Low-Pass Filtering Improves Behavioral Alignment of Vision Models
Authors: Max Wolff
Thomas Klein
Evgenia Rusak
Felix A. Wichmann
Wieland Brendel
Venue: ICLR 2026 Poster (2026)
Keywords: behavioral alignment; cognitive science; CLIP; computer vision; shape bias; error consistency
Primary area: applications to neuroscience & cognitive science
TL;DR: Low-pass filtering images improves DNN behavioral alignment with humans, and there is a physiological explanation for that.

Abstract: Despite their impressive performance on computer vision benchmarks, Deep Neural Networks (DNNs) still fall short of adequately modeling human visual behavior, as measured by error consistency and shape bias. Recent work hypothesized that behavioral alignment can be drastically improved through generative - rather than discriminative - classifiers, with far-reaching implications for models of human vision. 

Here, we instead show that the increased alignment of generative models can be largely explained by a seemingly innocuous resizing operation in the generative model which effectively acts as a low-pass filter. In a series of controlled experiments, we show that removing high-frequency spatial information from discriminative models like CLIP drastically increases their behavioral alignment. Simply blurring images at test-time - rather than training on blurred images - achieves a new state-of-the-art score on the model-vs-human benchmark, halving the current alignment gap between DNNs and human observers. Furthermore, low-pass filters are likely optimal, which we demonstrate by directly optimizing filters for alignment. To contextualize the performance of optimal filters, we compute the frontier of all possible pareto-optimal solutions to the benchmark, which was formerly unknown.

We explain our findings by observing that the frequency spectrum of optimal Gaussian filters roughly matches the spectrum of band-pass filters implemented by the human visual system. We show that the contrast sensitivity function, describing the inverse of the contrast threshold required for humans to detect a sinusoidal grating as a function of spatiotemporal frequency, is approximated well by Gaussian filters of a specific width.

Published as a conference paper at ICLR 2026
LOw-PASS FILTERING IMPROVES BEHAVIORAL ALIGNMENT OF VISION MODELS
Max 1,
Thomas Klein* 1,2,3,4
Evgenia Rusak 1,2,3,4,5 2
Felix A. Wichmann 22
Wieland Brendel:1.2.
ABSTRACT
Despite their impressive performance on computer vision benchmarks, Deep Neural Networks (DNNs) still fall short of adequately modeling human visual behavior, as measured by error consistency and shape bias. Recent work hypothesized that behavioral alignment can be drastically improved through generative- rather than discriminativeeclasiifee with far-reaching implications for models of human vision.
Here, we instead show that the increased alignment of generative models can be largely explained by a seemingly innocuous resizing operation in the gener- ative model which effectively acts as low-pass filter. series of controlled experiments, we show that removing high-frequency spatial information from discriminative models like CLIP drastically increases their behavioral alignment. Simply blurring images at test-time rather than training blurred images achieves a new state-of-ther of-thh-artttttt score on the model-vs vy-human benchmark, halving the current alignment gap between DNNs and human observers. Furthermore, low- pass filters are likely optimal, which we demonstrate by directly optimizing filters for alignment. To contextualize the performance of optimal filters, compute the frontier of all possible pareto-optimal solutions to the benchmark, which was formerly unknown.
We explain our findings by observing that the frequency spectrum of optimal Gaussian filters roughly matches the spectrum of band-pass filters implemented by the human visual system. We show that the contrast sensitivity function, describing the inverse of the contrast threshold required for humans to detect sinusoidal grating as function of spatiotemporal frequency, is approximated well by Gaussian filters of the specific width that also maximizes error consistency.
INTRODUCTION
While Deep Neural Networks (DNNs) widely considered the best models of the human visual system (Kriegeskorte) 2015 Kietzmann et al,, 2017] Cichy & Kaiser 2019) Doerig et al.) (023). there is large body of research exposing the many ways in which DNNs exhibit non-human-lide
behavior-see see (Wichmann & Geirhos 2023) for an overview.
One behavioral dimension on which DNNs differ from humans is their lack of shape bias: Geirhos et al.] (2019); et al. (2088) show that when presented with stimuli characterized by conflicting shape- and texture cues, humans will classify images according to the shape cue, while DNNs will classify according to the texture cue. Furthermore, DNNs systematically disagree with human observers about which images they find difficult, as (eiihho et al. (02T) show using the error consistency metric (Geirhos al., 22000) Klein al. (025). Both of these findings indicate that humans and DNNs likely implement different strategies for solving the task core object recognition (DiCarlo al. 2012), raising concerns about their suitability computational models of the human
'Equal contribution. Joint supervision.
Max Planck Institute for Intelligent Systems, Tübingen, Germany. University of Tübingen, Germany. 3 Tübingen AI Center. ELLIS Institute Tübingen. Cohere. Correspondence mn.wofff1611e @ @gmail..om t.klein tuichieumittchhineen.e

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visual system. Progress towards behavioral alignment of DNNs measured by shape bias, error consistency and out-of-distribution (OOD) robustness is monitored by the (MvH) benchmark (Geirhos et al. 2021).
Previous state-of-the-au results had been achieved by very large models trained on gigantic, diverse datasets (CLIP (Radford et al.] (021) and ViT-22B (Dehghani et al.] (023)). However, Jaini al.) (2023) have recently shown that generative models such as Imagen (Saharia et al. 2022) achieve the most human-like behavior to date, with an error consistency of 0.31 and shape bias of 0.99. Interestingly. Imagen also exhibits bias towards low-frequency features compared to other DNNs. Jaini al. (2023) speculate that this tendency could be caused by the diffusion noise encountered during training, or the generative objective itself.
The hypothesis that the generative objective is the driver of improved shape bias and error consistency ties into a much larger debate about the necessity of generative models for robust vision: It is an open question whether the human visual system operates in discriminative, bottom-up fashion (vision as inverse inference, (Von Helmholtz, 1867)) whether it is a model-based system with top-down priors, e.g. for predictive coding (Yuille & Kersten 2006). If Imagen were so human-like because of its generative objective, it could be hint that the human visual cortex also relies such principles.
However, a thus far neglected processing step Imagen is downscaling of inputs to a resolution of 64 64 pixels, rather than processing the full 224 x 224 pixel resolution. This downscaling operation effectively a low-pass filter. We thus consider different hypothesis, derived from our knowledge of the human visual system: Imagen's 's low-pass filtering might be the true reason for its increased human- like behavior, since both the optics of the human eye and early neural processing stages low-pass filters, least at the short presentation times used in the MyH benchmark. To investigate this hypothesis, we test how low-pass filtering of input images al test time affects behavioral alignment, which is in contrast with earlier work that investigated the effects of training on low-pass filtered images.
We find that low-pass filtering images at test time can drastically improve the behavioral alignment between almost all models and human observers, as demonstrated in Figure A ViT-H-14 OpenCLIP model tested on blurred images surpassing that of Imagen (0.31) (Jaini et al. 2023), halving the remaining gap between human- -NN and human-human alignment (see Table 1).
2.5 px) achieves an error consistency of 0.37, substantially
1. low-paas filter
trenstormation
standard blured
Emer Dorwsteer
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vm 14
comvltod
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Anjay Studies
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Figure 1: Low-pass filtering images increases human-machin Error Consistency. For the majority of investigated models, find that low-pass filtering images prior model evaluation can substantially increase their error consistency with human observers.
Prepending vision models with low-pass filters has clear physiological motivation: Like any optical system, the human eye acts as a low-pass filter for the light hitting the retina (Campbell & Green 1965 Williams et al. 1994), and the processing by the visual cortex acts as additional filter over spatial frequencies (Campbell & Robson 1968; Kelly 1979). This filter described by the contrast sensitivity function (CSF). The tuning curve of the CSF depends the temporal frequency of the
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signal, with higher temporal frequencies (i.e. shorter presentation times) moving the peak of the spectrum towards lower spatial frequencies (Kelly) 1979). turning the band-pass filter into a low- low-pass filter at very high temporal frequencies. We show that at the fairly short presentation times employed by MyH (200 ms), the CSF is approximated relatively well by low-pass filters, and the Gaussian that produces the highest error consistency to humans is close to the optimum approximation.
Furthermore, we observe that there is an inherent trade-off between accuracy and error consistency in MyH: To achieve maximal error consistency with human observers, classifier has to roughly match human accuracy on the benchmark (see Klein et al. (2025)). This means that while the three metrics that make up model-vs-humait
error consistency, shape bias and OOD-accuracy- have clear ceilings individually, the mathematical relationship between the metrics renders the overall ceiling score obtuse: not obvious what the best possible score on the benchmark is. We solve this problem by computing the frontier of all pareto-optimal solutions to the benchmark, thereby establishing the benchmark ceiling and revealing that MvH not yet saturated, and much more progress can theoretically be made achieving behavioral alignment between humans and machines.
Our contributions are thus the following:
1. We show that low-pass filtering images at test time (cquivalent to blurring or resizing images) drastically increases models' behavioral alignment measured by error consistency and shape-bias, more so than training on low-pass filtered images.
2. We thus provide an alternative explanation for the increased behavioral alignment of Imagen, which was previously attributed to its generative objective.
3. We explain these findings by showing that the best low-pass filters approximate the CSF at short presentation times reasonably well, suggesting that low-pass filtering works because matches the filtering implemented by the human visual system.
4. We analyze the hummmnn benchmark, revealing an inherent trade-off between the two objectives of OOD-robustness and error consistency. We quantify this relationship by computing the frontier of all pareto-optimal solutions to the benchmark.
2 RELATED WORK
DNNs models of the human visual system.
The question of whether DNNs are suitable models of the human visual system has taken center stage of vision science research (Wichmann & Geirhos
2023 Doerig al. 2023 Bowers et al. 2022 Cichy & Kaiser 2019 Schrimpf et al. 2018). Since a core property of any good model is its ability to reproduce the behavior of the target system, needed (Sucholussky et al. 2002)). Different approaches to this problem have been explored, Lin,ley et al. al. (2023), but at the center of our work is the model-vs-humin ---ummn-maan benchmark by Geirhos et al. (2021). ). While various attempts have been made to increase shape bias (Geirhos et al.) 2019: Li et al. 2020 Nuriel et al. 2021 Brochu 2019), direct optimization towards increased error consistency is underexplored.
methods for measuring (and potentially improving) the behavioral alignment humans
[
Frequency tuning in the visual system. The seminal work by Campbell & Robson 1968 gave rise to the idea that the early visual cortex is composed of channels sensitive specific bandwidths, by noting that human contrast sensitivity is a function of spatial frequency, rather than global contrast alone. There is ample physiological evidence supporting this theory, e.g. De Valois et al. (1822). The emergence of human-like contrast sensitivity functions in DNNs has been explored as well (L) al. 2022] Akbarinia al.] 2202). ) providing empirical evidence that DNNs exhibit human- like CSFs. Notably, these works find that DNN-CSFs are similar to the standard human CSF at unlimited viewing time. [Schyns& & Oliva (1994) showed that the channels of the human visual system do not operate at the same timescale: Humans process the low spatial frequencies first. Interestingly. this ordering (low to high) is also the order in which DNNs learn to make use of frequencies as features (Rahaman al.) 2019) (with more robust models relying lower frequencies more (Li et al. 2023 Yin et al. 2019)), and there seem to be benefits a visual diet that gradually introduces higher spatial frequencies VVgelsaagg et al., 2018 2024). Consequently, various attempts have been made to systematically train DNNs on low-pas, filtered images (Jinsi et al., 2023 Dang & Tong 2024). with one such training approach even achieving competitive shape bias results on MvH
Lu al.
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(2025). Jang & Tong (2024) also achieve slightly higher error consistency with clur-trained models than standard ones, but their EC values stay well below 0.2. In general, how DNNs utilize spatial frequencies is an active area of research: Subramanian et al. (2023) found that the bandwidth of frequencies to which DNNs are sensitive is much wider than that of humans, which is a potential source of behavioral differences between the two kinds of systems. They propose that narrowing the critical band of networks should make them more robust-in line with our finding that band-pass filtering to "human-matched" frequency range increases alignment humans.
3 METHODS
3.1 ALIGNMENT METRICS
To measure model's s behavioral consistency with humans, we use the model-vs prodel-ss-humarlln humarrppackeer Geiihoo et al. 202T). This benchmark consists of images from the training set of ImageNet- Ik (Russakovsky et al. 2015) grouped into 16 coarse "super-classes" (airplane, bear, bicycle, bird, boat, bottle, car, cat, char, clock, dog, keyboard, knife, oven, truck) which subsume multiple of the more fine-grained ImageNet- Ik classes at the basic level of classification (Rosch et al.) 1976). The images are corrupted by 12 different parametric image distortions, such as additive noise or color inversion. In addition, the benchmark contains stylized, adge-filtered, silhouette, cue-conflict (see Shape Bias below) images and sketches. All of these images were shown multiple human subjects, who had to solve a speeded classification task: Images were shown for 200ms, followed by pink-noise backward mask, suppress recurrent processing in the visual cortex as much as possible. Humans then had to classify images into one of the 16 super-classes. To evaluate new model on the benchmark, the model first has to classify all images, so that three metrics measuring the similarity of the model to human judgments can be computed, which we explain next.
Shape Bias. The shape bias metric is computed on the cue-conflict images. These images were generated using neural style transfer (Gatys et al. 2015) by combining the content (shape) of one image with the texture (style) of another. The dataset consists of 1200 images, sampled evenly from the 16 classes. To calculate shape bias, the model is first evaluated on all cue-conflict images. Then, all trials are discarded on which neither the class implied by the shape cue, nor the class implied by the texture cue was predicted. Shape bias is then defined as the proportion of the remaining trials for which the model decides according to the shape cue. Notably, the metric does not take model's overall accuracy into account, meaning that one can achieve high shape bias by classifying a few images into the class implied by the shape cue and predicting nonsense on other images (Doshi et al. 2024). Shape bias takes on values between O and I, with higher shape bias indicating higher behavioral alignment, because humans exhibit almost perfect shape bias (0.96).
Error Consistency. This metric indicates the extent to which two decision makers (in this case,
a model and human observer) make errors on the same images (Geirhos et al.) 2020 2021). is measured on all 17 experiments from the model -vamumnnn package, excluding handful of conditions on which humans performed below a threshold. Error consistency is computed by calculating Cohen's 's k (Cohen 1960) two binary sequences indicating whether each classifier gave a correct response on a trial, see Geirhos et al. (2000); Klein et al. (2025) for details. λ takes on values between -1 and 1, with 1 indicating maximum agreement, O meaning that they agree not more than expected by chance, and -1 indicating maximum disagreement. Within the model-vvshuman benchmark, EC values are averaged in hierarchical fashion, by first averaging across humans within each corruption strength, then across corruption strengths, and finally across corruptions. While a higher EC humans is always indicative of more behavioral alignment, it is not necessarily possible for EC to take on all values in [-1, 1]. since Kyaax depends on the accuracy mismatch between the classifiers (see Klein et al. (025) for an in-depth explanation).
OOD Accuracy. This metric measures the aggregate accuracy of a classifier on all 17 datasets. Higher OOD-accuracy considered better, because humans typically demonstrate higher OOD- accuracy on the model-vs-human corruptions. (Note that recently progress in model robustness has
http::://githubb.co///eeeeee
-humnn
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increased to the point of a paradigm shift, where models now outperform humans some corruptions (Li et al. 2025).)
3.2 MODELS AND TRANSFORMATIONS
We evaluate models trained on LAION-2B available through the open-source OpenCLIP package (Schuhmann et al. 2022, Radford et al., 2021; Cherti et al.] 2023; Ilbarco et al. 2021) In our experiments, we evaluate CLIP in the zero-shot setting using the 16 model 1 Vs wrr--hune hummnn classes and the standard 80 prompt averaging scheme.
For blur and resize transformations, we use torchvision (Marcel Rodriguez 2010 Paszke et al.) (017)) implementations of Gauss iroussianBlus anBlur and Res i ze with bi-cubic interpolation. We resize the image from its original size of Ro x Rg to the target resolution R₁ x Rj, and back up to Rg x Ro. This emulates the resizing that Imagen conducts. A higher "resize strength" means resizing to a lower resolution, i.e. larger ratio Ro/ Ri We visualize the strengths of blur and resize transformations that we use in Figure 7
3.3 LEARNING A FOURIER FILTER TO MAXIMIZE ERROR CONSISTENCY
We also experiment with learning a Fourier filter that maximizes error consistency with humans. To do so, we use the model -vs human images and corresponding human predictions, and the frozen OpenCLIP ViT-H-14 14 model. We first compute an optimal binary vector of human responses, as outlined in Appendix A.3] Our loss then induces the model to give classification responses leading to the same correctness values, by essentially optimizing the cross-entropy between the optimal correctness values and the model correctness values.
If the ideal response is the correct label, we induce the model to predict the correct label as well. Otherwise, we instead induce the model to predict the incorrect class which currently seems most likely to the model, because this particular wrong response (out of the 15 possible wrong responses) should be easiest to learn.
Since our filter Ge needs to respect Hermitian symmetry and we only want to modulate frequencies without conducting phase shifts, we parameterize the 224 x 224 DFT-matrix the filter as a real 112 x 112 matrix. (Note that most of the MvH images are in grayscale; we otherwise apply the filter to each color channel independently.) These parameters form the top left quadrant of the filter, and obtain the remaining quadrants by computing the complex conjugates, which of course still all have an imaginary component of zero. Since we are limited to the < 12, 000 images in MyH to learn 112 x 112 = 12, 544 parameters, optimizing this filter directly would cause overfitting and lead to noisy "adversarial" pattern. To prevent overfitting, we regularize the filter to enforce sparsity and smoothness, and use all available images for training, without train-test split. Sparsity is enforced via an L regularization over filter parameters, while smoothness is achieved by blurring the filter itself. Let F be the Fourier transform, fr be the image encoder, and fr the text encoder of the CLIP model. Let r be an image, and Go the filter to be leamed. Let by be a Gaussian blur function, parameterized by the blur strength y. Then, each element 8, of the vector s of cosine similarities between the image and all "labels" yı is given by
8; = sim (I: * + (Go)), (GG))
Let denote the one-hot encoded ground-truth label of x. H the cross-entropy, and the softmax function. Then the loss for a single image is given by
((x) = =(x)=
(o()))) + |||01|--
Since CLIP similarities are too close in magnitude to provide enough signal to the cross-entropy loss (after Softmax), we introduce a learned temperature parameter F, similar to Radford et al. (002)) Using a grid search, we found that an L' weight of 5 x 10-5 and blurring strength of 7 = 6.0
yielded the best error consistencies with humans. We use the Adam optimizer, and initialize the filter from ones with small amount of Gaussian noise (a² 10-5) added.
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Once we have obtained the final filter, apply it to the model-vs-huree images, classify them using the frozen model, and evaluate the error consistency with humans (exposed to clean images).
3.4 EVALUATING GOODNESS OF FIT TO THE CSF
The contrast sensitivity function (CSF) characterizes the contrast sensitivity of human observers function of spatial and temporal frequency (Kelly) 1979). We use the empirical estimates of the CSF obtained by Kelly (1979), which we describe in more detail in Appendix A.2 To quantify the goodness of fit between the human CSF (normalized to the range [0. LJ) and Gaussian of specific o, we compute LW RE between the normalized CSF and the Gaussian, for the relevant frequency range. This range is limited by the display apparatus in model-vs-hunen were presented at 3 of visual angle, so fmin = 3 cycles per degree, and the Nyquist frequency of the monitor, which was fmaz = 42.58 cpd. To take into account that the spectrum of natural images is roughly f- and that the power of the spectrum is therefore P(f) = f with 8 = 2, we weight the errors accordingly:
where images
Simer f (-((CF((F) - G
LWRMSE
Jmmax f-8 df
The ID spectrum depicted in Figure 4 can be thought of as the cross-section of a radially symmetric filter, thus giving rise to 2D spectrum that can be scaled appropriately and applied to images.
4 RESULTS
4.1 LOW-PASS FILTERING EXPLAINS IMAGEN'S 'S ALIGNMENT.
We hypothesize that Imagen outperforms our reference model on error consistency (EC, the degree to which model makes the same mistakes as humans) and shape bias (SB, the tendency of model to prefer shape cues over texture cues) simply because of its lower input resolution of 64 x 64 pixels, which amounts to low-pass filtering of the input. To test this hypothesis, we prepend the reference model with a low- ww-paass filter, which we implement once as a resizing operation analogous to Imagen, and once Gaussian blur, which should have very similar effect. We vary the blurring strength and observe the resulting changes in performance on the three alignment metrics. As reference model, choose OpenCLIP ViT-H-14, because it is the most well-aligned model we have access to, with a baseline ECC of 0.28, an SB of 0.60 and an OOD-accuracy of 0.78.
Shape Bias
Increasing either blur- or resize-strength monotonically increases shape bias (see Figure 2). Since blurring removes high-frequency texture information, the model is forced to rely low-freyuency shape cues. Independent of the method of implementation, low-pass filtering of input images alone account for Imagen's increased shape bias, even in models without the generative component that was formerly hypothesized to explain Imagen's 's human-level shape bias.
Error Consistency. As we increase the blurring strength, the model's EC rises from k 0.28 to K 0.37 (after blurring with 2.5)
0.35 (after resizing to 64 x 64), respectively (see Figure 2}. For reference, the previous highest error consistency had been reported for Imagen (Saharia et al. 2022) at к 0.31. After certain "critical point," however, error consistency begins to decrease for higher blurring/resizing strengths. We break down the error consistency gains into the different MyH conditions in Appendix A.5, but observe gains across conditions.
OOD Accuracy. Notably, these transformations do have significant effect on the model's OOD accuracy: OOD accuracy dips by 6 percentage points after blurring at a 2.5 and by percentage points when resizing the image to 64 x 64 (see Table 1). This is to be expected, since we are effectively removing features that the model can use. As we argue in Section 4.5) this drop in accuracy may actually be necessary to improve behavioral alignment. However, the drop in accuracy is by no means catastrophic. This is important, because shape bias does not account for accuracy (Doshi et al. 2024) and can be trivially increased by misclassifying specific images.

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0.9
Condlctency
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humans
0.28
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32
112 75
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Parameter
Figure 2: Low-pass filtering test stimuli improves behavioral alignment. Gaussian blurring [A+C] and resizing [B+D] both lead to higher shape bias (A+B) and error consistency (C+D). While the shape bias strictly increases under either transformation, the error consistency reaches maximum "critical point" and declines afterwards.
Together, these findings offer an alternative explanation for the increased behavioral alignment of Imagen: Its lower imput resolution alone suffices to explain its alignment. Jaini et al. (2023) go as far re-training ResNet-50 on ImageNet-II Ik with diffusion noise augmentation obtain shape bias of 0.78, but a standard R0sNet-101 with a prepended low-pass filter (a = 3.0) achieves a shape bias of 0.8 without any extra training.
4.2 LEARNING AN OPTIMAL FILTER INDEED YIELDS A LOW-PASS FILTER.
Given that low-pas www-pass filtering yielded considerable improvements, filter would look like. To address this question, we learn a filter in Fourier space for maximum error consistency using gradient descent (see [Section 3.3 for details), The resulting filter is depicted in Figure 3] where we plot the optimal Fourier filter. Since we deliberately parameterized the filter in way that allows for rotational asymmetries (i.e. different directions being affected differently) find an upper bound on the best possible filter, we observe some artifacts that could be a consequence of potential overfitting as speculated Section 3.3 However, the filter's spectrum clearly concentrated in the low frequencies.
wonder what the optimal
-112
venticl frequincy teycles per image]
0.3
0.2
112
112
112
horizontal frequency (cycles per image]
Figure 3: The optimal filter is low-pass filter. The log tra---rnnffomddd center-shifted amplitudes of the optimal filter, as function of the horizontal and vertical frequencies (in cycles per image).
4.3 LOW-PASS FILTERING WORKS BECAUSE APPROXIMATES THE CSF.
The human visual system differs from DNNs in that images are not directly available to cortical neurons at full spatial resolution and bit-depth. Instead, the visual input first impoverished by the imperfect optics of the eye, and later by the limited photoreceptor density the retina and other neural factors. The degree of the reduction in perceived contrast depends on the spatial frequency of the input (Campbell & Robson 1968), as well the temporal frequency of its presentation
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1979). The overall reduction in perceived contrast is described by the contrast sensitivity function (CSF), which gives the inverse threshold contrast as a function of spatial and temporal
Kellyy
frequency of stimulus. In figure Figure 4] show that the spectrum of our rest-performing low-pass filters approximates the CSF at 200 ms quite well. Interestingly, Li et al. 2022); Akkarinia al. 2023) have found evidence for human-like CSFs in DNNs, but specifically CSFs matching the standard human CSF for unlimited viewing time. If networks already exhibit a CSF that matches the human CSF at long viewing times, an adaptation has be implemented to account for the shorter presentation times-an whhchh tow-pass filtering accomplishes. We thus interpret our results to mean that by prepending models with low-pass filters, we effectively match their input to that available to the visual cortex of human subjects.
1.0
0.51
1.
Nomalzzd Contrre Sursiiitt
Lonepass fierr (mm2.5 pc)
1.55
0.8
2.05
0.6
WRMSE
0.49
0.48
0.4
0.2
0.46
0.0
fiker
Contrast sensitivity a presentation time of 200 ms as function of spatial frequency (red curve) is approximated well by the best Gaussian filter (blue curve). Right: The best-fitting Gaussian has a of about 2.5pæ, matching our empirical result. Evidently, this finding is robust to the exact choice of B.
Figure 4: Low-pass filters approximate the CSF well. Left:
Having established that Gaussian filters approximate the CSF well, we next test the effect of using the CSF as filter directly. Indeed, this yields error consistency of 0.365, which beats Imagen and is statistically indistinguishable from the ideal Gaussian. We thus conclude that the improved error consistency between humans and DNNs is caused by providing DNNs with images whose spectra filtered in way that approximates how the human visual cortex receives its input: Diffracted by the optics of the eye and processed by the first few neural transformations, presumably in the retina and the lateral geniculate nucleus (LGNN.
model
error consist. t
shape bias 1
OOD acc. 1
Humans (avg)
0.43
0.96
0.72
ViT-22B-384
0.26
0.87
0.80
OpenCLIP ViT-H-14
0.28
0.60
0.78
Imagen
0.31
0.99
0.71
OpenCLIP ViT-H-14 14 Resized (64x64) [ours]
0.35
0.91
0.75
OpenCLIP ViT-H-14 14 Blurred (a = 2.5) [ours]
0.37
0.96
0.72
OpenCLIP ViT-H-14 Fourier-filtered [ours]
0.38
0.95
0.73
Table 1: Low-pass filtering improves error consistency and shape bias.
4.4 LOW-PASS FILTERING WORKS ACROSS MODELS.
A natural next question is whether the blur and resize transformations only increase human-model error consistency for the OpenCLIP ViT-H-14 model, or whether this a more general phenomenon which applies to other models as well. To answer this question, we test several models included in the model-vs human package, as well as other OpenCLIP models, with prepended low-pass filters. We find that our approach generalizes almost perfectly: Shape bias and error consistency increase with low-pass filtering for almost all tested models. The effect is particularly strong for the OpenCLIP models, especially those with smaller patch sizes of 14 instead of 32 pixels (see Figure 5). The reason might be that a smaller patch-size biases a model towards high-frequency texture features, which are strongly affected by these transformations.
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0.45
A
Emor consestency
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Shape Blos
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Resize Parameter
Standard
Peremeter
Figure 5: Removing high-frequency information from test stimuli improves behavioral align- ment for wide range of models. We measure shape bias and error consistency for ResNet; SWSL, BiT-M, VIT, Noisy Student, and variety of OpenCLIP models. We find that in general, shape bias [A+B] increases with blur and resize strength, while error consistency [C+D] usually increases at first before dropping off again.
4.5 THE ACCURACY- ACCURACY-CONIISTENCYY TRAAEOFFF
Because OOD accuracy and enror consistency are odds. the ceiling performance on model-vs- human is not single point, but a curve of pareto-optimal solutions. which we call the pareto- prect-fronnter. The maximum possible error consistency to human observers 0.674, necessitating an OOD accuracy of 0.67. As the OOD accuracy approaches 100%, error consistency approaches 0. Between these two extremes, there a smooth curve of pareto-optimal solutions, which we plot in Figure 6 Since these values are optimal and thus fixed, there is no uncertainty about them, so we plot no confidence intervals. We relegate detailed explanations of how we arrived at these values to Appendix A.3] where also explain the inntuir-ittuiiive finding that the maximum EC extends beyond the average inter-human EC. We also find hints of this curve empirically in the models we investigate, see Figure (right).
While our filtered models achieve performance values closest to the noise ceiling of inter-human error consistency, there is still plenty of room for improvement, especially in terms of improving OOD accuracy without sacrificing alignment to humans. The MvH benchmark is clearly not saturated, but we note that it suffers from some idiosyncrasies- example, there are trivial ways of achieving high shape bias (Doshi et al. 2024), and error consistency is known to be noisy (Klein et al. 2025).
However, we still believe the trade-off between behavioral alignment and accuracy to be more fundamental: A machine that reaches super-human accuracy is bound to use features that humans do not use, which should generally result a misalignment of their behavior. By carefully imposing limitations on the model (in our case, limiting the frequency content of its input via low-pass filtering), more human-like like features may be extracted, albeit at the cost of accuracy.
4.6 LIMITATIONS
Neural Similarity Measures. A natural extension of this work would be to evaluate the effect of test-time low-pass filtering neural similarity scores as well. Ideally, one would compare the neural predictivity afforded by our most human-like models to that of many other models, across different datasets of neural recordings. Such comparisons platform (Schrimpf et al. 2018). However, model inputs in Brain-Score are standardized to subtend eight degrees of visual angle. To achieve this. MvH images padded with mean gray pixels, which amounts to a downscaling operation that conflicts with our test-time filtering. (Notably, as consequence, error consistency results on Brain-Score do not match model--s-huuaat We would thus have to drastically change the preprocessing pipeline of Brain-Score evaluate our approach, and then re-evaluate all other models for a fair comparison. Furthermore, the presentation times for different neural recordings might have been different, meaning that different low-pass filters would be optimal for different datasets. Hence, we leave an analysis the effect test-time low-pass filtering on neural alignment to future work.
made possible by the Brain-Score
Longer Exposure Times. Another logical question is how test-time low-pate tow-pass filtering affects model's EC to humans at longer presentation times, when the CSF looks more like band-pass filter with peak degrees of visual angle rather than low-pass filter. However, evaluating

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Figure 6: Pareto Frontier of MyH solutions. Left: We plot error consistency against OOD OcD-accuracy. and delineate the region of optimal achievable benchmark performance. We plot the performance of various standard models as reference points, as well as Imagen (blue point) and average human performance (green x). Our CLIP ViT-H-14 with prepended low-pass filter achieves performance closest to humans and improves over Imagen on both axes. Right: Empirical confirmation of this result. Models from OpenCLIP tend achieve the highest error consistency when their OOD) accuracy coincides with that of humans (dashed red line).
EC problematic at longer presentation times, for practical reasons. Human observers will make fewer mistakes on MvH, leading to the ceiling performance issues discussed in Klein et al. (2025). Furthermore, human error patterns themselves become more erratic, as random mistakes caused by attentional blips or motor noise dominate the errors. Another issue would be that at presentation times excess of 200 mss humans will make multiple saccades, moving the experiment away from the domain of perception into the domain of visual reasoning, presumably leading to different error patterns. We thus do not explore this avenue here.
CONCLUSION
In this work, we explained the high error consistency observed for the Imagen- modell by its resizing operation rather than its generative objective. This resizing amounts to low-pass filtering of input images at test-time, in contrast to other works likeJJang & Tong (2024) who achieved modest EC gains by low-pass filtering at train-time. We showed that by simply prepending discriminative classifiers with an appropriate low-pass filter, we can achieve even higher behavioral alignment, an effect which generalizes to the majority of tested models. We then demonstrated that low-pass filter is indeed optimal by actively optimizing filter for error consistency Fourier space, resulting low-pass filter.
We offer a potential explanation for this effect: By approximating the human contrast sensitivity function (CSF) short presentation times, ideal low-pass filter approximates how the human visual cortex receives its input. Incoming light diffracted by the optics of the eye and subjected to initial neural transformations, presumably in the retina and the lateral geniculate nucleus (LGN). Various attempts have been made to train DNNs on a diet of (initially) blurred images (Vogelsang al. 2018 Jang & Tong 2024 Lu et al. 2025). but results suggest that if one matches blurring filter to the human visual system, re-training of models might not be necessary to achieve improved behavioral alignment humans. This intuitively makes sense, since training models on low-pass filtered images might render the model less susceptible to such corruptions, leading to changed error patterns. Using the contrast sensitivity function as a filter establishes new state-of-the art error consistency on the model-vs-humaa benchmark. To contextualize this result and answer the question of how much room for improvement is left, we compute the frontier of pareto-optimal solutions to the benchmark, which was formerly unknown. The frontier reveals that further performance gains possible, even though they would have to exceed the noise ceiling of the benchmark. Together, these results imply that generative objectives may in fact not be necessary achieve higher alignment to human observers, with implications about the role of generative, "top-down" structures in the human visual system.
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6 REPRODUCIBILITY STATEMENT
We are committed to ensuring the reproducibility of our work. All code required to reproduce the experiments is provided in the supplementary material. Detailed proofs of all theoretical results included in the appendix. Experimental settings and hyperparameters are described in the main paper and supplementary sections.
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A APPENDIX
A.1 ILLUSTRATIONS OF TRANSFORMATIONS AND FILTERS
Clean
Blur (.=0.5)
Blur [o=1)
Blur (o= 1.5)
Blur (a= 2)
Blur (ar= 3)
Blur (o==
224
Figure 7: Illustration of blurring and resizing filters. Best viewed screen. Evidently, the effects of low-pass filtering (implemented as convolution with Gaussian kernel) and resizing with bi-cubic interpolation very similar: Resizing is form of low-pat tow-pass filtering.
original image
optimal
(sigma=2.55
Figure 8: Illustration of the learned optimal filter. Best viewed screen, We compare the effect of our learned optimal filter to that of the optimal Gaussian (a 2.5), which is visually indistinguishable. and plot the absolute delta between the resulting images show that there is indeed a difference.
112
-112
-122
112
133
132
112
132
112
113
0 Epoch
312
Epoch
Epoch
Figure 9: Progression of filter training, Best viewed on screen. We visualize the intermediate results of learning the optimal filter, analogous to Figure 3] The best filter is found at training epoch 97. Evidently, the diagonal frequencies decay quickly and equally in all directions, while the horizontal and vertical frequencies take longer, but eventually subside well.
A.2 MODELS OF HUMAN CONTRAST SENSITIVITY
The influence the optics of the human eye on the light received at the retina is described by the modulation transfer function (MTF) (Campbell & Green 1965] Williams et al.) 1994). The MTF describes how well an optical system such as the eye transfers contrast from the object to the retinal image at different spatial frequencies, before any neural processing takes place. The losses caused by
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100 80
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0
Cutoff frequency of high-pass filter [cycles per image]
High pass filtering. We test the effect of increasingly aggressive high-pass filters (i.e. decreasing cut-off frequency) accuracy drops for these filters, resulting in low error consistency values. The highest frequency contained in the image given by /2 112 158.39, so we test values between this maximum value and 10 cycles per image.
Figure 10: Ablation:
consistency humans. We observe quite drastic
low photoreceptor density on the retina and other downstream effects are incorporated in the contrast sensitivity function (CSF), but the MTF, We compare the MTF the Gaussian filter with optimal еπrоr consistency in Figure II While the fit may not seem great at first glance, note that the spectrum of natural images is typically f-1. so we weight the frequencies by their contribution overall power when evaluating goodness of fit.
We use the empirical estimate of the MTF obtained by Williams al. (1994), who measured the MTF using interferometry. Their MTF models the eye as diffraction limited system, resulting in the formula
M(Ss, sp) = ((fs,80)(m) + w2e w2)
where fa is the spatial frequency and so is a constant depending on pupil size (we assume a mm pupil size for 87.2 epd). The parameters W1, W2 and obtained by fitting this curve to empirical data. D(fs, so) describes the modulation transfer of such diffraction fifrrattio--lmiteed system as
D(S,, so)
π
80
80
For the CSF, the model by Kelly (1979), who model contrast sensitivity function of spatial frequency and retinal velocity (i.e. how fast the image moves across the retina):
G(ferv) ko amas
(6)
where fa is again the spatial frequency in cpd, the retinal velocity of the stimulus in degrees per second, while k 6.1 + 7.3]log] ())())) and Smaz obtain an expression that gives the contrast sensitivity as a function of spatial and temporal frequency, SSSs, fe), by noting that retinal velocity and temporal frequency related: ft/fs We refer
45.9 o42 are scale parameters. From G we can
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10-4
14
Optics
2.00
19
pass
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2.0
Morubterr Thardl
1.50
0.6
WRASE
1,25
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02
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degrew)
(px]
Figure 11: Low-pass filters approximate the MTF relatively well for low frequencies. Right: The spectrum of the MTF and our optimal Gaussian filter. Left: The goodness of fit between Gaussian filters of varying a and the MTF, calculated as power-weighted RMSE. Both analogous to Figure 4
the interested reader to the original papers for details and motivation of these models. (Note that fa 0 the function is we manually set the DC component of the filter 1.0 because we are concerned with contrast sensitivity, while the DC component gives the mean luminance.)
Matters are complicated by the fact that Kelly 1979) was concerned with stimuli of pure sinusoidal temporal frequency, that is, flickering or moving gratings. However, the stimuli in MvH are presented with hard temporal stimulus on- and offset, i.e. a boxear function time. The Fourier transformation of boxcar function of duration T is given by the sine function: All temporal frequencies fo contribute with coefficients given by sinc(T fiT',, so the boxcar-CSF Shoz for spatial frequency fs and duration T is given by
1f S(S, S.) sine ( (TT) dfc 1/2
Sooz (S..
The appropriate CSF in the context of MvH is thus Sbor (Sa, 0.2).
crucial step for using these formulas to construct the appropriate 2D filters is that the input scales need to be considered: Both MTF and CSF as formulated above expect frequencies in cycles per degree, while the DFT in our implementation is defined in terms of cycles per image. To take this into account, specify sampling rate of pixels, covering 3 degrees of the visual field of human observers in MvH) thus matching the inputs appropriately.
256 (because the images were presented at size of 256 256
3
A.3 COMPUTING THE PARETO FRONTIER OF MODEL-VS-HUMA.
For detailed explanation of error consistency, we refer the interested reader to Klein et al. (2025), but we will summarize the relevant details here. The model-vs-human benchmark consists of 17 corruptions (e.g. inverted colors, eidolon noise, etc) with different 46 different conditions arising due to varying corruption strengths. Within every condition
nodel-vs-human, the error consistency is calculated via Cohen's x (Cohen 1960) over binary vectors indicating whether a trial was solved correctly or not:
Pobs
Peap
K
(8)
Peap
Here, Pots is the observed consistency, i.e. the number of trials which both observers agreed by either both responding correctly, or by both responding incorreetly, Peep is the agreement expected by chance: Let a₁ and ag be the accuracies of the two classifiers, i.e. model and human subject. Then, Pezp = a1 42 + (1 a1) - (1- 02) (i.e. Pexp is the agreement expected by chance, assuming independent binomial observers).
model's error consistency to humans within a condition defined as its average (pairwise) error consistency to each of the human observers. Its overall error consistency is then calculated by
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hierarchical averaging: First across the conditions of comuppiin,, then across corruptions. The inter-human error consistencies are calculated in the same way, with only a subtle difference: Each humans. The green in Figure i the
of the humans is only compared to the remaining (hierarchical) average over these inter-human values. Hence, it is possible to achieve error consistency to humans exceeding the average inter-human error consistency. For an intuitive example, consider a human who only gave correct responses and another human who only gave incorrect responses. By responding correctly half of all trials, model would be more consistent both humans than they are to each other. As the OOD accuracy of model approaches 100%, its emror consistency to humans necessarily approaches 0, because the bounds on k get "squished" (compare Figure from Klein et al. 2025
Our goal is to find the maximum attainable average k: to the human observers. Let {0, 1 1)>* hold the fixed binary correctness sequences for this condition, with each observer heing one row of T, and each image column, Let E {0, 1)M be the response vector that we get to optimize, so that becomes maximal. The key insigh is that can be expressed
2(r; pqj)
Ky(s)
(9)
p 9j 2pqj
where
p = 5/ Ei 8y is the accuracy of the ideal model
4j \ Tig is the accuracy of human j
Tj = + Σ, s,T is the proportion of positive agreement between them.
We arrive Fq. (9) via the following derivation:
Pobs
((s = =)))
P(s = 1, =1) P(s P(s= 0,T; =0)
= =r,+ + P(s- 000=0,,7 =0,T; =))
=ry + (1s=1) -
= 1) -1)-
= 1,T= =
= =1 43 +r
Similarly, Peup p q3 + 2pqj. By using these expressions in Eq. (8) we obtain Eq. (9)
Conveniently, for fixed p n the denominator Dy(p) p Uj 2pq, - q + 24, is
constant with regard to the individual s. The numerator of Eq. (9) is thus linear function of under the cardinality constraint Σ, s; = This linear objective has weights (p))), given by
T3 4 j=1 D,(p)
(x(p) = Σ
(10)
The optimal binary sequence for fixed & can therefore be obtained by setting 8; 1 for the top k items according to (p) and s; = 0 for the others. The global optimum can then be obtained by simply sweeping & from 0 N. obtaining the optimum for each value of k. and selecting the overall best k.
Since the model-vs-human benchmark consists of multiple such conditions, this procedure needs to be applied to each condition independently, and results need to be aggregated using the hierarchical averaging employed by the benchmark: First, one has to average over all conditions within one experiment, before then averaging over all experiments. This procedure yields the maximum possible error consistency and the sequence of ideal responses, from which the accuracy can be obtained by following the same hierarchical averaging logic. Thus, has computed the lower right point on the pareto-fronticr in Figure
'The following derivation, while carefully verified by the second author, was obtained with heavy L.M support, discussed in Appendix A.6
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Published as a conference paper at ICLR 2026
The rest of the pareto- prreto-frontier be constructed by iteratively expanding the frontier. Note that for every condition the experiment, we have a list of tuples (M4, (s).ps). From this list, we can remove all dominated elements for which there is another element so that (xy Ky)) and (pj S p:), because these clearly cannot be part of a non-dominated solution. Next, we initialize the set of non-dominated solutions So O). Then, for each condition i:
1. Let Oj be the set of non-dominated elements in that condition.
2. Expand S;-1 by forming all possible combinations: S; {(s.)) p) + (k',p') (K,P)
St-1, (R', p') E O.).
3. Prune S3 by removing all dominated solutions.
To account for the fact that each condition contributes with different weight to the overall average (because the number of conditions C per experiment varies), we first weight each condition' 's elements with the appropriate factor c. The worst-case runtime of this algorithm is exponential, but the small scale required for model-vs-human we could calculate the full pareto-frontier on consumer hardware in less than minutes.
A.4 MODEL DETAILS
We present an overview of the models used in this work in Table 2
model
architecture
ResNet
He et al.
2016)
ResNet101
SWSL
Yalniz
2019
ResNeXt-101
BiT-M Kolesmikov èt al. 2020)
ResNet-101x/
VIT
Dosovitskay et al.
2200
ViT-L-16 (INIK and IN21K)
Noisy Student (Xie et a.
2020
EfficientNet-L.2
OpenCLIP (Iharcoo al.
2021
ViT-B-32
ViT-B-16
ViT-L-14
ViT-H-14
ViT-g-14
ViT-G-14
ConvNext-L.
Table Architectures and the color coding used for each model type. We adopt these from Geirhos al. (202I).
A.5 DETAILED RESULTS
Here, we provide additional results of our analyses. In Figure 12 we show confidence intervals for the measured error consistency values of CLIP ViT-H- 14 prepended with the optimal low-pass filter and our learned filter, as well as inter-human error consistency. Evidently, we almost close the gap to human observers.
A.6
LLM USAGE
The first authors of this submission use IDEs with built-in LLM support, so LLMs have been used to help with menial coding tasks. Beyond that, the authors have used GPassooo aid with verifying code or high-level approaches to problems in ways that we deem uncontroversial, with one exception: To err on the side of caution, the authors take no credit for Eq. and (10) While we have carefully verified that this solution is indeed correct, Chat-GPT5-Prr derived this result autonomously when prompted to do so with a detailed problem description and definition of error consistency. The idea of searching for the mareto-frontier in the first place (which deem the more important intellectual contribution than the algebraic manipulation) is fully our own. LLM tools have than would use thesaurus dictionary.
also been used only very sparingly in writing,
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Published as a conference paper at ICLR 2026
0.50
Error Consistency to Humans
0.45
0.40
0.35
0.30
0.25
0.20
Humans
VIT optimized Models
VTT blurred
VIT standard
Figure 12: Error Consistency to humans. We plot the consistency to human observers as measured by the model-vs-hume benchmark for a vanilla OpenCLIP ViT-H-14 as well as the same model with prepended low-pass filter 2.5) and filter learned in Fourier space. Confidence intervals are obtained via bootstrapping as described in Klein et al. (2025). See Figure 14) for breakdown by corruption.
0.40
OpenCUPP-IT-8-32 (151M)
OpenCLI-VITB-166 (177M)
0.38
OpenCLI-VITLL144 (428M)
OpenCUP-VT-H-144 (986M)
0.36
OpenCUPAVT-G-14 (1367M)
OpenCUP-VIT-g-14 (1367M)
Error Consistancy [x]
0.34
0.30
0.28
0.26
0.0
0.5
1.0
1.5
2.5
3.0
3.5
low-pass filter [px]
function of model size. We plot the enror consistency achieved for certain filter a, for each OpenCLIP ViT size in terms of parameter count. Evidently, the optimal or is fairly robust to the model size, and tends to increase a bit for larger models / smaller patch sizes.
Figure 13: Optimal
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Published as a conference paper at ICLR 2026
0.6
Experiment
colour
0.5
contrast
cue-canflict
Error Consistency to Humans
edge
eldoloni
eidolonil
eidolonilI
false-colour
high-pass
low-pass
0.3
phase-scrambling
power-equallsation
rotation
silhouette
0.2
sketch
stylized
uniform-noise
0.1
VIT standard
VIT blurred Models
VT optimized
Figure 14: Breakdown of EC gains by corruption. We plot the error consistency to human participants that every model achieves, broken down by corruption. Evidently, the EC gains are consistently achieved across all corruptions.
21


Reviews:
[{"review_id": "paJhP7Us2S", "reviewer": "Reviewer_5YdF", "summary": "This paper revisits the source of human-like behavior in modern vision models. Building on prior work suggesting that generative objectives (e.g., Imagen) yield superior behavioral alignment with humans, the authors demonstrate instead that the key factor is low-pass filtering, specifically, the effective downsampling to 64×64 resolution used in Imagen. Through extensive experiments across multiple architectures (ResNet, BiT-M, EfficientNet, OpenCLIP ViTs), they show that removing high-frequency content at test time (via Gaussian blur, resizing, or learned Fourier filtering) consistently: a) Raises shape bias (SB) toward human levels; b) Boosts error consistency (EC) beyond Imagen’s previous record (κ = 0.37 vs. 0.31); c) Only modestly reduces OOD accuracy (≈3–6 p.p.), revealing a clear accuracy–consistency trade-off curve.\n\nThe authors also derive a learned Fourier filter that optimizes EC directly, finding it converges to a low-pass profile nearly identical to the analytical Gaussian filter. Finally, they analyze the model-vs-human benchmark’s Pareto frontier, establishing the theoretical ceiling for behavioral alignment and showing that current models are still below this limit.", "questions": "Have you tested whether low-pass filtering also improves alignment on neural datasets (e.g., Brain-Score or fMRI RDMs)?\n\nHow sensitive is the optimal σ to model scale and patch size?\n\nCould low-pass filtering be integrated during training to recover the lost OOD accuracy?\n\nIs the Pareto frontier analysis robust to the noisy EC estimates reported by Klein et al. (2025)?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "The paper is well-organized and well-written, with smooth narrative flow from hypothesis to methods to results. I think the paper provides a simple yet powerful reinterpretation of why generative vision models appear more human-like,  reframing a potentially deep theoretical question (generative vs. discriminative objectives) as a signal-processing issue (frequency content). As well, The authors test across numerous architectures, including large-scale CLIP models, and show strong generalization of the low-pass effect. I think they do a great job by providing a theoretical  link to the contrast sensitivity function (CSF) of human vision provides a compelling neurophysiological explanation, integrating psychophysical and computational perspectives.", "weaknesses": "The psychophysical alignment argument can  be strengthen  by testing on neural benchmarks (e.g., Brain-Score, Algonauts) to verify that low-pass filtering also aligns representational geometry with biological vision.\n\nAlso a control, may include testing under longer exposure times or more naturalistic conditions which could  clarify whether the low-pass benefit is tied to the MvH’s 200 ms presentation constraint or generalizes across temporal regimes.\n\nWhile the learned Fourier filter converges to a low-pass profile, visualizing intermediate optimization stages could reveal whether specific spatial frequencies carry distinctive importance for behavioral alignment.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "51Dz91AgFD", "reviewer": "Reviewer_mKdF", "summary": "The paper claims that recent improvements in human–model agreement for vision systems mostly come from an overlooked preprocessing step that removes high-frequency detail. The authors show that applying a simple low-pass filter to images at test time makes standard discriminative models behave more like humans on a benchmark of human vs model errors. They confirm this in three ways. First, simple blur or downsampling increases human-like behavior without retraining. Second, a learned frequency filter that is optimized for alignment ends up looking like a low-pass filter. Third, a filter shaped by the human contrast sensitivity function produces similar gains, which links the effect to known properties of early vision. The paper also maps the trade-off between accuracy and alignment to clarify what levels of agreement are realistically achievable.", "questions": "- Do the gains persist on other human datasets or tasks (e.g., free-viewing, longer exposures)?\n- If you re-fit the Fourier filter on a subset of MvH and evaluate on held-out conditions, does it still outperform simple Gaussian/CSF filters?\n\n\nIn general, I enjoyed reading the paper, which was well written and interesting. I would strongly encourage acceptance once my minor comments have been answered.", "limitations": "", "rating": 8, "confidence": 3, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "- The writing is clear, the narrative is well structured, figures effectively support the claims. \n- The paper offers a simple, general, physiologically grounded intervention with immediate practical impact: prepend a low-pass filter at test time to improve human alignment without retraining.\n- Reinterprets prior SOTA claims by pinning gains to an overlooked preprocessing step. The Pareto-frontier view of MvH is insightful.\n- Broad cross-model evaluation; consistent trends; convergence of three lines of evidence", "weaknesses": "- Potential overfitting. The learned Fourier filter is optimized on MvH without a held-out set. But it makes sense in the context.\n- l.290: Please redefine EC ans SB because it requires coming back to previous section to find their meaning.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "sK0qkSyOw4", "reviewer": "Reviewer_tqZJ", "summary": "The authors investigate how simple down-sampling or low-pass filtering operations increase the alignment between *artificial vision models* (either discriminative, bottom-up, or generative, top-down) and *human vision* in terms of error consistency, shape bias, and out-of-distribution accuracy. They find that low-pass filtering similar to the human Contrast Sensitivity Function is optimal to maximize error consistency with humans and to get similar shape-bias in image recognition.  This finding is important in the discussion about the appropriate strategy (discriminative or generative) for object recognition both in humans and machines.", "questions": "(a) Regarding the notation question, consider that \\sigma is used for different things: the blur extent, the soft-max function, and the noise used to initialize the all-pass filter to be optimized. Moreover, in Eq.1, I understand that b_{\\sigma} is (or should be?) the filter, G_{\\theta}, -as in Eq.3-, no?. Additionally, the parameters of b_{\\sigma} are H_{\\theta}?... is this H an entropy? or is it a generic way to call the parameters? (H is certainly a cross-entropy in Eq.2, but H in Eq.1 is confusing -to me-). I get the ideas and they are fine, but notation is confusing (to me).   \n\n(b) Regarding related work, Li et al. 22 ( https://jov.arvojournals.org/article.aspx?articleid=2778843 ) and Akbarinia et al. 23 ( https://doi.org/10.1016/j.neunet.2023.04.032 ) show that this kind of bottleneck can also emerge when solving certain tasks implemented by certain (simple) architectures, but does not emerge in other (too deep) architectures.\n\n(c) Please clarify the discussion about whether the low-pass filtering is (or is not) required in the training of the models to increase the alignment with humans.\n\n(d) Incidentally (but this is a criticism/question to Jaini at al. 23 not to this work ;-) I think that the increased shape-bias found in their generative classification may not be surprising because the noise added to launch the inverse in the difussion process in Jaini et al. destroys the texture in the image, so naturally, the classification (finally done in the spatial domain) has to be more driven by shape. Could the authors elaborate on this?", "limitations": "", "rating": 8, "confidence": 5, "soundness": 4, "presentation": 4, "contribution": 4, "strengths": "The (apparently) simple experiments proposed here, removing high-frequency information either by down-sampling or by low-pass filtering the input images before feeding the models, actually addresses a *key discussion* in machine vision: is it better a bottom-up approach to analyze the images and then discriminate between classes? (as done in conventional discriminative classifiers), or is it better a top-down approach where one checks if the input is compatible to generated examples form a given class? (as done in the \"generative\" classifiers as the ones defined by Jaini et al. 23, ICLR 24). This question is also relevant in understanding human vision where both strategies are certainly applied (though it is not clear how).\n\nThe results presented here constitute an excellent counter-example that allows to tone-down the conjectures about the benefits of the generative approach exposed in the ICLR 24 spotlight paper of Jaini et al. 23. While Jaini et al. suggest that their system may be \"more human\" (in shape-bias and error consistency) because of the generative approach, this work shows that this similarity may be just due to the fact that the considered models work with down-sampled images, thus effectively giving the model a signal which passed through a bottleneck similar to the one happening at the human LGN. As a result, as the signal content given to the model approaches the content arriving to the human visual cortex, it is reasonable that (1) models working with such filtered inputs have a generally \"more human\" behavior, and (2) those models focus on shape rather than texture because the texture has been largely attenuated by the CSF-like filter.\n\nThis counter-example reasoning is nice, results confirm that the simple low-pass operation alone does the alignment job for discriminative models, and show that the optimal filter resembles the human filter.  \n\nFinally, the scientific question-explanation logic of the work is very well posed, which (I think) is unusual in certain ICLR papers: the authors first state an important question in a compelling way and propose a simple hypothesis as an alternative explanation. Then, they experimentally show that this alternative may be true, thus questioning recent work, and motivate further research.", "weaknesses": "Weaknesses in this work are minor, mainly limited to (a) some notation issues, (b) mention to works that show the emergence of human-like Contrast Sensitivity in artificial nets, and (c) clarification of the discussion between using the low-pass in training or test time.\n\n(a) Notation in Eqs. 1-3 can be more clear. I eleborate in the \"questions\" box below.\n\n(b) In the Related Work section the authors mention the work of Subramanian et al. 23 on the frequency response of ANNs. Other works (e.g. Li et al. J.Vision 2022 and Akbarinia et al. Neural Nets. 23) specifically adress the issue of the emergence of Contrast Sensitivities in ANNs which may be similar or different(!) to the CSF of humans depending on the task and on the architecture indicating a non-trivial interplay between these issues.\n\n(c) Finally, the discussion about whether the low-pass filtering is (or is not) required in the training of the models to increase the alignment with humans is not clear (to me). The presented experiments certainly show that using it in test time increases the alignment with humans, but it is not clear (to me) the behavior one could get with training based on low-pass filtered images (as is the case in infants with blurred vision and with the built-in CSF-like LGN bottleneck). May be this issue should be further clarified.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "2oYIygORGx", "reviewer": "Reviewer_V1fP", "summary": "This paper demonstrates that applying low-pass filters (gaussian blur and resampling) during test time improves model alignment with human perception in the context of the model-vs-human (Geirhos et al., 2021) benchmark framework. The authors claim that this result is further explained by established models of human visual sensitivity in the context of fast presentation times. To support this framework, an optimal filter that maximizes alignment is also learned and was shown to be, qualitatively, a low-pass filter.", "questions": "The authors do not discuss how this result compares with other test-time pre-processing methods. Does previous work exist? What about a set of experiments that benchmarks various corruptions used in OOD-accuracy experiments from MvH (e.g. uniform noise, low contrast, etc.)\n\nI recommend a key control experiment which would be to apply varying levels of high pass filtering as a test-time pre-processing manipulation. \n\nWhile a human psychophysics experiment may be difficult to do during the rebuttal period, testing a subset of the images in humans with longer presentation time with and without blur would be the key experiment to determine if it is indeed the magno stream behind the human baseline.\n\nAre the authors referring to the magno/parvo streams when they discuss the relationship to human perception, and claiming that the magno stream with it's low spatial frequency sensitivity is behind this low-pass-filtered effect described?\n\nMinor Points:\nA “neural transfer function” for short presentation times is mentioned in line 069 without prior introduction. Is this essentially the CSF?\n\nEquation 1 has a $H_\\theta$ that should probably be $G_\\theta$, the learned filter\n\nEquation 3 should have “df” to end both integrals on the numerator and denominator rather than being after the fraction.\n\nOn figure 3, there should be axis labels and the standard deviation of the learned filter (either in Fourier or real space) should be reported and compared against the gaussian blur filters. Furthermore, it could be helpful to show images filtered by this filter.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "The work addresses a topical problem of human-model alignment, specifically in an overlooked area of alignment, error consistency.\n\nIt is a particularly simple and elegant hypothesis and result that a simple low-pass filter can improve error consistency, and as far as I know, this is a novel result.\n\nThe manuscript is well written and communicated; the authors do a good job at leading the reader from the motivation, background to their results and conclusions\n\nPrior work on human visual processing well covered and incorporated into the work; the authors cite well known, tested, and accepted results from the human vision literature.\n\nWhile the experiment of blurring images is generally simple, the authors explore many different methods and levels of blur, and the results demonstrating a peak of error consistency for a given blur is interesting.", "weaknesses": "The majority of the weaknesses of the paper comes from the connection to human vision. While well cited, there are many shaky logical steps to support the claim that this improvement in error consistency with human data is because of a match in a low-pass-filtered image to the human percept of an image when viewed for only 200ms. Generally, I feel the result of improvement in alignment in itself with just a simple blur is interesting, and the shaky claims that this blur amount is a match to the effective blur of human vision in the context of a short presentation time detract from this.\n\nThe largest example of this is a major discrepancy between Kelly’s work on contrast sensitivity to spatiotemporal frequency and limited presentation time of images. Specifically, in Kelly’s work, two types of stimuli were presented, a flickering grating of the form\n$$\nf_S(x, t) =\\cos\\alpha x \\cos \\omega t \n$$\nAnd a moving grating of the form\n$$\nf_T(x, t) = cos \\alpha (x - v t)\n$$\nIn both cases, the time dependencies are sinusoidal with a precise frequency whereas in the case of limited presentation of stimuli, the time dependency is a boxcar function (with a Fourier transform of a sinc function which has a peak, in fact, at f = 0). It is erroneous to take the inverse presentation time to be the frequency. \nWhile Kelly's CSF might still be the best model available, the authors should at the very least acknowledge this discrepancy.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)