Only Brains Align with Brains: Cross-Region Alignment Patterns Expose Limits of Normative Models
Authors: Larissa Höfling
Matthias Tangemann
Lotta Piefke
Susanne Keller
Matthias Bethge
Katrin Franke
Venue: ICLR 2026 Poster (2026)
Keywords: brain alignment
benchmarking
representational similarity analysis
video models
Primary area: applications to neuroscience & cognitive science
TL;DR: We expose the limits of brain alignment of SOTA video models, and propose a framework based on cross-region alignment patterns in the brain towards more robust and meaningful assessment of brain-model alignment.

Abstract: Neuroscientists and computer vision researchers use model–brain alignment benchmarks to compare artificial and biological vision systems. These benchmarks rank models according to alignment measures such as the similarity of representational geometry or the predictivity of neural responses from model activations. However, recent works have raised a number of problems with these rankings, most critically their lack of discriminative power, raising the conceptual question of what it means for a model to be ''brain-aligned''.
Here we introduce *alignment patterns* - characteristic functional relationship profiles of each brain region to all others - and propose that models should reproduce these patterns to qualify as brain-aligned. 
First, we apply a standard benchmarking pipeline to a broad spectrum of vision models on the BOLD Moments video fMRI dataset across visual regions of interest (ROIs). 
We find diverse models appear *equivalent* in their brain alignment, reflecting the lack of discriminative power of conventional alignment benchmarks.
Conventional alignment evaluation is a pointwise similarity test: it assesses whether a model is aligned to an individual ROI. It is therefore sensitive to the specific invariances and scaling properties of the chosen metric. In contrast, *alignment pattern analysis (APA)* is a second-order *structural consistency* test: a model aligned to a given ROI should reproduce that ROI’s characteristic cross-region alignment profile. 
Applying this test, we find that, while these patterns are highly stable across brains of different subjects, even top-ranked models often fail to capture them. Notably, models that appear effectively equivalent in alignment diverge sharply under the relational criterion, demonstrating the added discriminative value of APA.
Finally, we argue for a clearer distinction between the criteria a model must meet to serve as a tool versus as a computational model. Conventional alignment measures may be sufficient for identifying neurally predictive models, but claims about computational or algorithmic similarity may require a stronger basis of evidence, including the reproducibility of relational alignment patterns.

Published as a conference paper at ICLR 2026
ONLY BRAINS ALIGN WITH BRAINS: CROSS-REGION ALIGNMENT PATTERNS EXPOSE LIMITS OF NORMA-
TIVE MODELS
Larissa Höfling" Matthias Tangemann Lotta Piefke Susanne Keller2
Matthias Bethge Katrin Franke If
Universiiyy Clinics Tübingen UUniversity of Tübingen, Tübingen Al Center {larissa hoefling, katrin. franke wtu.itttbinggnndd. de
ABSTRACT
Neuroscientists and computer vision researchers use model-brain alignment benchmarks to compare artificial and biological vision systems. These bench- marks rank models according to alignment measures such as the similarity of representational geometry or the predictivity of neural responses from model ac- tivations. However, recent works have raised a number of problems with these rankings, most critically their lack of discriminative power, raising the conceptual question of what it means for a model to be "brain-aligned". Here we introduce alignment patterns characteristic functional relationship profiles of each brain region to all others- and propose that models should reproduce these patterns qualify as brain-aligned. First, we apply a standard benchmarking pipeline a broad spectrum of vision models on the BOLD Moments video fMRI dataset across visual regions of interest (ROIs). We find diverse models appear equivalent in their brain alignment, reflecting the lack of discriminative power of conven- tional alignment benchmarks. Conventional alignment evaluation pointwise similarity test: it assesses whether model is aligned to an individual ROI. It is therefore sensitive the specific invariances and scaling properties of the chosen metric. In contrast, alignment paltern analysis (APA) is a second-order structural consistency test: model aligned to given ROI should reproduce that ROI's char- acteristic tross-region alignment profile. Applying this test, find that, while these patterns are highly stable across brains of different subjects, even top-ranked models often fail to capture them. Notably, models that appear effectively equiv- alent in alignment diverge sharply under the relational criterion, demonstrating the added discriminative value of APA. Finally, we argue for a clearer distinction between the criteria a model must meet to serve as a tool versus as a computa- tional model. Conventional alignment measures may be sufficient for identifying neurally predictive models, but claims about computational or algorithmic simi- larity may require stronger basis of evidence, including the reproducibility of relational alignment patterns.
Code is available at http://ggthhh.oooomcceceeaaaaaat
INTRODUCTION
Vision science aims at understanding the computational principles that support robust visual behav- iors in primates and other animals. During the last decade, DNNs have become an increasingly important tool towards this goal since they approach visual capabilities of humans and are highly predictive of neural activity Yamins et al. (2014); Yamins & DiCarlo (2016). This has led to new research paradigm, where diverse collections computer vision models are evaluated in terms of alignment with functional recordings from visual brain areas using variety of measures (Klabunde (et al. 2025). Correlations model--rain alignment with key model parameters, such as the training dataset, objective and architecture, then hint at biologically relevant constraints (Tang et al. 2025
"Equal contribution
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Benchmarking
Alignment pattern analysis
visual RO]
NeuroAl Turing Test
brain-brain
model-brain
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different
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Figure I: Alignment pattern analysis to distinguish between equivalently aligned models. Left: Standard prain-alignment benchmarks rank models according their alignment to brain region under some similarity transform. Comparing models' alignment scores to noise ceilings derived from brain-brain alignment scores aids interpreting model scores (e.g. the NeuroAI Turing Test Feather et al., 2025), but leaves open the problem of a lack in discriminative power to distinguish between between equivalently aligned models. Right: We introduce alignment pattern analysis (APA) as relational extension to the NeuroAI Turing Test that distinguishes models based on whether they reproduce the characteristic cross-region alignment patterns of brains.
Conwell et al. 2024 Sartzetaki et al. 2024). Recent progress in computer vision, including perfor- mant self-supervised models (c.g. Bear et al. 2023 Assran et al. (2025), along with the systematic
collection of large-scale fMRI datasets (Lahner et al. [0244] Allen et al. 2022). now make it pos-
sible to scale these efforts towards comprehensive brain-alignment benchmarks of vision models
(Schrimpf et al., 2018: 2020, Conwell et al., 2024 Sartzetaki et al. 2024 Tang et al.) (025).
The conelusions drawn from such benchmarks, however, rest on the assumption that differences in alignment scores reflect differences in brain-likeness in some meaningful way. Recent work has challenged the validity of this inference step on the basis several arguments and empirical findings. Different alignment measures yield inconsistent model rankings (Soni al., 20224, Ahlert et al.) 2024). evidently measuring different aspects of alignment, but the choice of alignment measure often explicitly motivated. Models varying in many aspects were found achieve similar brain-alignment scores, raising the question of whether currently used alignment measures have sufficient discriminative power tell apart more from less brain-like models (Conwell al. 2024
Wu et al. 2025). Further, inferences about brain-likeness from brain-alignment are usually based
on correlational evidence and are rarely subjected to rigorous causal testing (Bowers et al. 2022
Schaeffer et al. 2024 Bowers et al., 2023). This raises a deeper conceptual question: What does actually mean for model be "brain- "brain-alignedd, and how do we measure it? Previous works (Fetherr et al.) 2025) have posited the following requirement for model to be considered brain- like: its internal representations should be indistinguishable under similarity transform from those of other brains up to individual variability. In other words, if model looks much like brain as does the brain another individual, passes the NeuroAl Turing Test (Fig. [ left).
In our work, we adopt the viewpoint of the NeuroAl Turing Test and make three major contributions to advance the benchmarking of model-brain alignment:
1. Benchmarking vision models on video fMRI dataset
We evaluate broad family of state-of-the-art vision models with respect to their alignment to brain activations across human visual cortex to naturalistic videos and find that diverse models achieve comparable alignment scores (Fig. 2 This finding suggests a lack of discriminative power of conventional alignment benchmarks.
2. Operationalising equivalence in brain-alignment
We quantify this by devising heuristic based alignment score distributions across sub- jects to define effective equivalence (Fig. [ left). Using this tool, we find that many models appear equivalent their brain alignment, confirming the need for diseriminative practices in alignment benchmarks.
3. Alignment pattern analysis to distinguish among equivalently aligned models Third, we introduce relational criterion distinguish among equivalently aligned mod-
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els: We posit that model should only be considered aligned to brain region if it repro- duces the cross pross-region alignment pattern of this brain region (Fig. right).
RELATED WORK
Alignment benchmarks. Pioncered by work on visual object recognition (Yamins et al. 2014), neural network models have been compared the brain on large-scale benchmarks. Brain-Score (Schrimpf et | 2018) originally focused on static image processing along the ventral stream, later adding language regions (Schrimpf al., 202T). It provided a first large-scale platform for model evaluation. The Algonauts challenge extended this approach to whole-brain responses to naturalistic stimuli, first for static images and later extending to dynamic movie- paradigms (Gifford al., 2024 2023: Cichy et al. [022]] (019). Most recently, the challenge has emphasized multimodal video inputs, pushing alignment analyses into richer and more ecologically valid contexts (see e.g-
d' d'Ascoli et al. 2025).
Beyond these community benchmarks, several studies have systematically examined factors shaping model-brain alignment. Conwell et al. (2024) showed that vastly different architectures can achieve similar alignment, with variety of "visual diet" emerging as the most consistent determinant. Tang et al. (2025) found that single predictive objective understanding world dynamics leads to high linear predictivity (LP) scores across cortical areas, suggesting shared computational principles across the hierarchy. Using representational similarity analysis (RSA), (Sartzetaki et al. (2024) find that video models achieve the highest alignment in early visual regions, whereas semantic training objectives are key for both ventral and dorsal regions.
Frameworks for tightening definitions of brain-alignment. Standard alignment measures such as LP (Yamins al. 2014) and RSA (Kriegeskorte al. (008)) have been criticized as being too flexible and hence lack in discriminative power (Conwell et al. 2024). To address these shortcom- ings, stricter alignment measures (reviewed in KKlabunde al. 2025) and aggregation of several measures (Wu et al., (025) have been proposed. Beyond this, several frameworks have been devel- oped that aim to conceptually tighten the definition of aaiinmalignment, Nonaka al. (202T) propose the brain hierarchy score to quantify hierarchical correspondence between deep neural networks and brains. Thobani et al. (0225) suggest inter-animal transforms as a way for identifying alignment measures that exhibit sufficient degree of specificity while not being prohibitively strict. Here, we suggest alignment pattern analysis (APA), which is complementary to these previous works. Non- aka et al. (02II) evaluate hierarchical correspondence between entire models and the entire visual stream, whereas we focus on comparing patterns of alignment of individual model layers to those of individual ROIs. The IACT Thobani et al. (2025) serves evaluate and identify appropriate alignment measures, whereas APA can be performed with any alignment measure. Rather, APA can be seen as relational extensions to the NeuroAI Turing Test Feather et al. (2025), which sug- gests to compare model-brain alignment scores with model-brain with brain-brain alignment patterns.
alignment scores; APA compares
METHODS
3.1 DATASET
We base our analyses on the BOLD Moments Dataset (Lahner al., 2024). 3T fMRI dataset recorded from 10 subjects watching over 1000 different 3-second video clips. Each of the 1000 stimuli in the train split was shown three times to each subject, each of the 102 stimuli in the test split was shown ten times. Stimulus repetitions were presented in random order across 4 sessions. We use beta values (GLMSingle regression coefficients of each voxel and video shown), projected to fslr32k surface space, as they are output from the preprocessing pipeline (specifically, version B. (see Lahner et al. 2024 for details). We use the original train-test split and average the fMRI activity over repetitions, leading to higher signal-to-noise ratio on the test split, compared to the train split. While the voxel-wise beta values provided by Lahner et al. (024) are already centered and normalized across individual sessions, normalize and center them once more across the train set, and use the same standard deviation and mean per feature to approximately center and normalize the test split.
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We analyze ROIs from the Glasser HCP-MMP atlas (Glasser al. 2016 early visual areas (VI,V2.V3), dorsal stream (V3A, V3B, V6, V6A, V7, IPSI, MST, MT. EST, L01- and ventral stream (V4, V8, PIT, FFC).
3.1.1 NOISE CEILINGS
Because fMRI data are noisy, neither perfect predictivity nor perfect representational similarity can be expected (Walther et al., 2016). We compute two noise ceilings for each ROI in the following ways: Upper noise ceiling. We average the fMRI data of N-1 subjects for the given ROI. Then we use this average as predictor feature space and compute RSA/LP score between the average map and the remaining subject's 's ROI data. This yields one upper noise ceiling per subject, from which can compute
and the standard error of the mean (SEM) across the ten subjects. Lower noise ceiling. As suggested in the Neuro-AI Turing test Feather et al. 22025), we compute a noise ceiling based pairwise alignment scores between subjects. For given ROI, we sample five other subjects per target subject, excluding previously sampled pairs, for a total of 50 subject pairs, For each pair we compute the RSA/LP score between the regions' fMRI features of the two subjects, and again compute the mean across all resulting alignment values. For an estimate of the SEM of the lower noise ceiling, we do not divide by the number of samples (50), as they are not all independent. Instead, we divide by the number of independent predictors/prediction targets, i.e. subjects (10).
3.2 MODELS
We evaluate 47 ofatc-of-the-art pretrained image and video deep learning models that cover broad range of architectures, objectives and datatsets:
Taskonomy model bank. A collection of 26 models based on ResNet-50 and trained the same dataset of million indoor scenes, but for different tasks (Sax et al. 2018 Zamir et al., 2018 Supervised image models. We include ResNet (He et al. 2016) and ConvNext (Liu et al. 2022b) models from the timm library [Wightman] (019). all trained for object recognition on ImageNer- 1K. Self-supervised image models. As counterpart the supervised image models, we include ResNets trained on ImageNet- KK with the self-supervised SimCLR objective (Chen et al., (020), as provided by VISSL (Goyal et al. 202I). CLIP. We consider the ResNet-50 and Vision Transformer based CLIP models from the original codebase, all trained to align image and text representation on large dataset of 400M image-text pairs (Radford et al. 202T). Supervised video models. We use three video transformers from the mmaction2 toolkit (Contributors 2020): MVIT (Li et al.) 2022), Video Swin Transformer (Liu et al. 2022a), TimeSformer (Bertasius et al. 2021). All models were trained for action recognition on the Kinetics-400 dataset. Unsupervised video models. We include the ViT-based counterfactual world model (CWM) (Stojanov et al. 2025) which was trained on Kinetics-400 using an adapted MAE objective (He et al. 2022). Further, consider the V-JEPA 2 model (Assran et al.] 2025) that was trained on a large-scale video dataset using a variant of the MAE objective in feature space. VGG Transformer (VGGT). We include the 3D foundation model VGGT (Wang et al.) 2025) as comparison to the dominantly semantic models described above. This ViT-based model was trained to simultaneously predict multiple key 3D attributes from variable number of views of a scene.
For all models, extract representations for the last layer of up 15 blocks (e.g., residual block in a ResNet). For models with more blocks, we use 15 equally spaced blocks. We apply image models to each frame individually, and video models and VGG-T to the entire video clip (3s), and average representations over time. The resulting feature vectors are reduced using sparse random projection (Achlioptas) (003) to 5919 dimensions, following the Jdhnson-Lindenseen with an epsilon of 0.1 (Achlioptas) 2001).
Lemma
3.3
MEASURING MODEL-BRAIN ALIGNMENT
For every combination of model and ROI, we select the best layer the training set by averaging the alignment scores over subjects. Using the selected layer for all subjects, we then report alignment scores on the test set. We consider the following two alignment metrics:
Representational Similarity Analysis (RSA) compares representations based on representational dissimilarity matrices (RDMs), which are sufficient statistics for the representational geometry of
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system (Kriegeskorte & Wei 2021 Kriegeskorte al. 2008 RDMs constructed for the model and brain representation by computing the pairwise correlation distances the representation (1 Pearson correlation) for all samples. The overall RSA alignment score is then the Pearson correlation of the brain and model RDMs.
Linear predictivity (LP) measures alignment by fitting linear model that predicts brain activity from model features (e.g., Yamins et al. (014)). We fit ridge regression models predicting the preprocessed fMRI signals on the training using 5-fold cross-validation. We use the RidgeCV implementation from the scikit-learn package (Pedregosa et al., 201T), which selects the optimal alpha value using leave-one-out cross- cross-validiee validation from 19 candidate values on a logarithmic scale spanning 10 to 10º. Given the respective linear models fitted on the training set, we report the residual sum of squares (R") on the test set.
3.4 DETERMINING EFFECTIVE EQUIVALENCE BETWEEN MODELS
To determine when models are effectively equívalent in terms of brain alignment, we devise a heuris- tic based on bootstrap estimates of variability in model-brain alignment scores. For given model m with feature space Xm, generate bootstrap distribution of mean prain-alignmeet scores under a measure M by resampling subject indices with replacement. Specifically, we define a bootstrap index vector
I'
~ Uni with replacement,
I= ( 10).
and compute the corresponding bootstrap estimate the mean alignment score as
10
Σ M( Yz:).
10
k=1
We then derive 95% confidence intervals for the model's mean wrain-alignment score from the re- sulting distribution. A model m is deemed effectively equivalent to the top-ranking model t if its empirical mean alignment score (M(Xm. Y;)), falls within the 95% confidence interval of the top ranking model.
3.5 ALIGNMENT PATTERN ANALYSIS
We define an alignment pattern a under a similarity transform M between predictor feature space Φp and N target feature spaces V [bb],.., vi2
a' V.)) = MM w M( v2t), Vi) M(on.
(1)
3.5.1 FMRI-DERIVED ALIGNMENT PATTERNS
For IMRI-derived alignment patterns, both the predictor and the target feature spaces are sourced from brain activity from the BOLD Moments Dataset. IMRI-derived alignment patterns are defined between pairs of subjects p. f, where the brain activity of subject functions as the predictor feature space ép and the brain activity of subject functions as the target feature space V,, The alignment pattern for given ROI r E {1, 2,.,, N) and a pair of subjects p, l then defined as
(áp. W.) [M
vi.,..., M(ØJ, v..)....,,
M( N
(2)
We detail in the Appendix Section S2.1 how the variance of fMRI-derived alignment patterns is estimated.
3.5.2 MODEL-DERIVEL ALIGNMENT PATTERNS
For medel-derived alignment patterns, the predictor feature space is defined as the activations in one layer of the model, & and the target feature spaces are analogous to the case of IMRI-derived alignment patterns. A model-derived alignment pattern between model m and subject t for ROI is then
arlóm, v,) [M( Mt), M.. v
(3)
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3.5.3 STRUCTURAL CONNECTIVITY- ALIGNMENT PATTERNS
For comparing alignment patterns to structural connectivity patterns. we use connectivity matrix based on hiffusion--eighted tensor imaging (DTT) Pierpaoli et al. (1996) streamline-denstty from the Human Connectome Young Adult full dataset (Caron & Pestilli 2023) as provided through brainlife Hayashi et al. (2024) (provided as 'conmat" datatype). The procedure fits streamlines white-matter trajectory candidates |Smith et al. (2012) to diffusion MRI data. The number of streamlines intersecting both ROIS of pair of regions is divided by the volume of both regions to obtain the "density" -based connectivity matrix we use. For more information, see [Hayashi et al. (2024,, section dMRI processing. We average the connectivity matrices of 1065 subjects to obtain single connectivity matrix, C' = (Cra)r,t=I...N where C,, is the streamline density between regions and t. The structural connectivity-derriee alignment pattern for a given ROI is then
G.Nl where we exclude the ROI r since self-similarity not defined for streamline-density as alignment measure.
astraat (r) [cr,1 ..... Cr,r-1, , Cr,++
(4)
3.5.4 ALIGNMENT PATTERN SIMILARITY
Alignment pattern similarity between two alignment patterns, e.g. IMRI-derived alignment pat- tern (0pp Vx) and a model-derived alignment pattern on (om, ) is calculated as
(áa, Wz), an(om, v.))
(5)
where p is Pearson's 's correlation coefficient.
4 RESULTS
4.1 BENCHMARKING ALIGNMENT OF VISION MODELS TO THE VISUAL CORTEX
We evaluated broad range of vision models with respect to their alignment to human visual cortex (including carly, ventral, and dorsal regions) using two complementary alignment measures: RSA and LP. The models varied in architecture (CNNs and Transformers). training objective (various supervised and self-supervised objectives), modality (image and video), as well as model size and training dataset (Methods 3.2).
Consistent with previous work TTang et al. 2025) we found that the self-supervised V-JEPA model family (Assran et al. 2025) achieved the highest overall alignment scores across visual cortex, Notably, and consistent with another body of previous work (Conwell et al. 2202)). the best aligned models included CLIP with ResNet- differ in several important
according to both RSA and LP (Fig. 2g. Tables S3.1 S3.3
50 backbone and a ViT backbone, and the BEGG-Trasformer-- aspects from V-JEPA and each other, such as the training data, training objective and overall architecture. The analysis of model performances at the level of individual ROIs agreed with this finding (Fig. 2p).
To quantify the observation that different models do similarly well, we defined heuristic for effec- tive equivalence to the best-aligned model (see Methods Section 3.4), and found several effectively equivalent models for most ROIs (Fig. 2p. bold bars). These findings demonstrated the lack of dis- criminative power of alignment-mentt measure based model rankings, motivating the need for additional criteria to distinguish between models with effectively equivalent brain-alignment.
To contextualize absolute alignment scores, we further compared model-braid alignment scores to the distribution of inter-subject (i.e., brain-brain alignment scores, as recently proposed under the NeuroAI Turing Test framework (Feather et al. 2025) Fig. Zb. dash-dotted lines). We found the best-ranking models to reach or even surpass brain-brain alignment when evaluated with L.P. which indicates that the benchmark is saturated for LP on almost all ROIS (Fig. [2p and Tables [S..4 SS.2). We did not observe similar signs of saturation for RSA, so that we focus on this alignment metric for the remainder of our analyses but report results for LP in the Supplementary Material.
Models from the Taskonomy family consistently underperformed all other models in terms of brain align- ment. We report scores for these models in the Tables in the Supplementary Material.

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Model-brain alignment
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Figure 2: Diverse models achieve comparable alignment scores on the BOLD Moments Dataset. (a) Subject-averaged alignment scores (RSA/LP) across ROIs; errorbars are standard deviation across ROIs. (b) Subject-averaged alignment scores for individual ROIs (V1, V8, MST); errorbars indicate bootstrapped 95% mofiidence-interval around the mean. Effectively equivalent models (see Methods Sec. 3.4) are highlighted in bold. Dash-dotted line indicates NeuroAI Turing Test alignment reference, i.e. pairwise alignment between individual brains. Dashed line indicates an alternative alignment reference based comparing an averaged reference brain to individual brains (see Methods Sec. 3.1.I.
4.2 ALIGNMENT PATTERNS ARE A REPRODUCIBLE FINGERPRINT OF EACH BRAIN REGION
Visual brain areas, as functionally defined entities, have characteristic functional relationships to all other regions. We hypothesized that these relationships are reflected in stable, ROI-specific patterns of inter-region bligmment-jMil -MRR-derived alignment patterns (AP that are consistent across in- dividuals and characteristic for each ROI, and that these patterns could serve as a reference against which to evaluate models.
We estimated IMRI-derived AP by using one subject's brain activity in given ROI as predictor of another subject's brain activity across all ROIs (Methods Sec. both fMRI-derived and model- derived AP scores normalized to the NeuroAT Turing Test noise ceiling). We quantified the consistency of these patterns across the population using alignment pattern similarity (APS): for each subject, we computed reference AP from all pairwise combinations of the remaining subjects, and measured how well individual APs correlated with this reference (Suppl. Methods Sec S2.1
We use AP as abhreviation for both the singular alignment pattern and the plural alignment patterns avoid confusion with APS (alignment pattern similarity)

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Brain-brain alignment pattern analysis
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Figure 3: Brain-brain alignment patterns are consistent across subjects and characteristic for
ROIs. (a) RSA-based IMRI-derived AP for example ROIs, mean SEM across subjects. Shaded areas indicate where predictor and target ROI coincide.
(b) Dark gray bars: APS between fMRI- AP, horizontal lines indicate 95% percentile of the null distribution of APS. Red for ROIs where true value is significant, black where it is not, p-values FDR-corrected (see Methods Sec $2.2
derived AP and structural connectivity-derivees
We found these patterns to be highly consistent across subjects within each ROI, and distinct across ROIs (Figs. 3 $3.3).
To additionally validate IMRI-derived alignment patterns, we compared them to an independent measure of relations between brain regions: structural connectivity derived from diffusion-weighted imaging in 1065 humans (Caron & Pestilli, 2023] Fig. [S..2). We found high similarity between fMRI-derived and connectivity-derivee, AP, especially for early regions (Fig. 33, dark gray bars), and similarity was above chance for ROIs V1, V2, V3, V3B, V7 and IPS1 (see Methods Sec. S2.22. We therefore propose NeuroAl Turing Test framework to relational patterns of alignment across brain regions.
fMRI-derived AP reference for model-derived AP, extending the
4.3 ALIGNMENT PATTERNS DIFFERENTIATE BETWEEN MODELS THAT APPEAR EFFECTIVELY EQUIVALENT
We hypothesized that models that achieve similar alignment scores for a given ROI might nonethe- less differ in their pattern of alignment scores across ROIs, i.e. their model-derived AP. If this was the case, AP could provide relational criterion for distinguishing effectively equivalent models. We computed AP for the pest-performing models for a given ROI and indeed, we found that equivalently aligned models diverge in their alignment patterns (Fig- 4h, colored lines; results for LP-based AP Fig. (3..5). Can we say which model is more brain-like in terms of its relational pattern alignment scores? We calculated alignment pattern similarity between model-derived and IMRI-derived AP (Fig, 4p) and found that (i) all models fell short of brain-brain APS (i.e. the relational NeuroAI Turing Test) and (ii) oftentimes the most aligned models in terms of individual scores were not the most brain-alignes in terms of APS. Especially the high-ranking models from the V-JEPA family achieved low APS with most ROIs (Fig.
We can apply the same heuristic for effective equivalence on APS, and require that model, in order be considered meaningfully aligned a ROI, should be equivalent the best-ranking model both on the alignment score and APS (i.e. it should lie in the region where the gray bars in Fig. 4p intersect). When applying this criterion, the number candidate models is reduced drastically for most ROIs, and V-JEPA models are ruled out for most ROIs (Fig. 4b).
5 DISCUSSION
In this work, we show that standard brain-alignment benchmarking pipelines lack in discrimina- tive power for distinguishing between models that achieve similar alignment scores. We introduce and apply a relational criterion alignment pattern similarity and show that it can distinguish be- tween equivalently aligned models. Conceptually, by comparing model-derived alignment patterns
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Figure 4: Alignment patterns differentiate between models that appear effectively equivalent. (a) RSA-based IMRI-derived (black lines) and model-derived (colored lines, color mapping in previous figures) AP for four example ROIs (b) Alignment score (RSA) plotted against APS score for all models (gray dots indicate models from the Taskonomy library). Gray shaded areas indicate the 95% CI around the best model's these can be two different models. (c) Candidate brain-aligned models (all models effectively equiv- alent to best performing) without (black squares) and with (gray squares) APS requirement. In other words, only models that lie in the intersection of the vertical and horizontal shaded areas in (b) are marked with a square.
RSA and APS score, respectively. Note that
to brain-derived alignment patterns, alignment pattern analysis constitutes a relational extension of the NeuroAI Turing Test (Feather al. (025).
Alignment patterns: a tool for distinguishing tools from models. Consider this intuition: good model of V1 should not able to linearly predict higher visual areas by virtue of the progression of feature complexity in the primate visual hierarchy (c.g. Felleman & Essen 1991 Güçlü & Van Ger- ven 2015 Cao Yamins, 2024a). If an alignment measure (AM) predicts comparable alignment of a model to V1 and to higher visual areas (as is the case e.g. for LP and the V-JEPA models, see Fig. $3.5 this AM lacks the discriminative power to distinguish between models that are good predictive tools for brain region and models that genuinely resemble its underlying computational mechanisms. (Wichmann & Geirhos 2023). ). APA operationalizes this intuition and makes it quan- tifiable. More formally, whereas conventional model-brain alignment benchmarks test similarity in pointwise fashion (per-ROI alignment), APA adds recond-order structural consistency test. When applied, it exposes the underdetermination of pointwise alignment.

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On the practice of normalization. This underdetermination is more problematic in regimes of low alignment in the sense that there are many ways of being poorly aligned to the brain, and only few ways of being strongly aligned. In light of this consideration, both the practice of reporting normalized scores, as well as the question of which noise ceiling to normalize against, should be noise ceiling calculated from pairwise align-
reevaluated. Here, normalize scores relative ment scores between subjects, following suggestions by Feather et (2025, While this procedure increases the discriminative power, especially of LP (compare Figs. 2 and S3.1 it is questionable whether the additional differences that we can detect are really meaningful, given the amount of variance that is left unexplained. An alternative alignment target is the noise ceiling calculated be- tween aggregate reference brain and individual brains (Methods Sec. 3.1.I. Unlike the pairwise ceiling, this defines a benchmark based on shared neural structure rather than the similarity between noisy biological measurements. It is substantially higher and may provide a more meaningful target for models.
The contravariance principle. These considerations directly connect to the contravariance prin- ciple (see e.g. Cao Yamins, 2024b; Schaefter et al.) 2022). While a given function can be realized through many implementations, increasing task constraints should progressively restrict the space of viable solutions (see also Huh et al.] (024). Task difficulty is one way of increasing such constraints. In principle, this should promote representational convergence and thus stronger brain-model aligoment, as has been observed for many years (Kar & DiCarlo 2024). The recent breakdown of performance-aligneee performance gains are achieved through optimization toward alternative implementations that are not necessarily brain-like. This perspective also suggests that the correlation between task perfor- mance and brain alignment in low and intermediate regimes may primarily reflect convergence in generic, task agnostic representational components rather than task-critical computational strategies. Because natural visual environments exhibit strong statistical regularities, both brains and models may converge toward a shared representational basis adapted towards those statistics, reflected in non-zero alignment scores. However, multiple implementations of task-relevant computations possible within this shared basis. Consequently, intermediate aligmment may largely reflect overlap in general- pprepose visual representations rather than convergence toward the brain's 's task-relevant computational solution [anatarr et al. (2023). Alignment pattern analysis can be seen as a way of applying the contravariance principle through additional constraints, independent of task difficulty.
scaling (Linsley al., 2025), however, suggests that current
Towards a stricter framework for brain alignment. Our findings together with a growing body of literature (Conwell et al., 2024 Soni et al.] 2024, [hlert et al., 2024 Schaeffer al.) 22224) suggest that high model-brain alignment scores may be insufficient evidence to draw inferences about model-brain similarity. Progress towards computational models of primate vision with gen- uine mechanistic similarity requires stricter criteria such as APA, rigorous causal tests (Bowers) al., 2023), and explicit commitments about which scientific claims a given alignment measure is equipped to support and which not. This becomes all the more relevant as computer vision models grow larger and more powerful: they may become increasingly effective tools for predicting neural responses while at the same time potentially diverging from the brain's 's computational strat- egy (Linsley et al. 2025). Without a clearer framework for distinguishing predictive utility from brain likeness, progress alignment benchmark risks decoupling from scientific insight towards mechanistic understanding of how the brain works.
ACKNOWLEDGMENT
KF, MB, and LH were supported by the German Federal Ministry of Research, Technology and Space (PRISMA-V, 01GQ2501B); KF, MB, SK, MT by the German Research Foundation (CRC "Robust Vision", 276693517). KF and LH were additionally supported by the European Research Council (Eye to Action, 101117156).
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S1 DISCLOSURE OF LLM USE
We have used LLMs assist in the code writing process, including for plot creation, discuss ideas and concepts, in literature search, for searching information in a given work, and for refining text in this paper.
S2
SUPPLEMENTARY METHODS
S2.1 ALIGNMENT PATTERN SIMILARITY DISTRIBUTIONS
To assess whether model-brain alignment pattern similarities fall within or outside the distribution of brain-brain alignment pattern similarities (Feather al. 2025) first define subject-specific reference alignment pattern.
For given ROI and subject to. compute the mean brain-brain alignment pattern across all pairs of subjects (p, t) in which to does not participate, i.e., & to and A to. The subject-specific reference pattern for Po then obtained by averaging over all such alignment patterns that exclude
(10)
Σ ptP)(ta) tET((ta)
ar(op, Vt))
(S1)
P (toll IT Ktoll
where P and T denote the sets of all predictor and target subjects.
The brain-brain alignment pattern similarity for subject to ROI r is then defined as the average over the similarities between the reference pattern (fo) and all individual alignment patterns in which to functions as the target:
(ta) D
{p(af(o) ar ( V.,)) Up P\ P\(o))
(S2)
bruin
Analogously, the model-brain alignment pattern similarity distribution for subject to computed using the model feature space om as predictor:
(fo)
{p(afto) aro (pm, (col)
(S3)
model
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S2.2 SIGNIFICANCE OF FMRI-DERIVED-TEE SEATIIVVEED---SSUUUUAUAAA STRUCTURAL ALIGNMENT PATTERN SIMILARITY
To determine whether an APS value between structural and IMRI-derived alignment pattern is meaningful and not due to chance, we create a null distribution of structural patterns, and compute APS between the fMRI-derived pattern to those random patterns.
We create random patterns by sampling 18 regions k (.i kic) random from all regions contained in the full structural connectivity matrix C= (G..)s.==....M M. M > N. containing addi- tional regions to the ones included in our analysis. This yields one random alignment pattern per region,
Grannd (r) = (Ca()). (rl)
(S4)
where (r-) is the index of region in matrix C. We then compute the APS to each subject's fMRI- derived alignment pattern a (o 1p)) as
pla(o, (randx((()))
according to equation 3.5.4
We generated null distribution by repeating the permutation procedure 1000 times. For each ROI, computed a group-level test statistic by averaging the correlation values across subjects. The two-sided p-value was calculated as the proportion of absolute correlation values from the null distribution that were greater than or equal the observed absolute correlation value. To account for multiple comparisons across ROIs, p-values were corrected using the false discovery rate (FDR) procedure Benjamini Hochberg 1995). Statistical significance was assessed threshold of = 0.05 (FDR-corrected).
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S3
SUPPLEMENTARY RESULTS
roi
VI
V2
V3
V4
V8
PIT
FFC
V3A
V38
V6 V6A
V7
IPSI
MT
MST
FST
LO1
LO2
LO3
model
egomotinon
0.04
40,00
0.01
0.02
0.01
0.02
0.03
0,02
0.01
0.01
0.04
0.01
0.00
0,03
0.03
0.02
0.02
0.02
0.02
room-layout
200
colorization
0.09
0.02
0.04
0.03
0.01
0.01
0.02
0.01
0.00
+0.00
0.02
0.00
000
0.03
0.03
0.02
0.04
0.02
0.02
curvature
0,06
0.01
0.02
0.02
0.00
0.02
0.03
0.01
0.01
-0.00
0.04
0.02
0.03
0.04
0.03
0.02
0.03
0.02
point mutching
0.84
0.01
0.01
0.04
keypoints24
0.08
0.04
0.04
0.03
0.01
0.01
0.02
0.02
0.01
-0.00
0.04
0.01
0.02
0.04
0.03
0.02 0.03
0.02
0.02
fixated puse
0,05
0.02
0.04
0.01
0.01
0.2
0.04
0,03
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0.03
edge-texture
0.12
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0.01
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40.1
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0,2
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0.01
vanishing point
0.06
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0.03
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-0.00
-0.00
0.03
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0.04
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0.02 0.04
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0.03
nonfixated-- pose
0.01
normal
0.05
0.01
0.03
0.03
0.01 0.02
0.04
0.01
0.01
.0.01
0.03
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0.03
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0.03
0.05
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0.03
segment-semantic
0.07
0.01
0.03
0.02
0.01
0.02
0.04
0.01
0.01
+0.00
0.03
0.02
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0.04
0.03
0.04
0.02
0.03
edge-ncclusion
-0.01
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inpainting
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0.02 0.05
0.02
0.04
depth-zbuffer
0.05
0.06
0.01
-0.00
0.05
0.05
0.03
0.06
deusising
0.09
0.05
0.05
0.04
0.02
0.02
0.03
0.03
0.01
0.02
0.04
0.01
0.03
0.04
0.03
0.02 0.03
0.02
0.03
autoencoding
0.08
0.04
0.08 0.04
0.03
0.02
0.04
0.02
0.01
0.02
0.04
0.01
0.03
0.04
0.03
0.02 0.03
0.02
0.04
sogment-unsup25d
reshading
0.06
0.01
0.04 0.03 0.02
0.03
0.06
0.02
0.01
-0.00
0.04
0.02
0.03
0.06
0.05
0.04
0.05
0.04
0.04
jigsaw
0.09
0.03
0.05 0.05
0.02
0.04
0.01
0.01
0.03
0.05
0,05
0.03
0.04
0.03
depth-cuclidcan
0.06
0.02
0.04 0.04
0.03
0.04
0.06
0.03
0.01
+0.00
0.05
002
0.04
0.06
0.06
0,04
0.05
0.04
0.04
geement-unsup22
0.13
0.04
0.05
0.05
0.03
0.03
0.05
0,03
0.02
0.02
0.05
0.02
0.03
0.05
0.05
0.03
0.04
0.03
0.04
keypoints'd
0,06
0.05
0.03
0.05
0.04
0.07
0.07
0.04
0.05
0.04
class scene
0.06
0.03
0.05 0.06 0.05
0.04
0.07
0,04
0.02
0.01
0.06
0.03
0.04
0.08
0.08
0.06
0.06
0.04
0.06
class abject
0.06
0.04
0.06
0.06
0.04
0.04
0.07
0,03
0.02
0.01
0.06
0.03
0.04
0.08
0.07
0.05
0.06
0.08
0.06
timm/somvnest-
0.07
0.04
clip/ViT-I /148 386px
0,12
0.11
0.11
0.09
0.05
0.07
0.09
0.05
0.04
0.02
0.07
0.04
0.06
0.10
0.09
0.07
0.08
0.08
0.07
timm/commnext. huse
0.12
0.11
0.13
0,05
0.07
0,10
0.05
0.03
0.04
0.06
0.09
0.08
0.07
0.07
0.08
clip//iT-L/14
0.06
0.08
0,10
0.04
0.02
004
0.08
0.10
0.09
0.08
0.08
0.07
timm/convnest-sme
0.10
0.09
0.11
0.10
0.07
0.08
0.12 0.06
0.05
0.04
0.08
0.05
0.06
0.10
0.11
0.09
0.08
0.09
0.09
clip/VTT-B/16
v-jepawrjeua2vvit.
0.11
0.13
0.12
0.12
0.09
0.07
0.10
0.08
0.06
0.08
0.08
0.05
0.05
0.09
0.10
0.08
0.07
0.08
0.08
spt-ownnogt-wwwn
0.09
0.11
0.11
0.09
0,05
0.07
0,08
0.09
0.06
0.17
0.10
0.07
0.06
0.10
0,13
0.09
0.06
0.07
0.07
mmaction22swin-iig
0.12
timm/oonvactt-in
0.07
0.04
0.06
0.I1
0.09
0.10
0.14
0.07
0.06
0.05
0.10
0.07
0.08
0.14
0.14
0.12
0.30
0.11
0.11
mmactton22nvvii smmlll
0.21
0.16
0.12
0.09
0.07
0,10
0.06
0.07
0.06
0.05
0.10
0,10
0.08
0.07
0.08
0.08
mmaction2tineesfeen
0.15
0.14
0.13
0.13
0.09
0.09
0.13
0.07
0.05
0.06
0.08
0.06
0.06
0.11
0.11
0.09
0.09
0.10
0.10
timm/esnet188
0.12
0.10
0.11
0.15
0.11
0.12
0,18
0.09
0.07
0.06
0.11
0.08
0.09
0.16
0.15
0.14
0.12
0.13
0.14
hast/inele-resettle
0.05
0.17
0.15
clip/NiT-B/32
0.13
0.11
0.13
0.18
0.14
0.15
0.23
0.10
0.09
0.06
0.12
0.09
0.11
0.19
0.18
0.16
0.16
0.17
0.17
väst/sinctrreentt3
0.13
0.11
0.14
0.15
0.17
0,23
0.08
0.06
0.10
0.08
0.10
0.19
0.17
0.17
0.16
0.18
0.18
timm/hesne150
0.16
0.24
0.09
0.08
0.10
0.19
0.17
0.19
vger/VGTT-1B
0.14
0.09
0.11
0.20
0.15
0.15
0.24
0.10
0.10
0.08
0.13
0.09
0.12
0.20
0.20
0.16
0.15
0.17
0.20
clip/RN50
0.13
0.11
0.20
0,15
0.17
0.25
0.10
0.06
0.09
0.11
0,20
0.19
0.17
0.17
0.19
v-jjpa/rjepa2--iitteee
0.17
0.19
0.19
0.23
0.17
0.13
0.16
0.15
0.12
0.15
0.14
0.11
0.09
0.15
0.18
0.15
0.14
0.14
0.17
v-jepa/vjep22 vit large
0.17
0,22
0.20
0.24
0.17
0.13
0.18
0,16
0.13
0.15
0.13
0.12
0.10
0.17
0.19
0.15
0.13
0.14
0.16
0.17
0,20
0.11
0.22
0.14
0.16
Table S3.1: Model-brain alignment under RSA.
S3.1 ALIGNMENT SCORES AND ALIGNMENT PATTERN SIMILARITY ACROSS ALL LAYERS
17

---

Published as a conference paper at ICLR 2026
VI
V2
V3
V4
V8
PIT
ITC
V3A
V3B
V6
V6A
V7
IPS]
MT
MST
1ST
101
L.02
LO3
model
egonotion
0..00
0.04
0.05
0.04
0.04
0.14
0.07
0.12
0.08
0.06
0.07
toom-layout
0.17
0.03
0.08
0.06
0.02
0.05
0.05
0.02
0.03
-0002
0.13
0.06
0.10
0.10
0.08
0.05
0.12
0.06
0.06
colorization
0.24
-0.01
-002
0,07
0.04
0.06
curvature
0.15
0.02
0.06
0.05
0.02
0.06
0.05
0.02
0,94
-002
0.13
0.08
0,14
0.11
0.09
0.05
0.13
0.06
0.07
point-matching
0.13
0.02
0.07
0.06
0.04
0.05
0.06
0.06
0.05
0.05
0.14
0.07
0.15
0.11
0.09
0.07
0.10
0.05
0.08
keypoints2d
-0.01
0.09
hxated-pose
0.14
0.02
0.07
0.07
0.05
0.05
0.06
0.06
0.06
0.04
0.09
0.10
0.10
0.11
0.09
0.07
0.12
0.05
0.09
edge texture
0.34
0.09
0.12
0.01
-0.00
-002
0.09
0.09
0.08
0.04
0.09
0.06
0.03
vanishing point
0.000
0.02
0.08
0.06
nonfixated pose
0.13
0.02
0.07
0.07
0.07
0.05
0.07
0.06
0.06
0.04
0.15
0.08
0.14
0.11
0.08
0.08
0.10
0.05
0.10
normal
0.14
0.03
-0.06
0.09
0:0
0.09
segment semantic
0.18
0.02
0.08
0.05
0.03
0.04
0.05
0.02
0.06
0.00
0.12
0.09
0.11
0.16
0.12
0.08
0.15
0.06
0.09
odge-occlusion
0.15
0.03
0.12 0.09
0.07
0.05
0.08
0.03
0.03
-0.03
0.13
0.08
0,11
0.16
0.12
0.08
0.14
0.06
0.11
inpainting
depth-zbuffer
0.15
0.03
0.09
0.06
0:05
0.06
0.10
0.10
0.05
-001
0.18
0.08
0.15
0.15
0.11
0.06
0.17
0:07
0.18
denoising
0.25
0.13
0.15
0.05
0.06
0.09
0.09
0.15
0.06
0.13
0.11
0.08
0.05
0.13
0.06
0.10
autocnooding
0:22
0.10
0.14
0.10
0.10
0.05
0.07
0.07
0.07
0.10
0.15
0.06
0.13
0.11
0.08
0.06
0.13
0.11
segment-unsup256
0.16
0.08
0.14 0.08
0.08
0.06
0.07
0.10
0.05
0.04
0.20
0.12
0.13
0.36
0.13
0.09
0.17
0.07
0.11
reshading
0.17
0.04
00.1
0.15
0.17
0.13
0.12
0.19
0.13
jigsaw
0.26
0,08
0.14
0.13
0.09
0.06
0.07
0.07
0,08
0.05
0.18
0,11
0.12
0.14
0.11
0.07
0.14
0.07
0.10
depch. enclidean
0.15
0.06
0.12
0.09
0.10
0.11
0.11
0.09
0.07
0.01
0.18
0.12
0.17
0.17
0.14
0.12
0.20
0.10
0.13
segment- unsup3d
0.09
0.14
0.08
keypoints3d
0.16
0.08
0.15
0.10
10
0.08
0.09
0.09
0.10
0.05
0.21
0.16
0.17
0.19
0.18
0.12
0:21
D.11
0.13
class dass-soene
0.17
0:08
0.15
0.12
0.17
0.10
0.13
0.12
0.10
0.05
0.21
0.14
0.18
0.23
0.19
0.16
0.22
0.12
0.18
class-abject
0.12
0.03
0.16
0.19
0.17
0.13
0.17
timom/oovveet-largg
0.31
0.26
0.36
0.27
0.19
0.12
0.13
0.28
0.25
0.11
0.15
0.11
0.19
0.18
0.12
0.13
0.16
0.12
0.15
Dip//ViT-L/1448 336px
0.30
0.08
0.29
0.22
0.20
timm//oomnect-baee
0.31
0.26
0.36
0.28
0.17
0.18
0.19
0.19
0.20
0.15
0.23
0.18
0.27
0.25
0.19
0.20
0.25
0.20
0.22
clip/ViT-/14
0.28
0.19
0.27
0.23
0.19
0.21
0.17
0.20
0.19
0.09
0.34
0.22
0.39
0.27
0.21
0.21
0.30
0.22
0.21
tinm//oovneet-sni
clip/ViT-B/16
0.34
0.23
0.31
0.26
0.24
0.22
0.20
0.24
0.25
0.12
0.33
0.25
0.33
0.29
0.24
0.23
0.33
0.22
0.25
v-jepaajepu2-vvil-bee
0.28
0.31
0.33
0.25
0.18
0.35
0.28
0.35
0.32
0,25
0.23
0.25
0.24
0.23
0.29
0.24
opt
0.70
mmaction2/swin-tigg
0.35
0.25
0.35
0.23
0.30
0.20
0.22
0.32
0.27
0.22
0.34
0.29
0.31
0.30
0.26
0.27
0.33
0.23
0.29
timm/convnext-ii
0.19
0.24
0.29
0.26
0.26
0.24
0.32
0.39
0.39
0.34
0.34
0.37
0.29
0.34
mmactionn2/vvit-ssmm
0.56
0.37
0.40
0.26
0.31
0.19
0.19
0.39
0.29
0.30
0.34
0.29
0.23
0.28
0.23
0.22
0.28
0.20
0.24
mmaactio22tinesfomme
0.39
0.33
0.37
0.28
0.31
0.24
0.23
0.31
0.23
0.23
0.32
0.29
0.28
0.30
0.25
0.25
0:38
0.27
0.30
timm//rsnet18
viss//simllr-eeama 101
0.32
0.22
0.35
0.39
0.45
0.40
0.39
0.35
0.33
0.25
0.42
0.39
0.43
0.48
0.38
0.43
0.57
0.42
0.48
clip//iT-B/32
0.34
0.26
0.37
0.39
0.48
0.41
0.40
0:42
0.27
0.50
0.43
0.51
0.53
0.43
0.45
0.59
0.44
0.51
0.42
0.41
Limm/resnet50
0.33
0.25
0,36
0.42
0.52
0.41
0.42
0.41
0.42
0.36
0.50
0.46
0.48
0.53
0.43
0.48
0.56
0.45
0.55
vge/VGGTT-B IB
0.39
0.23
0.32
0.42
0.49
0.43
0.42
0.46
0.35
0.55
0.43
0.59
0.57
0.45
0.45
0.57
0.43
0.58
clip/RN50
0.45
0.28
0.44
0.55
0.55
0.45
0.47
0.54
v-jepavvjega22 vit giant
0.46
0.45
0.51
0.49
0.57
0.33
0.29
0.64
0.52
0.65
0.58
0.53
0.43
0.48
0.42
0.42
0.53
0.37
0.51
0.44
0.33
0.66
0.48
0.48
0.45
0.43
0.49
vejepaejea22-vit wiittniin-ittttt---- Ait--gian--844
384
0.57
0.64
0.68
0.53
0.56
0.34
0.30
0.85
0.61
0.86
0.62
0.61
0.50
0.51
0.51
0.41
0.51
0.34
0.47
Table $3.2: Model-brain alignment under RSA, normalized to NeuroAl Turing Test.
18

---

Published as a conference paper at ICLR 2026
roi
VI
V2
V3
V4
V8
PIT
PFC
V3A
V38
V6
V6A
V7
IPS1
MT
MST
EST
1.11
1.02
1.03
model
egamotion
0.03
0.01
0.01
0.00
0.02
0.03
0.03
-0.02
.0.01
0.01
0.01
.0.00
0.01
0.04
0.04
0.03
0.01
0.03
0.02
room-l
0.06
0.02
0.02
0.02
0.03
0.08
0.08
-0.00
0.01
0.01
0.02
0.02
0.02
0.04
0004
000
0.05
0.05
0.08
colorization
0.07
-0.00
004
0,55
curvature
0.06
0.04
0.03
0.02
0.03
0.04
0.04
-0.00
0.00
0.02
0.02
0.00
0.01
0.04
0.05
0.03
0.03
0.05
0.03
point-marching
0.04
0.22
0.02
0.01
0.02
0.02
0.03
-0.01
0.00
0.02
0.01
0.01
0.02
0,05
0004 0.03
0.01
0.03
0.03
keypoints2d
0,01
002
fixated-pose pase
0.03 0011
0.00
0.00
0.02
0.03
0.04
-0.01
-0.00
0.01
0.01
0.01
0.02
0,02
0.02
0.02
0.02
0.04
0.03
edge texture
0.08
0.04
0.05
0.04
0.03
0.04
0.03
-0.01
.0.00
0.01
0.01
-0.11
0.01
0,03
0.03
0.02
0.03
0.05
0.03
vanishing-point
0.04
noefixatied-pout
0.02
-0.00
-0.01
00.01
-0.00
0.01
0.02
-0.01
-0.00
0.00
0.01
-0.00
0.01
0.03
0.02
0.01
00.00
0.02
0.01
normal
0.06
0.04
0.06
0.06
0.06
0.09
0.07
0.01
0.02
0.03
0.03
0.03
0,03
0.09
0.07
0.06
0.08
0.09
0.08
segment--semattic
0.08
0.05
0.06
0.07
0.02
0.03
0.03
0.03
0.07
0.06
0.10
0.07
edge-occhusse
0.08
0,06
0.01
0.02
0.03
0.01
008
0.05
0.07
inpainting
0.03
0.01
-0.00
40.00
0:01
0.04
0.04
-0.01
-001
0.01
0.00
-0011
0,00
0.03
0.04
0.00
0.02
0.05
0.02
depti-zbufferr
0.07
0.04
0.05
0,06
0.01
0.03
0.03
0.03
0.02
0.07 0.05
0.10
0.07
denoising
0.02
-0.00
00011
-0.01
002
0.01
0.01
autoencoding
0.01
-0.00
-0.01
40.01
-0.01
0.01
0.01
-0.01
-001
0.01
0,00
-0.11
0,00
0,01
0.0
0.01
0.00
0.02
0.00
sognscat-unsup255
0.06
0.04
0.06
0,06
0.01
0.03
0.02
0.03
0.02
0.06
0.05
0.10
0.07
reshading
0.06
0,07
0.01
0.04
0.03
0.08
0.07
0.09
jigsaw
0.05
0.02
0.02
0.02
0.03
0.05
0.05
-0.01
0.11
0.03
0.02
0.00
0.03
0.05
D
0.0
0:3
0.05 0.04
deptb-cuclidean
0.06
0.05
0.06
0,07
0.02
0.03
0.03
0.03
0.02
0.08
0.06
0.08
0.10 0.09
segment- unsuppd
0.09
0,04
4.00
0.04
0.03
keypoints&d
0.08
0.05
0.06
0.08
0.077
0.11
0.09
0.01
0.03
0.03
0.03
0.03
0.04
0.10
0.09
0.06
0.08
0.12 0.08
class-scenc
0.09
0.07
0.08
0.09
0.11
0.10
0.03
0.06
0.04
0.06
0.06
0,06
0,11
0.09
0.08
0.11 0,10
class-object
0,10
0,11
0.04
0.10
0,10
timm/convnext large
0.14
0.12
0.14
0.19
0.19
0.23
0.21
0.08
0.14
0.06
0.11
0.12
0.12
0.26
0.20
0.20
0.23
0.27
0.24
ciiy/Vi--L/1-e 336px
0,15
0.13
0.14
0.19
0.19
0.24
0.22
0.07
0.14
0.08
0.12
0.13
0.14
0.28
0.24
0.23
0.23
0.27
0.25
timm//oonveet--uee
0.13
0.20
0.22
0.22
clip/Vi--/144
0.14 0.12
0.14
0.19
0.19
0.23
0.23
0.07
0.15 0.09
0.12
0.12
0.14
0.28
0.24
0.24
0.22
0.28
0.24
timm/convnext-ooe small
0.12 0.13
0.15
0.20
0.18
0.25
0.21
0.07
0.13
0.07
0,11
0.13
0.12
0.28
0.25
0.22
0.24
0.28
0.23
clip/VT--//16
0.14
0.15
0.18
0.24
0.07
0.15
0.08
0.15
0.23
0.23
0.28
0.23
v-jepa/ v-jepa/xjeuu2-
0.13 0.15
0.18
0.22
0.22
0.25
0.20
0.13
0.18
0.14
0.18
0.20
0.13
0.35
0.35
0.26
0.26
0.29
0.26
opt-cwm/ogt mwwppttwwmm
0.15
0.14
0.16
0.19
0.17
0.20
0.16
0.12
0.15
0.13
0.15
0.16
0,11
0,25
0.24
0.19
0.19
0.23
0.18
mmsction2/wwin-te tiny
0.11
0.12
0,14
0.07
0.10
0.07
0.09
0.22
0.21
0.22
0.20
timnvoonvnext-ting
0.13 0.12
0.15
0.17
D.17
0.22
0.20
0.07
0.12 0.08
0.10
0.10
0.12
0.27
023
0.21
0.22
0.26
0.24
0.16
0.14
0.15
0.17
0.17
0.22
0.21
0.08
0.11
0.10
0.10
0.10
0.12
0,28
0.25
0.21
0.16
0.24
0.21
0.07
0.05
0.06
0.07
0.09
0.03
0.05
0.04
0.05
0.05
0.09
0.10
0.10
0.12
0.11
8
0.13
0.14
0.18
0.08
0.15
0.09
0.12
0.15
0.14
0.30
0.25
0.25
0.27
0.25
visdlsimel-ressctt 101
0.15
0.13
0.16
0.21
0.22
0.27
0.25
0.10
0.16
0.09
0.14
0.16
0,16
0.30
0.26
0.24
0.26
0.30
0.26
clip/ViT-B332
0.15
0.13
0.16
0,21
0.23
0.29
0.27
0.10
0.17
0.11
0.15
0.17
0.17
0.34
0.28
0.27
0.29
0.35
0.30
visdUsimell-ressett500
0.15
0.15
0.21
0.10
0.16
0.10
0.14
0.25
0.23
0.27
1immviiinet50
0.12
0.11
0.13
0.19
0.21
0.28
0.25
0.08
0.16
0.10
0.13
0.16
0.16
0.33
0.28
0.26
0.26
0.31
0.28
vga/VGGF-18
0,16
0.14
0.16
0.22
0:23
0.31
0.29
0.12
0.20
0.12
0.18
0.20
0.19
0.35
0.31
0.28
0.30
0.35
0.32
clip/RN50
0.13
0.11
0.33
0.33
v-jepahjepa2 vit-giant
0.16
0.16
0.20
0.24
0.25
0.28
0.24
0.16
0.21
0.16
0.20
0.23
0.16
0.41
0.40
0.29
0.28
0.31
0.30
w-jeparkgenn2-vvt-lue -att-aa-ee
0.17
0.18
0.21
0.24
0.24
0.26
0.22
0.16
0.20
0.15
0.20
0.23
0.15
0.38
0.37
0.27
0.28
0.30
0.28
v-jepa/
0.16
0.23
0.19
0.38
0.27
0.28
Table S3.3: Model-brain alignment under Linear Predictivity.
19

---

Published as a conference paper at ICLR 2026
roi
VI
V2
V3
V4
V8
PIT
IFC
V3A
V3B
V6
V6A
V7
IPSI
MT
MST
FST
LO1
LO2
LO3
model
egomotion
0.08
0.05
0.01
-0.00
0.09
0.09
0.09
-0.17
-0.04
-0.28
0.06
-0.01
0.04
0.08
0.07
0.07
0.01
0.07
0.07
room-layouu
0.22
0.16
0.10
0.08
0.15
0.16
0.17
4008
0.05
-0.19
0.09
0.09
0.14
0.0
0.08
0.09
0.17
0.15
0.14
colorizaion
0.28
0.27
0.24
0.14
0.23
0.24
0.13
-007
-0.01
-0.29
0.06
-0.01
0.05
0.10
0.09
0.12
0,16
0,22
0.12
curvature
0.19
0.08
-007
-0.66
0.07
0.0
0,14
0,10
point matching
0001
0.11
0.07
0.05
0.09
0.07
0.05
00.15
0.02
-0.27
0.04
0.02
0.08
0.09
0.08
0.07
0.03
0.08
0.08
keypoinas2d
0.08
0.05
0.04
-0.02
-001
0.08
0.00
-0.11
-007
-0.31
-0,02
-0.05
0.00
0.04
0.04
0.02
0.04
0.04
0.03
lixated-poee pose
-0.11
0,10
edge-texture
0.38
0.23
0.21
0.13
0:15
0.12
0.08
-0.15
-002
-0.30
0.04
-0.03
0.96
0.06
0.06
0.06
0.11
0.12
0.08
vanishing-poiaa
0.14
0.13
0.10
0.05
0.12
0.15
0.11
-0.05
-0.04
-0.27
-0.00
-0.04
-0.03
0.11
0.10
0.12
0.15
0.14
0.12
nonfixated pose
0.01
00.01
40.66
-0.05
0.05
-.12
0.03
-0.29
0.05
-0.11
0.02
0.05
0.04
0.04
-0.05
0.04
0.04
normal
0.24
0.22
0.01
-0.22
0.16
0.24
segment-semeen semaanic
0.34
0.26
0.31
0.25
0.32
0.31
0.24
0.07
0.15
-0.09
0.18
0.11
0.22
0.18
0.17
0.16
0.31
0.31
0.23
edge-ocelusion
0.36
0.30
0.29
0.23
0.52
0.30
0.24
002
0.12
-0:05
0.15
0.04
0.15
0.22
0.18
0.13
0.29
0.30
0.22
inguiniing
0.04
0.04
40.02
40.29
-005
0.02
0.05
0.04
0.12
0.07
depil-zbuller
0.33
0.23
0.24
0.21
0.31
0.34
0.26
0.04
0.14
-0.13
0.16
0.06
0.17
0
0.14
0.12
0.26
0.31
0.21
denoising
-0.04
0.04
-0.02
-0.07
0.04
0.06
0.02
0.12
-0.66
-0.29
00.00
-0.03
0.01
0.02
0.04
0.04
-0.01
0.05
0.02
sutoencoding
-0.07
-0.02
-0.05
+0.07
00.05
0.03
0.01
-0.12
-0.07
-0.28
+0.00
-0.04
0.00
0.03
0.02
+0.05
0.03
0.01
segment- u5
0.24
0.08
-0.18
0.14
0.16
0.13
0.22
reshading
0.36
0.25
0.32
0.27
0.38
0.38
0.30
DOX
0.20
40.04
D
0.12
0.22
0.22
0.18
0.18
0.37
0.40
0.28
jiguawr
0.25
0.12
0.10
0.07
0.13
0.15
0.13
-0.13
0.03
-0.15
0.09
0.00
0.15
0.11
0.10
0.08
0.07
0.14
0.09
deplt-cuclidean
-011
0.26
segment- 2n
0.41 0.27
0.23
0.13
0.22
0.21
0.11
0.009
00.01
-0.18
0.06
-0.03
0.09
0.10
0.08
0.07
0.14
0.18
0.08
keypoints3d
0.41 0.29
0.29
0.28
0.57
0.39
0.27
0.06
0.16
-0.12
0.16
0.09
0.18
0.22
0.19
0.19
0.31
0.36
0.26
class scene
0.41 0.34
0.38
0.35
0.54
0.33
0,16
0.32
-0.09
0.28
0.20
0.33
0.25
0.20
0.20
0.44
0.35
0.33
class-abject
0.56 0.50
0.40
0.26
0.05
0.33
0.39
0.28
0.48
0,42
0.40
timm/com nett--lge
0.79 0.66
0.69
0.71
0.9
0.82
0.71
0.57
0.77
0.47
0.61
0:45
0.67
0.56
0.44
0.54
0.96
0.84
0.73
0.94 0.71
0.70
0.71
0.3
033
0.74
0.52
0.75
0.47
0.65
0.49
0.76
061
0.51
0.60
0.97
0.81
0.77
timm/oommaet
0.75
0.60
0,76
0.67
clip/ViT-L/14
0.87 0.66
0.70
0.69
0.95
0.81
0.76
0.45
0.80
0.60
0.61
0.43
0.75
0.60
0.53
0.63
0.91
0.84
0.75
timm//oomaast-snee small
0.73
0.70
0.75
0.74
0.90
0.86
0.69
0.53
0.72
0.56
0.57
0.50
0.67
0.61
0.55
0.59
1.01
0.88
0.71
clip/NiT-B/16 B/16
0.84
0.62
0.73
0.68
0.85
0.73
0.52
0.38
0.64
0.57
0.77
0.58
0.50
0.61
0.97
0.89
0.72
v-jepa--jepaZ-vit seseepe-vitt-tugeee hitee
0.74
0.79
0.87
0.82
1.0
0.00
0.68
DA8
0.98
1.04
DM2
0.76
0.71
0.77
0.77
0.68
1.10
0.89 0,80
ggt-ewmmopt-com
0.95
0.80
0.79
0.71
0.86
0.73
0.55
1.03
0.85
1,10
0.79
0.63
0.57
0.55
0.52
0.50
0.79
0.70
0.58
mmnoction2swin--iig
0.59
0.56
0.60
0.54
0.80
0.68
0.55
0.51
0.56
0.35
0.47
0.34
0.52
0.56
0.46
0.55
0.74
0.69
0.62
fimm/connest next dity
0.66
0.47
0.21
0.67
manetion2/nviit small
0.92
0.76
0.75
0.62
0.84
0.75
0.68
0.60
0.63
0:48
0.55
0.36
0.61
0.60
0.54
0.56
0.66
0.72 0.66
0.32
0.29
0.27
0.27
0.45
0.37
0.33
0.19
0.28
0.07
0.27
0.18
0.31
0.24
0.19
0.25
0.40
0.36
0.33
timm/resnet1 18
0.81
0.69
0.47
0.66
0.78
0,64
0.66
0.77
visalsimcli.reeeett
0.99
0.72
0.78
0.79
0.72
0.72
0.85
1.08
0.82
cli//VT--8/52
0.95
0.76
0.80
0:1
1.16
1.04
0.94
0.0
0.93
0.83
D.82
0.64
0.90
0.73
0.61
0.72
1.21
1.04
0.92
visdkiinell-eeeett
0.97
0.70
0.77
0.78
1.07
0.85
0.77
0.85
0.72
0.71
0.55
0.81
0.65
0.55
0.62
1.04
0,96
0.82
55mm/resace10
0.55
0.63
0,86
vgg/VGGT-11
0.95
0.75
0.81
0.84
1.14
1.09
1.01
0.92
1.10
1.11
0.96
0.75
1.03
0.75
0.67
0.75
1.27
1.09
0.99
clip/RN50
0.80
0.72
0,83
0.89
1.20
1.11
1,02
0.84
1.12
0.88
0.89
0.70
1/04
081
0.71
0.79
1,33
1.15
1.01
v-jepasjjeaa2-vii giant
0.95
0.88
0.91
1.23
1.01
0.81
1.21
1.12
1,06
1.04
0.88
0.83
0.58
0.88
0.77
1.19
0,96
0.93
s-jepw-jepaz-vit-umee Lange
1.30
0.97
1.00
0.92
1.18
0.95
0.74
1.26
1.08
1,21
0.94
0.86
0.79
0.81
0.72
1.18
0.94
0.87
v-jeparjeea2-vit-gaae
0.93
0.96
0.95
0.87
1.17
0.94
0.77
1.18
1.06
1.13
0.95
0.77
0,74
0.5
0.84
0.73
1.11
0.91
0.87
Table $3.4: Model-brain alignment under Linear Predictivity, normalized to NeuroAl Turing Test.
IMRI-derived APS Connectivity-eerived APS Random connectivity (95th percentile) Random connectivity (5th percentile)
V1
0.79
0.68
0.40
-0.24
V2
0.78
0.61
0.34
-0.25
0.58
0.25
0.26
00.23
V4
0.57
-0.02
0.28
-0.24
VS
0.68
0.09
0.25
-0.26
PIT
0.78
0.02
0.30
-0.32
FFC
0.82
0.20
0.28
-0.35
V3A
0.75
0.27
0.32
-0.29
V3B
0.76
0.52
0.30
00.11
V6
0.82
0.18
0.31
-0.22
V6A
0.80
0.30
0.32
00.0
V7
0.79
0.34
0.30
-0.31
IPSI
0.78
0.53
0.29
00.2
MT
0.81
0,24
0.33
-0.44
MST
0.81
0.22
0.33
-0.34
FST
0.81
0,23
0.29
-0.36
LOI
0.72
0.03
0.24
-0.4
LO2
0.79
-0.01
0.28
-0.36
LO3
0.75
0.26
0.26
-0.35
Table S3.5: APS.
20

---

Published as a conference paper at ICLR 2026
Model-brain alignment
RSA
Linear Predictivity
0.4
Normalized algnament scoze
0.0
0.0
IA
0.4
0.0
0.0
0.4
0.8
8A
0.0
0.0
0.8
0.4
ISK
0.4
0.0
-
is 1p
9 mhae
man 1o00000
Aun
Jun wat///////ooo
10 10R1AAA
0 SENU A//5A
some To
Alt /
0steusie
8
09Na//iio
ooooauu rarun
(Pluss/ee
zedala
zedala/ddefA
ddd
Models
sorted according to avg. normalized RSA score
Figure $3.1: Normalizing alignment scores shows that the benchmark is saturated for LP but not for
RSA. Legend see main Figure 2
21

---

Published as a conference paper at ICLR 2026
Alignment patterns
VI
V2
1.0
1.0
0.5
0.5
0.5
0.0
V3
1.0
1.0
0.5
0.0
0.4
1.0
0.2
0.1
0.5
FFC
1.5
V3A
0.3
0.2
0.4
V3B
V6
0.75
0.3
1.0
Horcn Frred Afgommnt S00TM
0.5
0.1
0.5
streamlite deasity
V6A
1.0
0.25
0.5
0.1
0.00
PS1
0.2
0.1
0.5
0.1
0.5
0.2
MST
FST
0.3
1.0
0.2
0.3
L01
LO2
1.0
0.2
0.3
1.0
0.1
0.5
0.2
LO3
ERO
VaA
IPSI
MT
MST
FST
LO1
102
fMRI-derived
0.1
structural connectivity-derived
Vo
PTT
VEN
V36
VBA
17
IPS)
M1
MST
EST
101
1,02
L03
Target
Figure S3.2: RSA-based fMRI-derived and stryçtural connectivity-derivee alignment patterns.

---

Published as a conference paper at ICLR 2026
Brain-brain alignment pattern analysis
b
LP-based fMRI-derived
Alignment pattern similarity
alignment petterns
DMR derived
donnect
Aligament score
1.0-
0.5
0.0-
0.0
01
Y2
V3-
M
V8-
PIL
FFC
YEA
VS
VBA
IPST
MT
MST
ESt
L01
102-
103
V7
V2
V4
V8-
PFC
VIA
VIB
v6-
VOA
V7-
(1S)
MT
5ST
1,03
103-
Target ROIS
Predictor
Figure $3.3: Brain-brain alignment patterns analysis L.P-based fMRI-derived and structural
connectivity-derivee alignment patterns. Legend see main Figure $
23

---

Published as a conference paper at ICLR 2026
Alignment patterns
1.0
1.0
0.5
0.5
1.0
1.0
1.0
0.5
0.0
V8
PIT
0.3
0.2
0.2
FFC
V3A
1.5
1.0
0.6
1.0
0.2
0.5
0.1
0.2
0.0
V3B
0.2
horca lroed algammnt S0U1V
0.25
0.1
streamline deasity
0.00
V6A
0.3
0.50
0.2
0.25
PP1
MT
1.0
0.3
0.2
0.5
0.2
0.1
0.0
0.2
MST
0.3
FST
0.1
0.0
0.0
0.3
L01
LO2
0.2
0.2
0.1
0.0
0.1
0.0
0.3
LO3
2
fMRI-derived
0.1
structural connectivity-derived
MT
MST
FST
1.11
102
Tanget
Figure S3.4: LP-based fMRI-derived and structural connectivity-derivel alignment patterns.

---

Published as a conference paper at ICLR 2026
Model-brain alignment pattern analysis
IMRI-derived AP
model derived
V1
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miso pdojusss pio
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Figure S3.5: Model-brain alignment patterns analysis for LP-based alignment patterns. Legend main Figure 4
25

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Published as a conference paper at ICLR 2026
Effect of training model-derived alignment patterns
VI
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Figure S3.6: RSA-based AP for trained untrained models.
26

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Published as a conference paper at ICLR 2026
Effect of model training on aligmment pattern similarity
V1
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Figure S3.7: RSA-based APS for trained untrained models.
27

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Published as a conference paper at ICLR 2026
Effect of training on model-derived alignment patterns
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Figure S3.8: L.P-hased AP for trained untrained models.
28

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Published as a conference paper at ICLR 2026
Effect of model training aliqmment pattern similarity
V3
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Figure $3.9: LP-based APS for trained untrained models.
29

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Published as a conference paper at ICLR 2026
Scores (alignment and APS) across all layers
RSA
vissl/simclr resnet1 1000epochs
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Figure S3.10: Scores (alignment and APS) for all models from the VISSL model family and all ROIs. Top panels show RSA, bottom panels show LP. Stars indicate best layers selected based on alignment score.
30

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Published as a conference paper at ICLR 2026
v jepalvjepa2_vtt_peeee vit
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Figure S3.11: Scores (alignment and APS) for all models from the V-JEPA model family and all ROIs. Legend see Fig. S3.10
31

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Published as a conference paper at ICLR 2026
clip/RN50
0.3
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clip/Vt-B/32
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Figure S3.12: Scores (alignment and APS) for all models from the CLIP model family and all ROIs. Legend see Fig. S3.10
32


Reviews:
[{"review_id": "U5vvuS0wxj", "reviewer": "Reviewer_jG2B", "summary": "This paper benchmarks tens of image and video models on the BOLD-Moments fMRI dataset using Representational Similarity Analysis (RSA) and Linear Predictivity (LP), argues that many models are practically equivalent despite their rankings because their model→brain alignment falls within the distribution of brain↔brain alignment, and introduces Alignment Pattern Similarity (APS)—a connectivity-grounded test of whether a model preserves each ROI’s cross-region similarity pattern. The key claim is that, although some models score well on RSA/LP, they generally fail to reproduce brain-to-brain cross-region patterns under APS, whereas brains do.", "questions": "- How sensitive are the practical-equivalence findings to the brain↔brain baseline (averaged vs. individual subject predictors; with/without hyperalignment; within-subject splits)?\n- Could you compare your equivalence criterion to the NeuroAI Turing Test (Feather et al., 2025) to delineate conceptual differences and overlapping assumptions?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "- The paper tackles the growing concern that conclusions about “brain alignment” depend on metric choice, and proposes a relational criterion that goes beyond local fits.\n- The practical-equivalence analysis is a strong way to assess when model-ranking differences are statistically meaningful.\n- Creative attempt to incorporate anatomical priors and cross-region dependencies into model evaluations.\n- It's nice that the paper includes a diverse set of image and video models across supervised and self-supervised objectives.", "weaknesses": "- My biggest concern is that the three aims—(1) benchmarking image/video models on the BOLD Moments dataset, (2) demonstrating practical equivalence of several models (in the sense that they lie within the brain-brain similarity range), and (3) introducing APS—feel only loosely connected. Each of these components could stand as an independent contribution, but when presented together, the narrative lacks a clear causal or conceptual throughline. For instance, the benchmark and equivalence analyses establish the limitations of RSA/LP metrics but are not explicitly framed as motivating APS; instead, APS appears as a parallel idea rather than a methodological extension.\n- The practical-equivalence conclusion hinges on the brain↔brain baseline, which is constructed by averaging voxel responses across subjects before comparing to a held-out subject. This raises concerns about functional alignment (are voxels/topographies aligned across individuals?) and the arbitrariness of cross-subject averaging. The authors should: (i) report within-subject baselines, (ii) test hyperalignment or response-based alignment, and (iii) evaluate subject→subject prediction without averaging. Related ideas have appeared in the NeuroAI Turing Test (Feather et al., 2025), which formalizes a similar distributional criterion for model evaluation; citing and differentiating from that work would clarify novelty.\n- The phrase “shifting the focus from single-metric rankings to the stability of rankings” is confusing, since two metrics (RSA, LP) are still used. The authors actually examine whether model orderings are stable given subject variability—i.e., when multiple models are within the brain↔brain alignment range. This should be explicitly defined early to avoid misinterpretation.\n- The statement that “recent benchmarks of video models have relied on a single alignment metric, while metric-comparison studies rarely include modern video architectures” should be supported with citations", "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": "9NMaZdKIwA", "reviewer": "Reviewer_NsfX", "summary": "This paper evaluates 47 vision models on the BOLDMoments video fMRI dataset using RSA and linear predictivity (LP) to assess brain-model alignment. The authors identify V-JEPA2 as achieving strongest alignment but show that many models are \"practically equivalent\" within subject-level variability. They introducxe Alignment Pattern Similarity (APS), a novel metric that compares cross-region alignment patterns to anatomical connectivity patterns. The key finding: while normative models can align well with individual brain regions, they fail to reproduce brain-to-brain cross-region alignment patterns, revealing a fundamental limitation of current approaches.", "questions": "Please refer to weaknesses.", "limitations": "", "rating": 4, "confidence": 3, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "1. **Important conceptual contribution:** APS is a genuinely novel idea that addresses a real gap in the field. The insight that models should preserve relational structure across regions (not just match individual regions) is valuable and biologically motivated.\n2. **Comprehensive model evaluation:** Testing 47 models spanning diverse architectures (CNNs, Transformers), objectives (supervised, self-supervised, multimodal), and modalities (image, video) on a large video dataset is thorough.\n3. **Strong empirical results:** The finding that brain-to-brain APS is high while model-to-brain APS is near-zero is an interesting result.", "weaknesses": "1. **Figure quality is severely lacking:** The sketch-based design of figure 1 is not ideal for a publication at a top tier conference. I would recommend redoing it with vector graphics. The other figures have almost illegible axis labels with overlapping/compressed model and ROI names. \n2. **Limited to RSA and LP Despite Known Limitations:** Paper correctly identifies that \"conclusions depend strongly on choice of metric\" yet only uses RSA and LP. Missing modern metrics like CKA[1], RTD[2] and NSA[3]\n3. **APS Validation is Incomplete:** No null model is presented. What is expected APS for random alignment patterns? No p-values or significance tests. Could spatial proximity or retinotopy explain results instead of connectivity? Does APS generalize to NSD or HCP 7T?\n4. **Model-Brain APS Results Are Hard to Interpret:** Low APS could mean: (a) models fundamentally limited, (b) wrong layer selection, or (c) APS too strict but the authors assert (a) without ruling out the other options. Layer-wise analysis of APS is also missing.", "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": "vDgO2OepQd", "reviewer": "Reviewer_yHdD", "summary": "The paper benchmarks 47 image and video models on BOLD-Moments fMRI with RSA and linear predictivity, finding that rankings depend on the metric and many models are practically equivalent within subject variability. It proposes Alignment Pattern Similarity, grounded in structural connectivity, to test whether models preserve each ROI’s cross-region similarity profile, and reports that brains align with brains while models generally fail to match these patterns, arguing for stricter anatomy-informed evaluation.", "questions": "Please see the weaknesses section for the question.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "- The authors raise a very important and timely issue regarding AI-model alignment.\n- The authors propose a solution that I think is quite novel, which is checking the inter-regional similarities between brain regions and with one region replaced by its best-fit model.\n-  Showing that most models fall within subject-level variability is a much-needed report.\n-  I personally find the high APS for brain-brain alignment very interesting (but could the author please provide the baseline elaborated in the weaknesses section below?).", "weaknesses": "- I have a philosophical, high-level concern. I am not sure if requiring the model representation (that has a best fit to one brain region) to have relationships to other brain regions in a way that one brain region relates to those brain regions is an unnecessarily strict requirement.  But I do think it is still an interesting thing to check and report, which could be the author’s point.\n\n- In this line of thought, I think another interesting analysis would be\n1. First, (within a single model) measure the similarity between layers that have high alignments to brain regions of interest. Call that s’_model_RSA(i,j) (or s’_model_LP) for brain regions i and j. For example, say layer 3 corresponds to ROI 1 of the brain, and layer 5 corresponds to ROI 2 of the brain. Then s’_model_RSA(1,2) measures the similarity between layer 3 and layer 5 of the networks.\n2. Measure and report R^2 between S’_model_RSA and brain-to-brain S_RSA (the latter one is from the paper e.g. Figure 5d top left red line).\nWould it be easy to perform this analysis? This is less strict than the author’s requirement. Qualitatively speaking, it is like saying we don’t care exactly how layers talk to each other (and how the ROIs talk to each other), and the only thing we care about is the relationship (RSA/LP value) between these layers and the relationship (RSA/LP value) between these ROIs. We might want these relationships to be similar (high R^2) between the model and the brain.\n\n- Perhaps more importantly, I think the authors should report the baseline for APS, where the ROI connectivity graph is randomly generated.\n\n- I understand that the paper's idea and methodology are not easy to explain, but I think the clarity of the paper can be dramatically improved. Figure 1 is well-intended, but it only makes sense after reading the entire paper. As a reader, it is especially unclear what the cartoon plots under \"Alignment Patterns\" indicate (one would wonder that the x-axis and y-axis are), unless they have read section 3.5 (which I don't think is also clearly written). I am not clear what the best way is to explain the overall pipeline, but I don't think Figure 1 helps much at all.  In section 3.5, the last sentence was nearly impossible to understand.", "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)