Disentangling the Factors of Convergence between Brains and DINOv3
Authors: Joséphine Raugel
Marc Szafraniec
Huy V. Vo
Camille Couprie
Jérémy Rapin
Stéphane d'Ascoli
Patrick Labatut
Piotr Bojanowski
Valentin Wyart
Jean-Remi King
Venue: ICLR 2026 Poster (2026)
Keywords: NeuroAI; Brain–AI alignment; Representational alignment; Hierarchy alignment; Emergence; Vision transformers; Self-supervised learning; fMRI; MEG; Temporal dynamics; Spatial dynamics
Cortical hierarchy; Development
Primary area: applications to neuroscience & cognitive science
TL;DR: We disentangle how architecture, training and data shape brain-like representations and hierarchy in vision models (eg DINOv3). Strikingly, the development of these vision models over training mirrors several aspects of the human brain development.

Abstract: Many AI models trained on natural images develop representations that resemble those of the human brain. However, the factors driving this brain-model similarity remain poorly understood. To disentangle how the model, training and data independently lead a neural network to develop brain-like representations, we train a family of self-supervised vision transformers (DINOv3) that systematically vary these factors. We compare their representations of images to those of the human brain recorded through fMRI and MEG, providing high resolution in both spatial and temporal analyses. We assess the brain-model similarity with three complementary metrics focusing on representational similarity, topographical organization, and temporal dynamics. We show that all three factors - model size, training amount, and image type - independently and interactively impact each of these brain similarity metrics. In particular, the largest DINOv3 models trained with the most human-centric images reach the highest brain-similarity. These findings generalize across seven additional models. This emergence of brain-like representations in AI models follows a specific chronology during training: models first align with the early representations of the sensory cortices, and only align with the late and prefrontal representations of the brain with considerably more training. 
Finally, this developmental trajectory is indexed by structural and functional properties of the human cortex: representations acquired last by the models specifically align with cortical areas with the largest developmental expansion, thickness, least myelination and slowest timescales. 
Overall, these findings disentangle the interplay between architecture and experience in shaping how artificial neural networks come to see the world as humans do, thus offering a promising framework to understand how the human brain comes to represent its visual world.

Published as a conference paper at ICLR 2026
DISENTANGLING THE FACTORS OF CONVERGENCE BETWEEN BRAINS AND DINOV3
Joséphine Raugel 1.2
Mare Szafraniec'
Huy V. Vo!
Camille Couprie'
Patrick Labatut
Piotr Bojanowski!
Valentin Wyart?
Jean-Rémi King'
Mett AI
Ecole Normale Supérieure, PSL Research University (empoiiineremmttte com
(jeanremi,
ABSTRACT
Many Al models trained on natural images develop representations that resemble those of the human brain. However, the factors driving this brain-model similarity remain poorly understood. To disentangle how the model, training and data inde- pendently lead neural network to develop brain-like representations, family of self-supervised vision transformers (DINOv3) that systematically vary these factors. We compare their representations of images those of the human brain recorded through fMRI and MEG, providing high resolution in both spatial and temporal analyses. We assess the brain-model similarity with three comple- mentary metrics focusing on representational similarity, topographical organiza- tion, and temporal dynamics. We show that all three factors model size, training amount, and image type independently and interactively impact each of these brain similarity metrics. In particular, the largest DINOv3 models trained with the most human-centric images reach the highest vimin-simillartt. These findings generalize across seven additional models. This emergence of brain-like repre- sentations in AI models follows specific chronology during training: models first align with the early representations of the sensory cortices, and only align with the late and prefrontal representations of the brain with considerably more training. Finally, this developmental trajectory is indexed by structural and func- tional properties of the human cortex: representations acquired last by the models specifically align with cortical areas with the largest developmental expansion, thickness, least myelination and slowest timescales. Overall, these findings disen- tangle the interplay between architecture and experience in shaping how artificial neural networks come to see world as humans do, thus offering a promising framework to understand how the human brain comes to represent its visual world.
train
A
Measures
Factors
Similarity Metrics
DINOV3
Model
fMRI
Spatial
GD9D
X e good
Training size
MEG
Image type
Method Figure. A. We compare activations of DINOV3 oo Encodiin response to the same images. B. To understand the factors that steer DINOv3 towards brain activity. we train from scratch a varicty of models on different image domains nuuman-centric, satellite or biological data), with varying amounts of data. C. We compare each model to both fMRI and MEG (high spatial and temporal resolutions) by computing the linear similarity their representations and the similarity of their hierarchical organization (encoding, spatial and temporal scores).
activations of the human brain in

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Published as a conference paper at ICLR 2026
INTRODUCTION
Brain-AT similarity. Deep learning has transformed computer vision over the past decade. State- of-the-art deepnets now achieve human-level or superior performance across a variety of tasks in- cluding classification (Siméoni et al. 22025] Tschannen et al. 2025), object detection (Redmon et al. 2016), semantic segmentation (Cheng et al. 2022). ) and medical image analysis (Esteva et al. 2017
Lorenci al. 2025). Surprisingly, the internal representations of these deep learning models appear to be related to those of the human brain: multiple electrophysiology (Yamins et al. 2014a] Yamins
& DiCarlo 2016, Schrimpf al. [0118] /Zuanng et al., (02T), functional Magnetic Resonance Imag-
ing (Eickenberg et al. [0177] Millet et al. [203]]
et al. [025 Tang et al.
2023
Nikolaus et al.)
2024.. suagmeto-eneepphalgrrpyyy studies (Cichy et al. [016]] Seeliger et al.
2018
Caucheteux &
King 2022. Banvville et al. 2025) have now consistently shown that the activation patterns of these
models linearly map onto those of the cortex in response to the same images.
Theoretical importance. Understanding the principles at the origin of this representational simi- larity between AI models and the human brain is of primary importance, to understand the laws of information processing that may be universally shared across neural networks. Indeed, several lines of research (Hasson et al. 2020 Huh et al. 2024] van Rossem & Saxe 2024, Cagnetta et al. 2024 Mehrer et al. 2020 Mahner et al., 2025 Simkova et al., 2025) suggest that there exists universal principles that constrain the structure and emergence of representations in neural networks.
Unclear causes. The precise factors responsible for the representational similarity be- tween computer vision models and the human remain currently unclear. This gap of knowledge is partly due to the fact that previous studies primarily focused on pretrained networks that simul- taneously vary in training objectives, architectures and data regime (Conwell et al., 2021 Rajesh al., 2024). How each of these factors independently and interactively leads model converge to brain-like representations thus remains unclear.
Challenge:
To address this issue, we systematically train a variety of DINOv3 models (Siméoni et al. 2025). while independently varying their size, data type and training quantity. DINOv3 has the ad advantage of being self-supervised, and can thus be trained on different types of naturalistic but non-human centric and non- labelebe data such as satellite images (Siméoni et al. 2025) and biological images (Lorenci al. 2025).
Here, we compare a variety of DINOv3 models to the brain responses to images, as recorded with ultra high field (7T) functional MRI and maggett--nccephalograpp (MEG) to get a high spatial and temporal resolution of the cortical representations, respectively. For this, we implement three similarity metrics. First, we use a standard linear mapping metric, often referred to as encoding score (Naselaris al. 2011b), which evaluates the linear correspondence between the representations of two systems. Second, we evaluate, with fMRI, whether this linear mapping follows a similar spatial organization, whereby the first and last layers of the model would best match the sensory visual and prefrontal cortices, respectively. Finally we evaluate, with MEG, whether this mapping follows a similar temporal organization, whereby the first and last layers of the model best match the early and late MEG responses, respectively.
2 METHOD
2.1 APPROACH
We aim to identify the factors that make modern computer vision models process and represent nat- ural images similarly to the human brain. Following previous work (Kriegeskorte et al., 2008 Di- Carlo et al.) 2012; King & Dehaene 2014),, we rely on the definition of d'"rrrreeettatin" as "Tinearly readable information". We employ the encoding analysis procedure introduced by Naselaris et al. (001lb) to evaluate the representational similarity between an AI model and brain recordings. This linear model seeks to find whether there exists linear mapping W x that reliably predicts m-ddimensional brain activity (Y E RR*m given the d-dimensional model activation (X E R=xdd in response to n images:
arg min {1| - x +
W
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with À the ridge regularization parameter. We use linear probes maintain geometries of compared representations. We use scikit-learns Rid RidgeCV (Pedregosa et al. 2011), 10 logarithmically regularization À in between 10 and 10 and a 5-split validaton.....n...
2.2 METRICS
Encoding score Given two representations X and Y, we quantity their overall representational similarity by computing, for each split separately and then averaged, an encoding score with Pearson correlation score R E [-1,
R = cor Xtest: ytest)
We rely on encoding as the basis for our three metrics (encoding, spatial, and temporal scores), rather than decoding, decoding metrics cannot be meaningfully compared across models with different architectures and representational spaces. Following the rationale of (Naselaris et al. (011a)) encod- ing provides an interpretable mapping from model features to neural responses, comparable across architectures and training regimes. For clarity, we can either summarize the average R score across brain dimensions, or plot them all separately get information about where brain activations are linearly predictable from the model. In some analysis, we use R/max(R), the normalized encoding score, which peaks at 1.
Spatial score To assess whether model organized its processing hierarchy similarly to that of brain with spatial score, we proceed in four steps. First, we evaluate an encoding score for each dimension m of the brain, and from 22 layers k E [0, 1] the model, where the first layer, and is the last layer. Second, we identify the layer that best predicts this brain response: k". Third, approximate the hierarchical position m of each brain region, as its Euclidean distance from V1 in the standardized MNI space, in mm. Note that this is coarse approximation, as the actual cortical hierarchy does not strictly follow such distances, and may be considerably more complex (Felleman & Van Essen 1991). Finally, compute the spatial score the correlation between m and k" For clarity we restrict these analyses to regions of interest.
Temporal score To evaluate analogous metrics from MEG recordings, estimate temporal score: the correlation between the model layers k and Tlayer the time at which each layer of the model is maximally predictive of brain activity. To limit noisy estimate, we average on the temporal window during which R* 2 95% where R is the normalized encoding-score of the layer k.
2.3 MODELS
Architecture. DINOv3 is an open-source, state-of-the-art, self-supervised learning vision trans- former model trained on 1.7 billion natural images (Siméoni al. 2025). We train, from scratch, selection of eight variants of this DINOv3 model to ensure comprehensive evaluation ranging through architectures, training scale and data types.
First, leverage the DINOv3-7B, trained across le" checkpoints. We analyze comparatively DINO Small, Base, Large and Giant, after training for 5e training steps on 1.7B images with the same con- figuration. Additionally, we train and analyze comparatively versions of the DINO Large archi- tecture: DINO human, DINO Cellular and DINO Satellite. These models were configured similarly and trained, from scratch, over 5 steps on 10M images; they only differ in the type of images with which they were trained.
Table 1: Specifications of DINOv3 model variants. Parameters
Model DINOv3
Layers
Batch
Images
40
4096
Human centered 1.7B
DINOv3 Giant
1.1B
32
4096
Human centered 1.7B
DINOv3 Large
300M
24
4096
Human centered 1.7B
DINOv3 Base
86M
12
4096
Human centered 1.7B
DINOv3 Small
21M
12
4096
Human centered 1.7B
DINOv3 Human
300M
24
2048
Human centered 10M
DINOv3 Cellular
300M
24
2048
Cellular 10M
DINOv3 Satellite
300M
24
2048
Satellite 10M
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2.4 DATASETS.
Images. DINOv3-7B and DINO human were trained on the same human- data. This dataset was constructed from a large pool of web images, street views and ImageNet (Deng et al.) 2009). These images went through platform-level content moderation to prevent harmful contents, in order to obtain data pool of approximately 17 billion images. This data pool was curated following the procedure of (Siméoni et al. 2025) to obtain a large-scale pre-training dataset of 1.7 billion images. To compare models trained with different types of images, we re-trained three distinct large DINOv3 cellular and satellite images matched
with one of three types of natural images human- in terms of quantity (10M images each).
fluman-centrie images correspond to the dataset used for training the original DINOv3 model. For our comparative analyses on human-centric, cellular and satellite images, we randomly selected from this dataset of 1.7 billion images a subset of 10 million images.
Cellular images correspond to the ExtendedCHAMMN dataset, which consists of fluorescent micro- scopic images of cells revealing cellular structures into different channels (c.g. nucleus, mitochon- dria, microtubules, etc.) (Lorenci et al. 2025).
Satellite images correspond to random subset of the SAT-4933 dataset, which consists of approxi- mately 500 million images sampled randomly from Maxar RGB ortho-rectified imagery at 0.6 meter resolution (Siméoni et al. 2025).
Maghetoenccphaoorraphy (MEG). We use the THINGS-MEC dataset (Hebart et al. 2023a which consists of MEG recordings from four healthy participants viewing 22,500 naturalistic im- ages, representing total of 1,800 object concepts (Hebart et al. 2023b). Images were presented during 1.5s, while participants maintained fixation. To limit the impact of noise we apply a band- pass filter between 0.1 and 20 Hz, down-sample the signal at 30 Hz, tome-lock the brain responses to individual words, and epoch the corresponding neural data between -0.5 s and +3 relative to word onset using MNEEPython (Gramfort et al. 2013). Finally, we z-score MEG signals across words, for each MEG channel and each time point independently. time ROIs We study individually three 0.05s-long time ROIs across the processing time of an im- age, to study the relative impact of each layer in the encoding of the cognitive process at play during that time. These time windows span 13s, 13-.188 and .5-55s.
Tlager, To study the dynamics of each layer, we compute Trlayer, the mean of the temporal window during which Rlayer 2 95% where Rlayer is the normalized encoding-scor of each layer.
Functional Magnetic Resonance Imaging (IMRI). We leverage the Natural Scenes Dataset (Allen et al, 2022). )). a 7 tesla fMRI dataset which consists of recordings from eight subjects, each observing total of TO 000 natural scenes during 4 seconds each, while performing a continuous recognition task. We encode the BOLD signal on the fsaverage surface at 5.5 s after image onset. This timestep corresponds to the peak of decoding of the image from the BOLD signal.
Regions of interest (ROIs). For clarity, we select representative set of 15 regions of interest (ROIs) spanning the anatomy of the cortex, among the regions encoded with an averaged FDR-corrected 1-test p < 0.01, among voxels forming the ROI. These ROIs are distributed from posterior-occipital lobe to prefrontal cortex.
To investigate the cortical properties that index representational similarity, we analyze our results in light of four cortical maps, made available through Neuromaps (Markello et al., 2022):
Cortical expansion (Hill et al.) 2010) reflects the difference of cortical surface area between in- fants and adults. Myelin concentration is estimated from the Tlw/T2w ratio in the HCP $1200 derived from mapping electromagnetic networks to hemodynamic network and indexing the temporal integration window of each region (Shafiei et al. 2021). Cortical thickness is estimated by measuring the distance between "white" and "pial" " Freesurfer surfaces 2012) from structural MRI in the Human Connectome Project
dataset (Van Essen al.) 2013). Intrinsic timescales
(an Essen et al. 2013).
2.5 STATISTICS
fMR voxels. We only plot and analyze voxels thresholded with p < 0.01 after a FDR-corrected t-test. Across subjects. To evaluate statistical estimates across subjects, perform a Wilcoxon test using scipy (Virtanen et al. 2020). To correct for multiple comparison. we apply a false discovery rate correction, as implemented in MNE-Python (Gramfort al. 2013).
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Half times. To analyze the speed of convergence of DINO models during training, estimate the 'hll time": the training step at which the similarity metric reaches half of its final value.
RESULTS
3.1 DINOV3-BRAIN SIMILARITY
A
B
0.1
R
0.0
2
0.00
0.35
Time (s)
R
Figure I: Brain-DINOv3 similarity across space and time. A. Across cortical space. Similarity between DINOv3 embedding and the IMRI responses to corresponding images as estimated with Pearson Brain-Score, and fel-correceedtthessooldee sphere, top right: bottom view; bottom right: lateral view of right hemisphere). B. Across time. Similarity between DINOv3 embedding and MEG responses to the corresponding images. The er- ror bar indicates the standard error of the mean across subjects.
at < 0,01 (left: medial view of left hemi-
Encoding score. To verify that DINOv3 generates representations of natural images that are sim- ilar to those of the brain, we perform cross-validated encoding analysis by evaluating the linear mapping between the activations of DINOv3 and of the brain in response to the same images. Func- tional MRI results show that DINOv3 has representations that primarily peak in the visual pathway (R=.45 + .009 .039 SEM across subjects), mostly in the auteral-ocippiooeempraa (MT: R=.34 .026) and ventromedial visual cortex (VMV2: R=.28 + .225), Fig TA
MEG results show that this similarity rises around 70 ms after image onset (R=.09 + .017, Fig and remains significantly above chance level up to 3 seconds after image onset (p < 1e-3).
These results are consistent with past studies (Eickenberg et al. 2017 SChhimmp' et al. 2018 Tang
al. 2025) and additionally show that areas typically discarded from the visual pathways, e.g. pre- frontal regions BA 44, BA 45, IFSa and IFSp, also present activations that are linearly predictable from the Al embedding.
A
D
Argmax loyer aes
Rn
0.0
#
Best encoding
Distance from
Figure 2: The representational hierarchy of DINOv3 corresponds to the brain's. A. Voxel-wise best encoding layers of DINOv3, FDR-corrected and thresholded at p < 0.01 (left: medial view of left hemisphere, top right: bottom view; bottom right: lateral view of right hemisphere). B. Plotting the correlation between the best encoding layer for each region and the euclidean distance of this region from VI, mm. The Pearson correlation = 0.38, p < le-6. Plotted regions are encoded with FDR-comeed cor-ccorected thresholding at p < 0.01. C. Dynamic brain-score across time between each layer of DINOv3 and MEG responses to the corresponding still images. D. Plotting the correlation between the layers and their Tlayer in s. The Pearson correlation is r= 0.84, < 1e-5. Plotted regions are encoded with FDR-corrected thresholding p 0.01.
Spatial score. Does the hierarchy of representations of DINOv3 correspond to the visual hierarchy in the human brain? To address this question, estimate the "spatial score". The fMRI results
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confirm that the lowest layers of DINOv3 tend to best predict the lower-level sensory regions such as VI, whereas the highest layers tend to best predict higher-level regions of the brain, such as the prefrontal cortex (Fig 2A. B). The Pearson correlation between (i) the Euclidean distance between each brain region and V1, and (ii) the best encoding layer is highly significant, R=0.38, < le-6 (Fig
Temporal score. To complement this fMRI "spatial score", we evaluate an MEG "temporal score". We identify the layer which best predicts each time ROIs relative image onset in the MEG. The results show significant correlation between layers and their Tlayer hereafter referred as the temporal score (Fig c D). The temporal score R=0.96, p< l layers of DINOv3 consistently align with the earliest and latest MEG responses, respectively.
shows that the early and late
Generalization to multiple architectures. To test for generalization of Encoding, Spatial and Tem- poral scores we reproduce these results on a variety of seven vision models including CNNs, super- vised and self-supervised ViTs as well as vision-languan
contrastive transformers. We find similar
scores for all three metrics, across all these models (Figs. [1 S3 S4
3.2
WHAT FACTORS LEAD DINOV3 TO BECOME BRAIN-LIKE?
Impact of training. To clarify the emergence of brain-like processes in DINOv3, we evaluate the encoding score, spatial score and temporal score at each selected training step of DINOv3, and summarize their developmental speed with a "half time": i.c. the training step where half of the final score is reached. First, before training the encoding score reaches R=.03 - 2e after training ultimately converges to R= + 5e (Fig These R-scores are averaged across voxels the best voxel peaking at R=.45 + .38.. The half time of the encoding score occurs around 2% of the training, around 2e training steps (i.e. 800 million images). Second, the temporal score emerges faster than the encoding-score: with a half time around 0.7% of the training, and convergence at R=0.96 (p< le-12) Finally, the spatial score reaches its half time later, at 4% of the training, and converges to R=0.4 (p< 1e 0) We reproduce the spatial score using the value of each ROI along the gradient map from (Margulies et al. (016) instead of the euclidean distance of this ROI from V1, see Supplementary Figure S6
We obtain a similar increase across training
from -0.32 to 0.13 (1st to last checkpoint).
Generalization to multiple architectures. Across seven vision models with diverse architectures CNNs. supervised ViTs, dision-langusa contrastive transformers, etc. - trained models consistently show higher encoding, spatial, and temporal scores than untrained models. Additionally, trained models tend to show convergent encoding, spatial, and temporal scores, whereas scores from un- trained models vary more widely- likely reflecting differences in their inductive biases. These additional analyses are reported in SI S2 S3 S4 S5 comparing trained and untrained versions of all models.
Tempani score
Ineeding score
0.6
0.4
0.2
11.
Training (54)
Figure 3: Evolution of scaled temporal, encoding and spatial scores as function of DINOv3's training. The Evolution of these unscaled metries is presented in Supplementary Fig. S7
Are these developmental trajectories identical across temporal and brain regions of interest? To ad- dress this issue, evaluate the same analyses on specific regions temporal windows of interest. Functional MRI results show that low-level visual regions (c.g. V1, V2) are marked by lower half times of the last layer than high-level prefrontal cortices (e.g. IFSp, IFSa), Fig FAg,A,C.E The correla-

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tion between half time and anatomical location (coarsely defined the Euclidean distance to V1) is R=0.91, ps 1e- Similarly for MEG, earlier windows (e.g. <200 ms) are marked by lower half
C
8,
Hall Eine
11.00
Training Steps
0:59
230
60
20
358
8
R.
Hell-ime
R
2003
Training Steps
322 C31
Timets)
10c
15% 300
Window (2)
16
Figure 4: Emergence brain-like representations. A. Normalized brain encoding scores of layer I as function of training for each brain ROI. The dashed line indicates the 50% of the maximum encoding score for each region. B. Same as A for MEG time regions of interest (tROIs). C. Half time for each brain ROI. D. Half time for each time ROI. E. Correlation between half time of encoding score across training, and distance of each ROI from V1. F. Correlation between half time of encoding score across training, and time position of the encoded cognitive process (tROI).
times than late time windows (e.g. >1,00 ms), Fig 4.D.FF. The correlation between half time and temporal peak is R=0.84, p< 1e" Overall, these results show that the brain responses of the sensory and prefrontal cortices contain representations of images that are acquired early and late in the training of DINOv3, respectively.
coll
Large Dba Riv
Seek
Figure 5: Impact of model size. For inter-model comparisons, significance to p< 1e-3 are rep-
resented by asterisks *. A. Encoding (reds), spatial (purples), and temporal scores (greens) function of training and model size. Logarithmic fits of scores across training. B. Scores on the final k=4e@ training step. C. Encoding scores for VI and Brodmann area 45 the end of training.
A
ssc
250-
60
Amencc
Ttating checogicee
THESE2 CHeSSpORES
Taining Checkpsits
Figure 6: Impact of image type. For inter-model comparisons, significance to p< le are rep- resented by asterisks s. A. Encoding (reds), spatial (purples), and temporal scores (greens) as a function of training and image type. B. Scores on the final k=4e² training step. C. Encoding scores for V1 and Brodmann area 45 at the end of training.
Impact of model size. How does model size impact convergence? DINO models of larger scale appear to converge quicker and encode higher-level ROIs more accurately. Model size consistently leads bigger encoding scores at the end of training (Reiant 0.107 >
Riarge 0.105 >
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Rass 0.101 > Rsmall 0.096 with p 1) Similar, although noisier phenomena can be observed for spatial scores and temporal scores (p < 1e-a), Fig 5A. B. Does model size impact encoding scores similarly across ROIs? Applying the same analysis for each ROI separately shows that model size primarily increases encoding of higher-level cortices like BA45, IFS as compared to visual cortices like VI, V2. All models present this size-dependent increased encoding significantly in higher-ROIs only the smallest ones in VI, V2 (p < le (Fig Fig for all studied ROIs).
Impact of image type. To assess how image types influence the development of brain-like repre- sentations in model, we train, from scratch, three distinct DINO models, each using one of three natural images datasets: satellite images, cell images and classic (human-centricc) images. We fo- cus on a single DINOv3 architecture (Dino Large), with fixed training length and training data quantity (10M images) and data type as the only varying factor. Training improves encoding scores, spatial scores and temporal scores for all image types (Fig (A), suggesting that these models learn visual features that are universal across these different types of natural images. However, these brain-similarity metrics are lower for satellite and cell images than for human centric images, for encoding, spatial and temporal scores (Fig 6A, B). Interestingly, this difference is observed across all studied regions of interest: e.g. both V (p 1c-3) and BA45 (p 1) are better encoded by a model trained with human centric photos than other models (Fig (Cg see [SIT] for results on all studied ROIs. At the end of training, DINO human reaches significantly higher performance regarding encoding, temporal and spatial scores (p < 1e Fig 68. These results might unravel from the fact that human centric images reflect visual input that humans are exposed to, whereas satellite images and cell images are images that human brains have not been trained to process.
3.3
LINK TO CORTICAL PROPERTIES
Cortex expansion
Halltime (8 traiiig)))
Cortex thickness
Halt Witttmmtt 1% trarinng
A
B
2.5
2.5
2.0
1.5
1.5-
1.0
oppansion
expansion
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thickness
2.5
Cortex timescales
Hat come traning
Cortex myelin
HatrGine 1% takiing
C
D
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2.0
1.0
32.75
20
1.66
1.4
timescales
myeli
myelin
Figure 7: Relation between shared representations and cortical properties. A. Left. Cortical expansion index, estimated from the difference between adults and infants' brains, for each ROI (Hill et al. 2010). Right. Correlation between cortical expansion and half time. Each dot an ROL. B. Same A for cortical thickness, estimated from (Van Essen et al. 2013). C. Same as A for cortical time scales, as estimated from MEG source reconstruction in (Shafiei et al. 2021). D. Same
A for myelin concentration, as estimated from (Van Essen al. 2013).
Is the development of brain-like representations predicted by functional, structural and develop- mental properties of the cortex? To explore this issue, evaluate the correlation between the representational half time of encoding and four properties of the cortex.
Cortical expansion. First, we focus on the developmental expansion of cortical regions. Using an atlas comparing infant and adult cortical structures (Hill et al.) 2010), we found strong positive correlation (R=0.88, p < 3 between half time and cortical expansion (Fig A This indicates that cortical areas marked by greater developmental growth are also those whose representations emerge later the AI model.
Cortical thickness. Second, we assess the correspondence with cortical thickness, utilizing HCP S1200 estimates. Our results show significant correlation (R=0.77, p < le suggesting that cortical areas with larger cortical sheets exhibit longer half times (Big Cortical dynamics. Third, the areas with the slowest intrinsic dynamics, as estimated from a source- reconstruction of MEG activity, also those that tend to have the longest half times (R=0.71, p
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022).. This result directly echoes our MEG results (Fig 2) whereby deeper layers of DINOv3 tend to be associated with slower brain responses (Fig 7C).
Cortical myelin. Finally, this dynamic property appears linked to myelin concentration (an Essen
et al. 2013). Myelin, which facilitates faster neuronal transmission, demonstrated a strong negative correlation with half time (R=00.55, p-val =le =1e-3). This implies that higher myelin concentration is associated with shorter half times (Fig 7D).
In summary, these findings demonstrate a strong predictive relationship between the speed which brain-like representations emerge in Al models and various structural and functional characteristics of the cortex, across development and once developed.
4 DISCUSSION
Main findings. Understanding why artificial neural networks develop representations that resem- ble those in the human brain remains a fundamental challenge to neuroscience and Al (Huh et al.) 2024 Hasson et al. 2020 Shen al., 2025 Caucheteux & King 2022p. While recent studies have documented brain model similarities across a wide range of architectures and training paradigms (Wang et al., 2023 Conwell et al. 2022). the exact factors that cause this convergence and their interactions remain unclear. Here, we independently manipulate three factors model size (from DINOv3 small to giant), training length (from 0 to 1e? steps on several training sets of 10M and of 1.7B images) and image type (human-centric, satellite images and biological images) to test how each of them contributes to the emergence of brain-like representations of natural images. Our find- ings demonstrate that these three factors all independently and interactively impact the extent to which a self-supervised model converges to brain-like visual processing. Results show that size, training duration, and data type each shape the emergence of brain-like representations but also brain-like hierarchies, measured through spatial and temporal scores, Representations, temporal and spatial hierarchies all emerge though at different pace across training. When comparing untrained and trained versions of seven diversified vision models, we find that the vast majority of them consis- tently develop qualitatively similar encoding, spatial and temporal scores as DINOv3. Our analyses complement prior work examining only convergence between multiple models (Huh et al., 2024), demonstrating that this convergence also extends onto human neural representations. Finally, we find that the emergence of brain-like properties during training follows the developmental maturation of the human cortex from birth through early adulthood. Although these developmental trajectories have not been tested in other models, the convergence of encoding, spatial, and temporal scores for eight diversified vision models may suggest that these additional results regarding DINOv3 are generalizable to other models. Future research will allow to test this hypothesis.
Nativism and empiricism. In particular, the model-brain similarity increases consistently with larger DINOv3 architectures, longer training, and more ecologically valid data. These results consistent with an increasing set of studies showing linearly aligned representations of natural im-
ages Yaamins et al. 2014b) Kriegeskorte] 2015 Schrimpf et al. 2018 Tang et al.) 2025 Thobani cet al. 2025). with a hierarchy that maps the functional organization of the visual cortices (Eicken-
berg et al. 2017 La Tour al., 20022), and dynamics that reflect the ordering of the model 's layers
(Seeliger et al. 2018: Cichy et al.) 2016). In addition to its factorial disentanglement, our study provides additional contributions.
First, this model-brain alignment is not confined to the visual pathways (Eickenberg et al. 2017 Schrimpf et al.) 2018, Tang et al., 2025) but extends into high-level- multi-modal regions of the cortex, including the prefrontal cortex (although see e.g. [Solomon et al. (2024) for low-
dimensional set of image features identified in the prefrontal lobe).
Second, our independent manipulation of model size, training duration, and data type further show how these factors interact with one another: the largest architectures best align with brain activity as (1) they get trained and (2) on rcologically-rrlevan naturalistic images.
Third, even non-human-centric datasets (satellite images, biological images) support partial con- vergence in early visual areas, implying that low-level statistics shared across environments are sufficient to bootstrap early representations. Our findings indicate that models trained on human- centric images still tend to develop representations that more closely resemble those of the human visual system. However, it remains unclear whether this advantage reflects low-level image statis- ties (e.g., natural color and texture distributions) or higher-level semantic properties typical of hu-

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man experience. Additionally, it remains unclear whether this higher alignment for human-centrr dataset is driven by a distribution of images more similar to the one in the training data of DINOv3. Distinguishing between these factors will require future research, for example by evaluating brain responses from participants watching controlled non-human centric images. Overall, these results suggest that while the architecture supplies a potential, the data remain critical in making these systems learn representations that are similar to the brain. This interaction between architectures, training and data provides an empirical framework the long-standing debates in cognitive science on nativism versus empiricism - showing how 'innate' and 'experiential" interact with one another in the development of cognition.
Towards a model of the visual cortex ontogeny. This model-brain alignment follows a surpris- ingly steady developmental trajectory. Early in training. the models rapidly acquire representations that align with the fast and low-level visual responses of the sensory cortices. In contrast, the emer- gence of slow and high-level representations particularly those aligning with the prefrontal cortex aappears to require both far more training data.
This developmental trajectory echoes the biological development of the human cortex: the brain areas with which the Al models align last during their training are precisely those with the greatest cortical thickness, slower intrinsic timescales, prolonged maturation, and lower levels of myelination -i.. the areas of the associative cortices that are known slowly develop throughout the first two decades of life (Dehaene 2021). This result suggests that the sequential acquisition of representa- tions in artificial neural networks may spontaneously model some of the developmental trajectories of brain functions. In doing so, they may ultimately provide a new computational framework to understand the staged maturation of visual processing in biological systems (Vogelsang al. 2024 Zaadnoordijk et al. 2022
Long et al. 2024
Open questions. Several results were not anticipated. First, the temporal score, encoding score and spatial score do not appear to emerge simultancously - hence leading to the novel question of why these metrics follow this specific order. The factors that lead the temporal score to emerge are not entirely clear. However, the temporal score rises before the encoding score [3] suggesting that the encoding score alone does not solely explain the temporal score. Second, the spatial and temporal scores are initially negative (respectively significantly, p 0.05, and non-significantly, the beginning of model training. This means that the deepest layers of a random DINOv3 tend to best predict fast and low-level brain responses at the very early (but not late) stages of training. Finally, the half times of these three metrics are reached in between 1% and 4% - i.e. only n=1.6B images of DINOv3 training quantity. This suggests that while low-level brain-like representations are very quickly learnable, the high-level representations of the brain require a very large amount of data to be fully acquired.
0.05) at
Limitations. While this study offers a controlled analysis of brain-model convergence, several limitations warrant consideration. First, our findings are based exclusively on single family of self-supervised vision models (DINOv3), which are hierarchical by design. It thus remains an open question whether similar spatial. temporal and encoding scores would emerge with other architec- tures and training objectives (Conwell et al. [022]. Second, fMRI and MEG offer limited resolution and thus provide coarse population-level brain activity and may overlook fine- ane-ggrinedd neural mech- anisms. Third, our analyses focus solely on the adult brain, leaving open the question of how these alignments emerge across development. Understanding when these correspondences arise will re- quire data from infants, children, or longitudinal cohorts (Evanson et al. 2025). Additionally, analyze two datasets where participants watched images most often passively: future work should assess how different tasks modulate the alignment between DINOv3 and the prefrontal cortex. Fu- ture research should also extend this systematic exploration to additional factors driving brain-like convergence, as well as to the interactive effects between all of them. Finally, while we quantify the similarity between representations from models and the brain, the exact nature and semantic structure of these neuronal representations continues to be subject of intense ongoing research (Gifford et al. 2025 Graumann et al.; 2022). Closing this interpretability gap certainly remains a major challenge to both neuroscience and AI.
Conclusion. Beyond the characterization of the spontaneous convergence between AI models and brains, these findings chart a path toward using AI models as tools to investigate the organizing principles of biological vision in the human brain. By showing how machines can come to see like us, our findings provide cues as to how the human brain may come to see the world.
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A REPRODUCIBILITY STATEMENT
The paper is based on an open-source MEG dataset (Hebart et al. (223a) and an open-source fMRI dataset (Allen et al. 2022), as well several open-source vision models (versions of DINOv3 Siméoni et al. (025 ). These models and datasets are cited in the Introduction and the Methods sections. Regarding the comparative trainings, the satellite and human-centric datasets are currently proprietary, whereas the cellular dataset is collection of open-source datasets (Lorenci et al. 2025 Linear decoding code available at:
ptlps:/mmin tool / stabl blaebbllb/ee http//ggnerrrdeeeeeeee mne. decodi ng. 8 iginggsn mator.
htm

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B APPENDIX
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Figure S2: Encoding scores across cortex, reproduced for a range of seven vision models with varying architectures and training objectives including CNNs (ResNet-50, ConyNeXt-Large). self-supervised ViT with different objective than DINOv3 (ViT-MAE, masked image reconstruc- tion), a supervised VIT (VTT-L/16), and vasion-language contrastive transformers (CLIP, SigL.PP2, OWL-VVT)..
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Figure S3: Maximally encoding layers across cortex, reproduced for a range of seven vi- sion models with varying architectures and training objectives including CNNs (ResNet-50, ConvNeXt-Large), a self-supervised ViT with different objective than DINOv3 (ViT-MAE, masked image reconstruction), supervised VIT (ViT-L/16), and vision- -laguaee contrastive transformers (CLIP, SigLIP2, OWI-VVT).
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C ETHICS STATEMENT
Among the three datasets this study leverages, only the human-centric dataset is constituted from images from the web and imageNet; each of these images went through platform-level content moderation to prevent any harmful contents (Siméoni et al. 2025).
The present paper uses two datasets regarding research with human subjects, both these datasets have already been published, are open-source and cited in the paper. These open-source recordings were collected after participants' informed consent and were validated by the corresponding Institutional Review Boards.
D ACKNOWLEDGMENTS
We warmly thank the authors of (Hebart et al. 2023a) and (Allen et al. 2022) for building and open-sourcing the MEG and fMRI datasets on which we relied for this study. We thank the Meta communication and legal teams for their support throughout the submission process.
E
LLM USE
An LLM was used in preparing this manuscript for proofreading purposes.
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Reviews:
[{"review_id": "T9cpWzb0Yo", "reviewer": "Reviewer_eWiA", "summary": "The paper analyzes how variants of DINOv3, that were trained from scratch while independently varying model size, training duration, and image type, align with human brain activity. Then using  fMRI and MEG, they quantify alignment via three complementary metrics: an overall encoding score (linear predictability), a spatial score (hierarchical correspondence across cortical regions), and a temporal score (layer-to-latency correspondence). They find that all three factors matter, with the strongest brain similarity achieved by larger DINOv3 models trained longer on human-centric images, and that spatial and temporal hierarchies in the model mirror those observed in cortex.\n\nThis alignment follows a developmental trajectory during training: early layers quickly align with early visual areas and fast MEG responses, whereas later layers only align with higher-order (including prefrontal) regions after substantially more training. The speed (half-time) at which regions become linearly decodable from DINOv3 correlates with cortical properties", "questions": "Scope clarification:\nWhy was the analysis restricted to DINOv3? Do you expect the same patterns (on size, training, and data domain) to hold for other self-supervised vision transformers or CNN-based models?\n\nChoice of DINOv3:\nWhat motivated selecting DINOv3 as the reference model for brain alignment? Was it chosen because of prior evidence of brain-like representations, or mainly because of its training flexibility?\n\nBaselines:\nHave you considered including other models (e.g., ResNet, CLIP, MAE, or supervised ViTs) in the same evaluation framework to check whether the observed effects are specific to DINOv3 or more general?\n\nMetric validation:\nHow sensitive are your conclusions to the three metrics (encoding, spatial, temporal)? For instance, would a representational similarity analysis (RSA) or decoding-based metric lead to the same developmental trajectory findings?\n\nUntrained controls:\nCould you include results for untrained or partially trained DINOv3 checkpoints? This would help isolate the contribution of training versus architectural priors in explaining the fMRI/MEG alignment.\n\nSpatial hierarchy measure:\nThe spatial score is based on Euclidean distance from V1, which is a pretty coarse proxy for cortical hierarchy. Did you test other spatial gradients (e.g., the principal sensory-to-transmodal gradient or visual hierarchy maps) to verify that the effect holds?\n\nInterpretation of “human-centric data” effect:\nHow do you distinguish whether the advantage of human-centric images comes from their semantic content (ecological relevance) versus low-level statistics or augmentations used during training?", "limitations": "", "rating": 2, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "I think the paper is clear and easy to follow. It’s written in a straightforward way and focuses on a well-defined question: how certain factors within DINOv3 (model size, training time, and data domain) affect its ability to linearly predict human brain activity from fMRI and MEG. The methodology is clean, and the factorial setup makes the analysis intuitive.\n\nThis is a very exciting topic and the authors do a good job in  connecting representation learning with neuroscience. Also , they chose sensible axes for exploration. The three proposed metrics (encoding, spatial, and temporal) are complementary and help make the results interpretable. I like that they include both spatial (fMRI) and temporal (MEG) comparisons, which makes the findings richer and more biologically grounded. The “developmental trajectory” result is an interesting angle and ties well with biological plausibility.", "weaknesses": "The title should make clear that the analysis is only done on DINOv3, not on vision models in general. \n\nI think the rationale for picking DINOv3 as the model to analyze isn’t well justified. There’s no comparison to other strong baselines that are already known to align well with brain data (e.g., ResNet, CORnet-S, CLIP, SimCLR). Without that, it’s hard to know if the observed effects are special to DINOv3 or just generic to large self-supervised ViTs.\n\nAnother issue is that the paper defines its own metrics and only applies them to DINOv3,  so it risks being a bit circular. It would be stronger if the same metrics were applied across multiple models to show whether DINOv3 is actually distinctive.\n\nA control including the metrics  for untrained or random-weight models would be great. I think  including these would help confirm that the correlations aren’t just due to low-level image statistics or architectural biases (for example, ViTs already encode spatial frequency patterns similar to early visual cortex even before training).\n\nFinally, the overall impact is somewhat limited because the findings: larger models trained longer on naturalistic images better align with brain data, are consistent with earlier work (e.g., Yamins & DiCarlo 2016; Schrimpf et al. 2018; Cichy et al. 2016). The novelty mainly comes from the within-family comparison and the correlations with cortical properties, so it would help if the authors clarified this and avoided overselling the generality.", "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": "TnZ4YWTTvH", "reviewer": "Reviewer_bhwe", "summary": "The paper investigates factors contributing to neural network-brain representation alignment by training variants of the self-supervised vision transformer DINOv3 with systematically varied (1) model size, (2) training amount, and (3) image type. Model representations are compared to human brain activity measured via fMRI and MEG using three complementary metrics: encoding score (overall linear similarity), spatial score (hierarchical organization across brain regions), and temporal score. The authors find that larger models trained longer on human-centric images achieve higher brain-similarity scores across all three metrics, and that different brain regions align at different training stages i.e. early sensory regions align first, followed by higher-level prefrontal regions.", "questions": "The temporal score emerges very early in training (~0.7% half-time), but this might be trivially expected from architecture alone rather than learned through training. The DINOv3 architecture inherently processes information from early-to-deep layers, which naturally corresponds to the brain's early-to-late temporal dynamics.\nWhat is the temporal score for completely untrained (random) models? If it's already high, then the \"emergence\" during training is misleading.\n\nThe only difference training might make (given that the untrained ver seemed to give negative correlations)  is reducing the randomness in the layer-to-time mapping (i.e. randomly initialized model might just get a random assignment to each of the random-looking layers, thus resulting to low depth - time correlation score), not necessarily creating the “temporal alignment” de novo.\nThey mentioned in DI “deep layers of untrained models best predict early brain responses, which is backwards”, is this consistently observed or just suspicions (i.e. just inferred from ~0 negative correlation score)\nFigure 6C shows that non-human-centric images (satellite, cellular) produce minimal prefrontal alignment. \nDo you observe any alignment difference to prefrontal cortex (specific ROIs or relative alignment strength change) when the neural data were recorded under different behavioral tasks (e.g. those that involve judgement (recognition) (the fmri dataset) vs. passive viewing (the MEG dataset) ?\nIt would be interesting to see quantitative support for statements like \"experience plays a larger role\" in the Discussion. \nSince some findings/ disentangling attempts align with previous study (and people’s intuition) - It would be interesting to dive into how these factors actually interact with each other. e.g.  Does the training amount effect depend on the model size? Is there a minimum model complexity (albeit difficult to define) where adding more training stops helping as much? Can you compensate for a smaller model by training it much longer, and if so, what's the tradeoff? if not, what were the minimal components of models necessary for training to be contributive to brain-alignment (representation level) ?\nWill these findings always model/ training diet-specific? (will quantitative attempts always fail when we change these factors?) Can any general qualitative conclusion be drawn from this?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "- The combination of fMRI and MEG with three complementary similarity metrics (overall encoding, spatial, temporal) provides a more complete characterization than prior studies relying on single modalities or metrics, which allows evaluation of alignment across both spatial mappings (cortical hierarchy) and temporal processing sequences rather than using only pre-selected layer-region pairs or fixed time windows (unlike most existing studies).\n -The analysis extends beyond the traditional ventral visual stream to include prefrontal regions (BA44, BA45, IFSa, IFSp),  show linear predictability in higher-level associative cortices, though interpretation requires careful consideration (see weaknesses part)\n- The correlational analysis linking alignment emergence speed to cortical properties (developmental expansion, thickness, myelination, intrinsic timescales) provides biological context, though this constitutes post-hoc analysis using existing atlases rather than experimental validation", "weaknesses": "- Limited scope (question of generalizability) -\nAll experiments use DINOv3, It's unclear whether findings generalize to: 1) architecture family with different inductive biases such as CNNs (with presumably much stronger ventral-stream-hierarchical-aligning inductive biases) 2) supervised models (what’s the role of learning objective under the context of diets needed for training?) or (3) other self-supervised methods (e.g., MAE, SimCLR). one could expect conclusions (especially regarding the training dynamics/ relative convergence speed (in terms of half-time) between the three measures) changes accordingly.\n- Ambiguity in ROI-level encoding scores\nSection 2.2 states scores can be \"summarized as the average R score across brain dimensions\" but doesn't specify whether ROI-level comparisons (Figures 5C, 6C) use: A) averaging across all layer-region pairs within each ROI, or B) selecting the best-predicting layer for each voxel then averaging those scores within the ROI. If Method A is used, prefrontal ROI scores could potentially be systematically underestimated because most layers (early to intermediate layers) that perform poorly in behavioral linear readout would be anticipatedly to also poorly predict prefrontal activity (one would expect only the latest layers are relevant). \n- Novelty?\nThe finding that larger models achieve higher brain alignment (Small < Base < Large < Giant; Figure 5) substantially replicates Huh et al. (2024, \"Platonic Representation Hypothesis\"), which tested multiple model families with varying sizes and observed similar scaling trends (with few exceptions where larger ≠ better). The present study's scope is even narrower (single architecture family DINOv3, only 4-5 size variants) to claim general principles about model size effects beyond DINOv3-specific properties. Though the region-specific analysis (Figure 5C showing disproportionate benefits for BA45 vs. V1) adds nuance\n\nMinor prose issues: \nFigure 3 axis- showing the relative score compared to the optimal score rather than raw score? consider clarifying in caption or axis label.\nLine 212 missing line break (fMRI description runs into ROIs description)\nLine 239 missing space after period\nLine 301 extra space before “?”", "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": "VLtwy2lsmr", "reviewer": "Reviewer_9Mpq", "summary": "The authors train multiple different DINOv3 models with varying model sizes (s, B, L, g, 7B) and different pretraining datasets (Human centered, Satellite, Cellular). Given checkpoints of these models at different training iterations, they compare the representations with the representations of the human brain recorded with fMRI and MEG on the THINGs and natural scenes datasets. The study finds that (1) larger models converge quicker and encode higher-level ROIs more accurately, (2) longer training and (3) more human-like data (in contrast to medical/satellite data) leads to a higher degree of model–brain similarity.", "questions": "* The brain “alignment” metrics are shown over the training time, but the DINOv3 framework has several mid-training/posttraining phases (gram anchoring, high resolution training). Did you investigate the effects of this training phases wrt. to the brain alignment scores?\n\n* It was observed that larger models have generally larger correlations to the human brain representations than smaller models. Did you also evaluate the distilled models from the DINOV3 released models? I would be curious whether these would also be able to match the higher prediction scores when having a larger teacher.\n\n* How did you exactly extract the representations at different layers of the model? Were the CLS token, patch tokens, register tokens used and how were they potentially aggregated?\n\n* Did you investigate any more sophisticated probes than linear probes (e.g. attention probes)?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "* The paper studies an interesting question and the methodological comparisons between the DINOv3 models and brain measurements is sound (2 different measurements, independent datasets, 3 different metrics).\n\n* The paper is well written and explains the methods and results in an understandable way.\n\n* The alignment is studied over many different training iterations, allowing insights into how it progresses over the training time which is particularly interesting and often missing in other similar studies.\n\n* The authors not only evaluated the representational alignment but also the spatial correlation between the location in the brain and the different layers in the ViT giving insights whether the representations are constructed/progress in a similar way.", "weaknesses": "* The study only investigates DINOv3 models and does not investigate any other SSL objective or image/text, supervised objectives. The paper is called: “Disentangling the Factors of Convergence between Brains and Computer Vision Models” but only looks at different training iterations (#samples seen), model size and dataset type (human centered, satellite, cells). Model architecture and training objectives are not studied. In previous studies [1], the objective was a key driver of alignment to human similarity judgements. Therefore, it would be pretty interesting to study how the score differs in relation to publicly available image/text models, supervised models, SSL models that only rely on masking (e.g. MAE [2], CAPI [3], AIMM [4]) and the ones relying on contrastive/multi-view objectives (SimCLR [5], MoCo [6], BYOL [7]). This is partly acknowledged in the limitations section but the title and some of the claims are too general (speaking of Computer Vision Models) given that these factors are not investigated.\n\n* Isn’t the “Impact of image type” analysis confounded by the fact that the datasets that were used to gather the fMRI, MEG data are also more human-like (THINGs and natural scenes)? I would be curious to see how that changes when the alignment is evaluated on medical/cell images and satellite images. This should be discussed in this section.\n\n* Minor typos: In line 239 a space is missing. In line 301, there is one space too much before “?”.\n\n\n[1] Muttenthaler, Lukas, et al. \"Human alignment of neural network representations.\" ICLR 2023.\n\n[2] He, Kaiming, et al. \"Masked autoencoders are scalable vision learners.\" Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2022.\n\n[3] Darcet, Timothée, et al. \"Cluster and predict latent patches for improved masked image modeling.\" TMLR 2025.\n\n[4] El-Nouby, Alaaeldin, et al. \"Scalable pre-training of large autoregressive image models.\" ICML 2024.\n\n[5] Chen, Ting, et al. \"A simple framework for contrastive learning of visual representations.\" International conference on machine learning. ICML 2020.\n\n[6] He, Kaiming, et al. \"Momentum contrast for unsupervised visual representation learning.\" Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2020.\n\n[7] Grill, Jean-Bastien, et al. \"Bootstrap your own latent-a new approach to self-supervised learning.\" Advances in neural information processing systems 33 (2020): 21271-21284.", "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": "cHpRminQhK", "reviewer": "Reviewer_omL2", "summary": "The present paper studies the factors that drive brain-model alignment. In particular, it investigates three dimensions: model size, training time, and training data. The authors train DINOv3 from scratch and compute encoding, spatial, and temporal scores. The three varied dimensions interactively impact each of these alignment metrics: (1) the largest models reaches the highest alignment, (2) the emergence of brain-like representations follows a specific chronology during training, and (3) the developmental trajectory is indexed by both structural and functional properties of the human cortex.", "questions": "Personally, I found the analyses regarding the training trajectories of the alignment metrics to be the most interesting part. Has there any work been done on this before already? That would help to judge the novelty of the present approach.\n\nAre images from THINGS and the Natural Scenes Dataset in the Human centered 1.7B data? If so, does this matter for the presented results?\n\nFor Figure 3: the temporal score is only based on the MEG data, the spatial score soley on fMRI data, and the encoding score on both, right? Based on this, are these three metrics directly comparable to each other? The fact that that they are in the same figure and the wording in the text seem to suggest so, but I am a bit unsure.", "limitations": "", "rating": 8, "confidence": 4, "soundness": 4, "presentation": 3, "contribution": 4, "strengths": "I found this paper to be very insightful and interesting to read. The systematic analysis of differnet model properties on brain alignment is one of the most important analyses to be done in this field. \n\nThe methods and data are very sound (although I cannot speak a some of the very specific MEG/fMRI details). The paper builds on a wealth of prior work, but complements it nicely by taking an important next step. It was easy to read and follow, and is a great thematic fit for ICLR.\n\nIn particular, I found the analyses regarding the temporal and spatial score, as well as the alignment trajectories during training, to be very interesting -- I haven't seen something like this before.", "weaknesses": "While the authors vary three different dimensions, they don't explore the whole space (e.g. they did not look at varying the data in all model sizes). This risks of missing potential interaction effects.\n\nThe present study considers only a single model class. This is not a major problem from my side, and it is already acknowledged and discussed by the authors.\n\nMinor: one could use consistent typesetting for R (italic or not).", "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)