether one believes that the implementation of this toolbox is a sufficient contribution.

I propose to accept the paper, because I can imagine the practical value in having such a standardized toolbox. It will at least save the community some work, and if standardized procedures for generating these stimuli emerge, results could become more directly comparable. Furthermore, the authors argue for an alternative paradigm in how to evaluate human-machine alignment, which I deem a valuable intellectual contribution, even though this point remains a bit vague. Elaborating more on what this would entail could improve the paper. Overall, the paper does feel a bit thin, lacking both evaluations of a relevant suite of models and human reference data. While the latter might be too much to ask, evaluating a few models (e.g. models scoring high on Brain-Score or human-vs-machine) would have been interesting and justified the submission to ICLR better.

As a general point of discussion: In principle, these tasks could be integrated into Brain-Score, if there was human data to compare models to. While I understand that the authors are (for fair reasons) skeptical of the Brain-Score methodology of averaging performance, it would still be valuable to at least reflect this dimension of human-DNN-(mis)alignment on Brain-Score as well, even if it just highlights how none of the models that do well on predicting neural data show human-like performance on these tasks.


### Nitpicks
* in line 49, there seems to be a broken citation
* in line 108, there should be a space: "to understand"
* in the README of the repository, the link to ImageNet classification is broken (because it should be imagenet_ood_classification)
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<thread>
creation date:
1730452148879

thread_type:
official_review

<thread_object>
creator: reviewer_egun

<content>
summary:
This work provides Python code to generate controllable visual stimulus sets from 30 well-known cognitive psychology experiments. The underlying motivation is to provide a hypothesis-driven approach for comparing neural networks to human vision, as an alternative to benchmarking with natural image datasets and associated behavioral or neural responses (e.g., BrainScore). The work also introduces three alternative methods for evaluating model predictions, accompanied by example Python implementations.

strengths:
1. Bridging cognitive psychology and neural network research is a laudable goal. Curating experimental results and making them accessible to the neural network research community is an effective way to achieve this and clearly establishes the motivation for this contribution.

2. The collection of stimulus sets is extensive and diverse.

3. Based on my review, the codebase appears well-designed, well-organized, and well-documented. The paper itself is also clearly written.

weaknesses:
1) The paper describes "datasets"; however, it contains no actual, complete data—only stimuli and stimulus generation scripts. In behavioral research, data typically includes, at a minimum, temporally ordered pairs of stimuli and empirical human responses. Generating stimuli is only the first step.

   A major hurdle in comparing human behavior with neural network models is obtaining high-quality human behavioral data collected in controlled settings. This hurdle is particularly challenging for researchers from engineering departments, where institutional infrastructure and practical know-how for conducting in-person human experiments are often lacking. Since data sharing is relatively new in experimental psychology, many classical results are available only as aggregate data (e.g., bar charts or ANOVA outcomes) rather than as recordings of human responses at the single-trial, single-participant level. Such raw responses may be essential for testing new hypotheses and evaluating statistical significance and reliability.

   Currently, the lack of associated behavioral data severely limits the potential impact of this contribution. For comparison, consider the works by Robert Geirhos et al. on robustness to transformations. These works have been impactful not only for their research questions but also for providing large-scale human response data as a community resource.

2. The theoretical contextualization of this work is relatively narrow and one-sided. The authors motivate their approach by referencing the 2022 BBS target paper by Bowers et al., reiterating some of its arguments (e.g., "...competing on observational datasets that do not
support any conclusions regarding the mechanistic similarity of DNNs and brains"). However, the current paper does not substantially engage with any of the counter-arguments presented in the 29 responses to the Bowers et al. paper. Scientific debate is essential, but it can only be productive when effort is made to consider opposing views.

questions:
1. In my opinion, for this work to be complete and ready for publication, each task should be accompanied by human data, either collected by the authors or curated from open data repositories. This data should be provided in both raw format and aggregated by condition. If the effects are strong and unequivocal, obtaining the relevant data should be straightforward. If the interpretation of the experimental outcomes is more complicated, the availability of detailed behavioral data becomes even more important. I believe that such a substantial revision would make this work highly impactful for the field.

2. A complete description of each experiment should also include experiment-specific instructions for both experimenters and participants, as well as the expected results according to alternative hypotheses. An experiment in psychology is more than just a set of stimuli.

3. Will training a neural network directly to imitate human performance in these tasks produce a model that the authors consider valid? If not, it may be worthwhile to specify the appropriate training distribution for each task.

4. In this context, it might be worthwhile for the authors to discuss the recent paper by Marcel Binz et al., "Centaur: A Foundation Model of Human Cognition," which appears to take a direct data-fitting approach.

Minor: There are multiple issues in the bibliography, including duplicated entries and papers already published in conferences cited as arXiv preprints.
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</thread>
<thread>
creation date:
1730691049016

thread_type:
official_review

<thread_object>
creator: reviewer_yu8u

<content>
summary:
This is a very interesting and well timed paper that introduced a dataset full of interesting psychological visual phenomena to test and compare humans and machines in a more precise and well-rounded manner than just accuracy/performance on a classification dataset (which is classical in computer vision & machine learning). The paper is extremely well written and articulated the need for a dataset like this one, and the authors do a spectacular job in citing all the relevant literature in this new type of "NeuroAI" wave where humans and DNN's are being compared. The only main weakness I see is that I wish there were more evaluations, or at least one solid one which is why I have reserved my vote for a marginally above threshold. Nevertheless, I do think that all in all this paper should be accepted provided a more extensive evaluation of at least one Neural Network with more detail -- that I did not see in the Appendix (although the Appendix does do a great job in covering all the vision science phenomena and illusions that are to be studied/tested by humans and machines).

----------

**Upgrade*

I have downgraded by score to 3 after reading all the reviews from different reviewers and I think the consesus holds that even though the papers intention is very insightful and unique, the lack of experiments and empirical validation for human-machine visual alignment undersells the true potential value of this paper. I think adding those basic experiments (only even 1 to 3 architectures) can put it at a high Oral-worthy position for a future CV/ML conference.

strengths:
* The paper is extremely well written. Despite having a background at the intersection of human and machine vision as a reviewer and being quite familiar with nearly all the works cited, the authors have done a great job in articulating why this dataset is important **for machine vision & computer vision** hence the proper fit for ICLR.
* The paper covers a lot of great literature at the intersection of machine vision and human vision, in addition to papers that try to link both phenomena. I think this paper could serve as a great "reference" to read for many early graduate students who would like to learn more about the topic that the authors are exploring and justifying its importance.
* The rigor in which the paper all 30 psychological findings in the main body and appendix is superb.

weaknesses:
The greatest weakness I see in this paper is that there is little to no evaluations. There is barely only two figures to evaluate the ResNet-152, and the paper leaves me with a bittersweet feeling of wanting to know how well did the ResNet-152 do compared to the average human on the collection of all the images in the toolbox.

questions:
Is there anyway we can get a figure of how the ResNet-152 does for all 30 psychological findings, as if we could get a row of 1 model entry as if this was Brain-Score? I think this would really drive the paper home and come full circle so that readers from computer vision and machine learning who do not know anything about neuroscience (or assume that machines see just like humans) -- can be surprised and find this paper even more valuable.
</content>
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</thread>
<thread>
creation date:
1730722085300

thread_type:
official_review

<thread_object>
creator: reviewer_poc1

<content>
summary:
The paper introduces MindSet a toolbox for testing DNNs on more than 30 psychological findings.  Including  test for low-level vision,  and high level vision such as visual illusions  and  shape and object recognition. This toolbox, will help authors test on a controlled environment how brain-like are these.

strengths:
I really appreciate the effort that went into creating this toolbox. I think it could greatly benefit the community, especially since many recent tests have focused increasingly on performance in neural alignment, often overlooking functional alignment beyond accuracy alone. The presentation is well-executed, and I particularly enjoyed the failure modes discussed in the supplementary material. My only suggestion would be to consider moving some of these examples into the main text to further highlight the value of this toolbox.

weaknesses:
Perhaps my only question is that it seems that is largely synthetic images on black and white,  which makes it more controllable, but not sure if this would also constrain the kind of models and training routines that can be used in the test, because most models would be trained on complicated backgrounds, such as naturalistic scenes.

questions:
* Would the types of images in the benchmark constrain the kinds of models or training data that people can use, given that most models are usually trained on more crowded scenarios?
</content>
</thread_object>

</thread>
<thread>
creation date:
1730741889765

thread_type:
official_review

<thread_object>
creator: reviewer_wknk

<content>
summary:
They authors introduce MindSet, a benchmark of image datasets and experiments to compare visual perception of humans and models. The benchmark spans multiple levels of perception, from low and mid-level vision to object recognition and visual reasoning. What differentiates this benchmark from others is that it combines experimental manipulations with each image dataset. For example, in a version of a same/different task that's been studied over the years, the authors test the task as they change object features, such as the color, open/closed-ness of the shapes, etc. The benchmark is comprehensive, albeit scattershot, and the authors also discuss a variety of ways to evaluate models on their benchmark.

**Update**

After discussing with the authors, I've decided to keep my score. I think they have great points about how NeuroAI should shift incorporate a broader set of psychological experiments as well as perturbations to build the next generation of models of biological vision. I also think that the experiments and datasets described in this paper could be one part of that story. But I disagree with the authors that the current paper constitutes a sufficient work package for ICLR. What I would have liked to see are benchmarks on the TIMM library (https://github.com/huggingface/pytorch-image-models) to show empirically how today's models, designed by computer scientists, respond on these experiments. The authors point out that some of that work has been done in the past, but there is not central benchmark for these models, and the authors must back up their claims of misalignment and failure of today's models with evidence. I believe the bar for publication in a journal/conference that is not a review should be experiments like these, even if they are null results. If those experiments were here I would recommend acceptance. As they are not, I do not think this paper should go through and will keep my score where it is.

strengths:
I really like the emphasis on perturbation, which is an essential missing ingredient in popular benchmarks like BrainScore. This indeed appears to be the main — and IMO a powerful — motivation for the paper. To push computational vision and neuroscience into a new and more effective regime for modeling brain data.

The benchmark itself is comprised of many different interesting experiments taken from psychology and neuroscience. I appreciate the goal of searching for a single model that can explain everything from lightness illusions to visual reasoning.

weaknesses:
The biggest problem with this paper is that it does not make a clear contribution. None of the experiments are novel. There's no actual benchmarking done on models. It's a scattershot approach which is probably the only way this could be done, but there's no control across the different benchmarks — i.e., a single set of visual primitives used to test low-and mid-level vision and visual illusions vs. another set for shape/object recognition. I don't know what to take from this paper other than it's reasonably argued that the current state-of-the-art is insufficient and perturbations would be a better way of advancing models of human vision. Not much more to say than this. What the paper needs is the following:

- A large scale survey of existing models. How well do they capture human data for each experiment?
- If there's no positive result in existing models, provide a model that has a positive result.
- Ideally, and as mentioned above, data is held constant while you change task. For example, could you test relational vs. coordinate change, NAP vs. MP, Muller-Lyer illusion using the same constituent visual components? That is, the number of "on" pixels is the same in each example, but the perceptual effect those pixels have is fundamentally different? This would at least make it possible to plot the experimental results of each of these experiments against each other. This is more of a thought than a must have, given the wide variety of experiments, but IMO it is an essential part of testing models in order to ensure that task rather than statistics are driving a given result.

Another problem I see is in the similarity judgement analysis summarized in Fig 2. The authors use euclidean distance between stimuli to compare their representations at different layers of a network. Why use this instead of the decoder method? There's no reason why euclidean distance is the "right" metric for making decisions at a given layer. Moreover, I don't believe that the bar-and-whisker plots back up the authors' assertion of a difference in same/different shape conditions in early vs. late layers.

questions:
See weaknesses.
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<thread>
creation date:
1732030979313

thread_type:
official_comment

<thread_object>
creator: authors

<content>
title:
General response

comment:
It is striking how divided the responses are to the submission, with two 8s, two 3s, and a 6. We think this reflects the fact that our toolkit stems from a fundamentally different approach to studying DNN-brain alignment, and some basic misunderstandings of that approach.  When scores are distributed like this, an average score provides a misleading overall assessment, and it disadvantages controversial articles that are more likely to receive contrasting responses like this.  The critical question is whether the higher or lower scores better characterize the contribution. 

Before responding to the individual reviewers below we would like to (1) highlight how our approach to evaluating DNN-brain alignment in vision is quite different than standard practice and (2) address the two most important criticisms that were common across multiple reviews. In both cases, there are important misunderstandings that are contributing to the lower scores. 

A standard practice in NeuroAI for assessing DNN-brain alignment is to develop benchmark composed of a set of naturalistic images paired with associated behavioural or brain responses so modellers can compete in accounting for variance explained on that dataset.  More recently, some benchmarks have included artificial stimuli manipulated in theoretically motivated ways, such as the most recent version of Brain-Score.  But to date, the field is characterized by attempting to build models that account for as much variance on a relatively small set of benchmarks, with relatively little consideration of psychological findings.  By contrast, the MindSet: Vision toolkit includes stimuli from 30 psychological experiments where the qualitative main effects of manipulations are well-known, theoretically significant, and robust.  For instance, humans are reliably subject to a specific visual illusion under certain conditions. Our toolkit can be used to build models that account for a wide range of critical psychological findings at a qualitative level (e.g., does a model show a significant illusion or not). Both approaches have their place, but the latter method is rarely used at present. Our goal is to highlight to the ML and NeuroAI communities a set of critical psychological experiments as well as the tools to facilitate the testing of models on these experiments where the qualitative effects are well established.
</content>
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</thread>
<thread>
creation date:
1732799578381

thread_type:
official_comment

<thread_object>
creator: authors

<content>
title:
General comment to editors summarizing our responses: Part 1

comment:
We just wanted to highlight our more important points and disagreements with the reviewers after a lengthy exchange.  In all the back-and-forth it is easy to lose track of the fundamental issues.  We first consider the most important criticisms of the reviewers, and then describe why we think the toolkit is an important contribution.

The three negative reviewers all see promise in our work, but 1) they all think we need to test a set of ANNs on all the experiments and 2) some reviewers think we need to collect behavioral datasets for some or all of our experiments.  The reviewers make three arguments to support their conclusions.   

First, some Reviewers are unconvinced that ANNs will struggle on the psychological experiments included in the toolkit, and without this, it is not clear whether the experiments provide a useful test of models. But it is already well demonstrated that ANNs do poorly on a wide variety of psychological experiments.  For example, three benchmarks based on images in our toolkit have recently been added to Brain-Score, and 21 models all did poorly (2 of the benchmarks using stimuli from our toolkit won the competition).  We have also provided references to 9 articles that systematically explored different image conditions included in our benchmarks, and again, all models fail in important ways on these image datasets. And Bowers et al. (2023a) in Behavioural and Brain Sciences systematically summarized how ANN account for few findings in psychology.   

Imagine we had carried out the requested simulation studies and assessed how well a few ANN performed on a single experiment in each of the 30 stimulus conditions.  The ANNs would fail on many of the conditions (see above), and then we would be open to the criticism that some other (untested) models would succeed.  That is, we will only have provided a superficial falsification of a small set of models. And that would be a valid criticism.  Furthermore, reviewer wknk does not think reporting failures of ANNs is adequate, writing: “If there's no positive result in existing models, provide a model that has a positive result”.  This view is commonplace, as NeuroAI has a strong bias against accepting papers that highlight limitations of ANNs (Bowers et al., 2023b).  This is another reason not to provide a superficial falsification of ANNs.  And in any case, our goal is not to falsify ANNs, but to encourage researchers to adopt a different way of assessing ANN-brain alignment to make better models of biological vision.

Second, some of the reviewers think we need to carry out behavioral studies to make it easy for researchers to assess how much variance models account for, similar to the Brain-Score approach.  The assumption is that trial-level data provides a better assessment of a model than the condition-level results reported in psychological studies. But this is mistaken. The entire point of manipulating independent variables is to test specific hypotheses.  By contrast, by assessing amount of variance explained, you do not know whether the model captures the impact of the independent variable.  For instance, a model could account for a substantial amount of variance and not account for the manipulation at all (of course, if the model accounted for 100% of variance, it would necessarily account for the manipulation, but short of that, there are many other sources of uncontrolled variation that a model could account for without accounting for the manipulation).  The Brain-Score approach makes sense when analyzing observational datasets in which no variables have been manipulated, but in our dataset, the key issue is whether ANNs can capture key effects of the manipulation.  We know the impact of the manipulations from past psychological research, and we know that current ANNs struggle with these effects.  Our toolkit can support many research programs attempting to build models that account for these key properties of human vision.
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Metareview:
metareview:
The paper introduces the MindSet: Vision toolbox which allows us to manipulate images systematically and uses scripts to test DNNs on 30 psychological experiments. This enables us to carry out hypothesis-driven comparisons with human visual perception.

While the paper is well-written and the motivation to use augmentations for analyzing different behaviors is compelling, it received mixed reviews, with two scores of 8 and three scores of 3. Discussions among the AC and reviewers highlighted key issues that persist despite the rebuttal:

[limited insights from these experiments] The paper lacks systematic benchmarking of all baseline AI models in these experiments. It remains unclear what we should do or not do to improve the designs of these models even after benchmarking models on these experiments.

[the dataset is incomplete] Human behavioral data in these experiments is missing. This makes us fail to assess how much variance models account. Besides, these human performances can serve as an upper bound and help us identify performance gaps between humans and AI models. Unfortunately, all these are missing in the current version.

[limited contributions and novelty claims are vague] Given the two points above, the paper neither contributes a complete solid dataset nor does the paper present claims or insights on what the current AI models are bad or good at.

At this stage, the paper is not ready for publication, but addressing these issues could significantly enhance future versions.