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REVIEW 4 major objections 8 minor 142 references

A Turing Test for Artificial Nets devoted to model Human Vision

T0 review · 4 major / 8 minor · reviewed 2026-08-09 · deepseek-v4-flash

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Pith's one-line read A compact set of ten low-level vision facts can separate artificial vision networks by how closely they mimic human early vision.

desk verdict The Decalogue is a genuinely useful low-level benchmark for vision models, but the paper's specific ranking claim rests on an unvalidated Euclidean read-out and should be treated as illustrative, not established. read the letter →

arxiv 2502.00721 v3 pith:TP3CDSVM submitted 2025-02-02 q-bio.NC

Jorge Vila-Tomás , Pablo Hernández-Cámara , Qiang Li , Valero Laparra , Jesús Malo This is my paper · ORCID
classification q-bio.NC
keywords low-levelvisionTuringtestpsychophysicsreceptivefieldsdivisivenormalizationcontrastmaskingmodelevaluationspatio-chromatic
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper argues that claims about deep networks modeling human vision remain unproven for low-level vision, and proposes a direct check: a fixed set of ten calibrated stimulus series, each tied to a documented human visual fact, that any image-computable network can be fed. The test covers spectral sensitivities, brightness and color saturation, contrast sensitivity functions, receptive-field shapes, and nonlinear behaviors such as saturation, crispening, energy masking, frequency masking, and orientation masking. Applied to three networks of similar architecture, the test ranks a parametric model whose nonlinearities were psychophysically tuned as the most human-like, with the quality-estimation network second and the segmentation network third. The paper's methodological claim is that even this qualitative application of the test is enough to produce a meaningful ranking and to point at specific architectural corrections.

What carries the argument

The load-bearing machinery is the Decalogue: ten sets of calibrated digital stimuli that probe the retina-V1 information bottleneck, each standing for a documented human visual fact. The read-out is deliberately simple—the Euclidean departure between a model's response to a test pattern on a background and its response to the isolated background—and the comparison is made against either standardized human sensitivity surfaces or qualitative curve shapes, namely saturating responses that shift monotonically with background energy, frequency, or orientation. The nonlinear behaviors the test targets are classically produced by divisive normalization, a gain-control operation that divides each neuron's response by a pooled measure of neighboring activity, and the top-ranked model uses parametric versions of that operation tuned by the Maximum Differentiation method, a psychophysical procedure that searches for stimulus directions where model behavior differs most.

What would settle it

Re-run the same three-model comparison with alternative read-outs, for example a linear classifier probe at the same layers or a Minkowski distance with exponent other than 2, and check whether the model ordering changes; the paper's own discussion of read-out choice implies it could. A more direct test would measure human detection thresholds on the exact Decalogue stimuli and check whether the Euclidean read-out predicts those thresholds before relying on the qualitative curves.

Watch

Extended reading notes

Core claim

On the paper's own terms, the central discovery is that a low-level Turing test built from ten classical retina-V1 facts separates three deep networks in a way that tracks their training histories, and that the model with psychophysically tuned divisive-normalization nonlinearities comes out closest to human behavior. The advantage is most visible in nonlinear spatio-chromatic responses: the leading model reproduces saturating contrast responses, the correct frequency ordering, and the right direction of masking by energy, frequency, and orientation, while the other two show expansive or inverted curves, wrong chromatic scaling, and masking that increases rather than decreases visibility. The paper is careful to note that even the leading model fails some feature-interaction facts, such as color induction and some texture-masking details, and it treats these failures as diagnostics for where its parameters should change. It also stresses that read-out location matters, showing that in the parametric model the spectral sensitivities are human-like at the first layer but degrade at deeper layers.

Load-bearing premise

The test's rankings depend on treating the Euclidean distance between a model's response to a test on a background and its response to the isolated background as a faithful measure of how visible the test is to a human, and the paper does not validate that read-out against human thresholds.

Editorial extensions

If this is right

  • If the test is adopted, a vision network can be screened for low-level biological plausibility without high-level task benchmarks or complicated read-out decoders.
  • The qualitative version of the test is sufficient to separate the three models, so quantitative automation is not a prerequisite for model selection.
  • For the parametric model, failures on feature-interaction facts indicate which divisive-normalization kernels to adjust, making the test diagnostic rather than merely evaluative.
  • The same stimuli can serve as fine-tuning targets after conventional training, so standard goals and human-like constraints can be alternated during optimization.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Since the paper itself grants that read-out choice is arbitrary and can change rankings, a stronger version of the test would report the model ordering under several read-outs, such as classifier probes and non-Euclidean metrics.
  • A natural quantitative extension would assign weights to the ten facts and collapse the qualitative scores into one human-alignment index that could be used as a training loss.
  • One could also measure human detection or discrimination thresholds for the exact Decalogue stimuli and test directly whether the Euclidean read-out is the right proxy, rather than taking it as given.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 8 minor

Summary. The paper proposes a low-level 'Turing test' for image-computable models of human vision: a Decalogue of ten psychophysical and physiological facts about color, contrast sensitivity, receptive fields, and nonlinear masking, together with calibrated stimuli and code to generate them. To illustrate the test, the authors apply it to three deep models: BioMultiLayer, a parameterized cascade of linear-nonlinear stages; PerceptNet, an end-to-end trained quality model with divisive normalization; and Bio U-Net, a segmentation network with the same encoder style as PerceptNet. Using a Euclidean-distance read-out of network responses and qualitative visual comparison of response curves, the paper reports that BioMultiLayer behaves most like humans, especially on nonlinear spatio-chromatic responses, and that 'even with this qualitative application ... a significant ranking is possible.' The paper also argues that the test can suggest architectural or training changes for non-human-like models.

Significance. If the proposed test were validated, it would fill a real gap: existing benchmarks such as BrainScore focus on high-level object recognition, while low-level vision facts have not been systematically compiled for image-computable model evaluation. The paper's strengths are its concrete, reproducible stimuli, the public code, and the honest framing of the evaluation as qualitative, including an explicit warning in Section 2.2 that read-out choices are arbitrary. The authors also show that the test can expose qualitative divergences among models of similar architecture, which is a useful diagnostic demonstration. However, the central quantitative claim—that the test ranks models by human alignment—rests on an unvalidated read-out and on subjective scores in Table 2; the demonstration is illustrative rather than confirmatory.

major comments (4)
  1. [Section 4.1] The read-out for visibility is the Euclidean departure between the response to test+background and the response to the isolated background, but Section 2.2 explicitly states that 'the selection of the read-out mechanism is not trivial' and that 'these (arbitrary) decisions definitely affect the characterization of the system, e.g. its frequency response.' No validation is given that Euclidean distance in the activation space of these particular networks is monotonically related to human visibility or discriminability; there is no noise model, no threshold criterion, and no calibration against human psychometric functions. Since every curve in Figures 10-12 and every entry in Table 2 depends on this single choice, the ranking of the three models is not supported unless the read-out is validated or the results are shown to be robust across a family of read-outs (e.g., different norms, Minkowski exponents, or linear classifiers).
  2. [Table 2 and Section 4.2.5] The claim that 'even with this qualitative application of the proposed test ... a significant ranking is possible' is not operationalized: the symbols in Table 2 are assigned by the authors with no defined scoring rubric, no inter-rater reliability, and no statistical test. The word 'significant' appears to mean 'clear to the authors' rather than a statistically supported statement. To make the ranking reproducible, the authors should either (a) define a quantitative distance between model response curves and reference human curves (e.g., a goodness-of-fit measure with tolerance bands), (b) report the sensitivity of the ranking to plausible variations in the qualitative judgments, or (c) explicitly restrict the claim to an illustrative demonstration rather than a ranking.
  3. [Section 4.1 and Section 4.2.5] The evaluation is partly circular for the BioMultiLayer model: its linear parts were built to contain the tested spectral sensitivities and Center-surround/Gabor receptive fields, and its nonlinearities were tuned on contrast masking and distortion data that overlap with the Decalogue (stages 1 and 4 tuned to reproduce distortion and contrast masking; stages 2 and 3 tuned via Maximum Differentiation). The paper acknowledges the linear part of this issue in Section 4.2.5, but the same concern applies to the nonlinear facts: if a model was fit to masking phenomena, observing masking in that model is not an independent success. The authors should explicitly separate facts used in model design/tuning from held-out facts and present the ranking separately for fitted versus emergent properties.
  4. [Figures 10-12 and Section 4.2] The comparative claims rest entirely on single realizations of the models, without sensitivity analysis. For the two trained networks, random initializations, training data, or choice of read-out layer can materially change CSFs and masking behavior; the paper reports no variation across seeds or layers. Even for the deterministic BioMultiLayer, the parameter values are the result of a fitting procedure, and no bootstrap or perturbation analysis is shown. Without such robustness checks, the observed differences between models cannot be distinguished from incidental implementation choices.
minor comments (8)
  1. [Section 1.1] In the prologue, 'questionare' should be 'question are' (or 'questionnaire').
  2. [Section 2.1] The sentence 'However, it is not obvious how how these tasks may be combined' contains a duplicated word 'how'.
  3. [Table 1] In the Modality column, the entries for facts 3 through 10 appear as 'Text'; this should be 'Texture'.
  4. [Section 3.2.2] The phrase 'chromati contexts' should read 'chromatic contexts'.
  5. [Section 4.1] The description of the read-out would be clearer if it stated whether the Euclidean distance is computed on raw activation vectors or on some normalized version; the units and scale of activations affect the numerical values and hence the curves in Figures 10-12.
  6. [Acknowledgments] The sentence 'The invited talk ... was funded be the University of Bristol' should say 'funded by'.
  7. [References] Reference [13] is a personal communication with no year or affiliation; please provide a proper citation or remove it.
  8. [Throughout] The model name 'Bio U-Net' appears as 'Bio-UNet' in several places; please use a single consistent spelling.

Circularity Check

2 steps flagged · score 6.0 of 10

The central ranking is partly a report of BioMultiLayer's construction and fitting data: linear facts are built in, and the masking facts were used to tune its nonlinearities.

  1. self definitional [Section 4.2.1 'Spectral sensitivities and color responses (properties 1 and 2)'; Section 4.2.5 'Summary of results']
    "In this network the first linear layer has achromatic and opponent channels defined by construction so the Vλ [84] (subplot A) and the opponent curves of Jameson & Hurvich [106] (subplot B) are trivially obtained. ... It is not surprising that the parametric model (the BioMultiLayer) has bigger alignment in the linear parts (properties 1 and 5) since sensitivities and center-surround and Gabor receptive fields were parametrically built in that model model."

    Property 1 and the receptive-field part of property 5 are not independent checks of human alignment for BioMultiLayer: the achromatic/opponent channels and the center-surround/Gabor kernels are part of the model's definition. The paper's own wording ('trivially obtained', 'parametrically built in') shows that BioMultiLayer's top scores on these facts are restatements of its construction, so those rows of Table 2 do not provide independent evidence that the Decalogue can discriminate models. The abstract's claim that this model is closer 'in terms of receptive fields' is therefore true by construction for the linear part.

  2. fitted input called prediction [Section 4.1 'Networks and experimental setting'; Section 4.2.4 'Energy masking and feature masking']
    "The nonlinearities of the 2nd and 3rd stages of the model were tuned via the psychophysical method of Maximum Differentiation in [32]. And the nonlinear parts of the 1st and 4th stages were tuned to reproduce subjective opinions on distortion and contrast masking facts [33, 34]."

    The Decalogue's nonlinear facts include the contrast-masking behavior on which BioMultiLayer's nonlinearities were fitted: fact 8 is energy masking and facts 9-10 are frequency and orientation cross-masking, while the model's stage 4 is described as 'wavelet analysis and cross-masking between textures' whose nonlinear parts were tuned to 'reproduce subjective opinions on distortion and contrast masking facts.' The paper reports fact 8 as 'qualitatively human' for BioMultiLayer and gives that model the best nonlinear scores in Table 2, so the abstract's emphasis on its 'nonlinear behavior for spatio-chromatic patterns' is in part a report of fit quality, not an independent prediction.

full rationale

The Decalogue itself is an honest compilation of classical low-level facts, and the paper is transparent about BioMultiLayer's constructed nature: it says the linear parts (spectral sensitivities, opponent channels, center-surround and Gabor receptive fields) are built in, and that the nonlinear parts were fitted to distortion and contrast-masking data. That transparency prevents a score of 8-10. However, the central claim that BioMultiLayer is closest to humans, and that the test yields a significant ranking, is partially forced: properties 1 and 5 are true by construction for BioMultiLayer, and its advantage on the nonlinear masking facts reproduces the contrast-masking data used to fit its nonlinearities. The paper does contain independently informative results, such as the CSF scaling from sinusoidal responses ('an original check done here since they were not built in') and the relative ordering of PerceptNet versus Bio-U-Net, so the circularity is partial, not total. Separately, the ranking depends on an unvalidated Euclidean read-out: Section 4.1 defines responses as the 'Euclidean departure between the response to test+background with regard to the response to the isolated background', while Section 2.2 warns that 'the selection of the read-out mechanism is not trivial' and that arbitrary decisions 'definitely affect the characterization of the system.' That is a validity limitation on the ranking rather than a construction-level circularity, but it compounds the two reduction steps above.

Assumptions & free parameters 3 free parameters · 4 assumptions · 0 invented entities

The paper introduces no new physical entities. The free parameters are the fitted parameters of the BioMultiLayer model and the hand-assigned qualitative scores; the evaluation depends on these as well as on the assumed read-out (Euclidean distance) and the assumed adequacy of the Decalogue as a characterization of early vision.

free parameters (3)
  • BioMultiLayer divisive normalization parameters (stages 2 and 3) = not reported here (see Martinez et al. 2018)
    Tuned via Maximum Differentiation to human contrast masking data; these values are reused in the paper's evaluation, so the model's human-like masking behavior is partly a restatement of the fit.
  • BioMultiLayer nonlinearity parameters (stages 1 and 4) = not reported here (see Martinez et al. 2018, 2019)
    Fit to reproduce subjective opinions on distortion and contrast masking facts, which overlap with Decalogue facts 6-10.
  • Qualitative similarity scores in Table 2 = symbols assigned by authors
    The paper's ranking is based on hand-assigned qualitative scores with no quantitative threshold, error bars, or independent evaluation.
assumptions (4)
  • ad hoc to paper Euclidean distance between model responses to test+background and background alone is a valid visibility read-out.
    Adopted in Section 4.1 without validation; the paper itself (Section 2.2) argues read-out choice is non-trivial and can change rankings.
  • domain assumption The selected ten facts characterize the retina-V1 adaptive information bottleneck.
    The paper claims the Decalogue describes the bottleneck; this is a domain assumption drawn from classical vision science, not proven here.
  • domain assumption Human qualitative behavior for non-standardized facts (2, 8, 9, 10) is as described by the authors.
    The human curves are inferred from classical literature and eye inspection, not measured in this paper.
  • ad hoc to paper Feeding 128x128 digital images with assumed 64 cycles/deg sampling is a valid image-computable test for these models.
    Stated in Section 4.1 without sensitivity analysis or justification for each model's native input scale.

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Cite this review

Pith. "Pith review of A Turing Test for Artificial Nets devoted to model Human Vision." pith.science (2026). https://pith.science/paper/TP3CDSVM

@misc{pith2026250200721,
  author       = {Pith},
  title        = {Pith review of: A Turing Test for Artificial Nets devoted to model Human Vision},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/TP3CDSVM}},
  note         = {Machine review of arXiv:2502.00721}
}
read the original abstract

In our invited talk at the AI Evaluation Workshop of the University of Bristol back in June 2022 we argued that, despite claims about successful modeling of the visual brain using ANNs, the problem is far from being solved (even for low-level vision). Open issues include: where should we read from ANNs to reproduce human behavior?, this ad-hoc read-out is part of the brain model or not?, should we use artificial psychophysics or artificial physiology?, artificial experiments should literally match the experiments in humans?. There is a clear need of rigorous procedures for experimental tests for ANNs models of the visual brain, and more generally, to understand ANNs devoted to generic vision tasks. Following our experience in using low-level facts from Visual Neuroscience in Image Processing, we presented the idea of developing a low-level dataset compiling the basic spatio-temporal and chromatic facts that are known to happen in the retina-V1 pathway, and they are not currently available in existing databases such as BrainScore. In our results we checked the behavior of three recently proposed models with similar architecture: (1) A parametric model tuned via Maximum Differentiation [Malo & Simoncelli SPIE 15, Martinez et al. PLOS 18, Martinez et al. Front. Neurosci. 19], (2) A non-parametric model, the PerceptNet, tuned to maximize the correlation with human opinion on subjective distortions [Hepburn et al. IEEE ICIP 20], and (3) A model with the same encoder as PerceptNet, but tuned for segmentation (published later as Hernandez-Camara et al. Patt.Recogn.Lett. 23, Hernandez-Camara et al. Neurocomp. 25). Results on 10 compelling psycho/physio visual facts show that the first model is the one with closer behavior to the humans in terms of receptive fields, but more interestingly, on the nonlinear behavior for spatio-chromatic patterns of a range of luminances and contrasts.

Figures

Figures reproduced from arXiv: 2502.00721 by the authors.

Figure 1
Figure 1. Image Denoising, Image Compression, Image Segmentation and Image Classification architectures with (eventually) biological correlates in the LGN, the V1 and beyond. However, it is not obvious how how these tasks may be combined to explain biological vision. 3 [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Given a deep model successfully trained for some visual task, the read-out location and read-out mechanism (or decoder) is important to assess its biological plausibility. 4 [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. In artificial physiology (left) and in artificial psychophysics (right), the arbitrary decoder to read-out model activations is critical. more sophisticated techniques such as reverse correlation which are used both in physiology [82] and in psychophysics [83], and these are not yet widely used in machine learning. Regarding the experimental setting, should one go for a literal reproduction of the experiments with h… view at source ↗
Figures from the paper (15 more)
Figure 4
Figure 4. Figure 4: In measuring the spectral sensitivity of certain elements of a network one may try a literal reproduction of the human psychophysics (left) or an idealized experiment (right). The literal reproduction could be done through matching experiments [84]: finding the ratio o…
Figure 5
Figure 5. Figure 5: show a series of these stimuli in the (linearly spaced range of luminance [0.5, 120] cd/m2 on (linearly spaced) backgrounds of luminance in the range [1, 160] cd/m2 . These stimuli are easily generated in digital levels (i.e. ready to feed conventional artificial model…
Figure 6
Figure 6. Figure 6: shows colorimetrically calibrated stimuli with such deviations (in the range [-20, 20] of the linear RG and YB tristimulus values of the Jameson and Hurvich space) in different backgrounds, which are easy to generate and modify by using the code provided in this work6 …
Figure 7
Figure 7. Figure 7: shows Gaussian windowed test noise patches of 4 cycles/degree (cpd) in images subtending 1 degree with average luminance of 50 cd/m2 and linearly spaced RMSE contrasts (from left to right) in the range [0, 0.3]. The different rows show the same tests on different backg…
Figure 8
Figure 8. Figure 8: shows 6 cpd horizontal Gabor patches with average luminance of 50 cd/m2 and RMSE contrast increasing linearly from left to right in the range [0, 0.3]. These Gabor patches are shown on top of band-pass noise of contrast 0.2, with the same frequency, but different orien…
Figure 9
Figure 9. Figure 9: The proposed method: feed the model with series of images, compute responses (using the simplest possible read-out mechanism) and make quantitative comparisons with standard sensitivity surfaces or qualitative comparisons checking the nonlinearitiy using different adap…
Figure 10
Figure 10. Figure 10: Spectral sensitivities of the considered models (top) and corresponding responses to luminance and linear deviations from white in the cardinal red-green and yellow-blue directions (bottom). On the one hand, knowledge of the standard spectral sensitivity, the CIE Vλ c…
Figure 5
Figure 5. Figure 5: In the chromatic cases, responses are computed with tests on an achromatic background (black [PITH_FULL_IMAGE:figures/full_fig_p013_5.png]
Figure 11
Figure 11. Figure 11: Achromatic and chromatic contrast sensitivities of the considered models (top subplots A, B, C), and different sets of receptive fields computed in different ways at different depths of the models (bottom). See text for details on the psychophysical and the physiologi…
Figure 12
Figure 12. Figure 12: Contrast responses of the considered models in different masking conditions and for achromatic and chromatic textures of different frequencies. The color code (indicated in the subplots corresponding to Percepnet, but applicable to the equivalent curves of the other m…
Figure 8
Figure 8. Figure 8: For this last example, the BioMultiLayer model gets bigger attenuation for the background of the same orientation, particularly for high frequency (see the red curves), and the other orientations lead to responses that are between the no-mask condition (in blue) and th…
Figure 13
Figure 13. Figure 13: (Top) Human-like saturation behavior in contrast response happening in generic shallow autoen￾coders [21]. (Bottom) Human-like behavior obtained in image segmentation U-Nets when they are equipped with bio-inspired Divisive Normalization to improve their performance […
Figure 14
Figure 14. Figure 14: Examples of errors happening in the model [33, 34, 35]. Left: too sharp chroma nonlinearity in achromatic context -wrong gray curve- (taken from [PITH_FULL_IMAGE:figures/full_fig_p019_14.png]
Figure 15
Figure 15. Figure 15: Left: Adaptation in the CSFs in autoencoders obtained from training on the proper (colormetrically calibrated) environments for color adaptation [21]. Right: Improved contrast perception by using bio-inspired Divisive Normalization (with adaptive contrast responses su…
Figure 16
Figure 16. Figure 16: Top: Image PDF factorization from the contrast nonlinearites (div. norm.) illustrated in Figs. 7 and 8, as shown in [103]. Bottom: autoencoders trained with distortion metrics based on the contrast nonlinearities described in our proposal capture natural image statist…

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Pith tools

Reviewed August 9, 2026 · model on record in the stance chip above.