Paper Citation Record · LEDGER

Disentangling the Factors of Convergence between Brains and Computer Vision Models

As of 21 August 2026, this Paper Citation Record lists 62 of 62 outbound references and 9 inbound Pith citation observations for arXiv:2508.18226.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2508.18226 v1

Coverage vector

measured 62 of 62 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T16:32:28.456536Z

measured 71 of 71 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 9 of 9 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T15:46:43.243166Z

measured 1 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

62 of 62 outbound references displayed

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External citation measurements

0
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

Observation 73fbd1b2-4dab-4d95-851c-88888253d0b3 · outbound

This paper cites write newline.

Disentangling the Factors of Convergence between Brains and Computer Vision Models write newline

Reference 1

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Observation 88960a67-ad2b-456b-8323-95a751ed06e7 · outbound

This paper cites Allen, Ghislain St-Yves, Yihan Wu, Jesse L.

Disentangling the Factors of Convergence between Brains and Computer Vision Models Allen, Ghislain St-Yves, Yihan Wu, Jesse L

Reference 2

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Observation 938dbe43-fc70-47d2-a54c-e3724789e90e · outbound

This paper cites Scaling laws for decoding images from brain activity.

Disentangling the Factors of Convergence between Brains and Computer Vision Models Scaling laws for decoding images from brain activity

Reference 3

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This paper cites Tomasini, Alessandro Favero, and Matthieu Wyart.

Disentangling the Factors of Convergence between Brains and Computer Vision Models Tomasini, Alessandro Favero, and Matthieu Wyart

Reference 4

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Observation 25835d4f-8b08-4aca-85e7-befc0abee783 · outbound

This paper cites Brains and algorithms partially converge in natural language processing.

Disentangling the Factors of Convergence between Brains and Computer Vision Models Brains and algorithms partially converge in natural language processing

Reference 5

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This paper cites Schwing, Alexander Kirillov, and Rohit Girdhar.

Disentangling the Factors of Convergence between Brains and Computer Vision Models Schwing, Alexander Kirillov, and Rohit Girdhar

Reference 6

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Observation 488837d9-be0d-45d7-abca-5568cc61b5b8 · outbound

This paper cites Comparison of deep neural networks to spatio-temporal cortical dynamics of human visual object recognition reveals hierarchical correspondence.

Disentangling the Factors of Convergence between Brains and Computer Vision Models Comparison of deep neural networks to spatio-temporal cortical dynamics of human visual object recognition reveals hierarchical correspondence

Reference 7

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This paper cites Prince, George A.

Disentangling the Factors of Convergence between Brains and Computer Vision Models Prince, George A

Reference 8

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This paper cites What can 1.8 billion regressions tell us about the pressures shaping high-level visual representation in brains and machines? BioRxiv, pages 2022--03, 2022.

Disentangling the Factors of Convergence between Brains and Computer Vision Models What can 1.8 billion regressions tell us about the pressures shaping high-level visual representation in brains and machines? BioRxiv, pages 2022--03, 2022

Reference 9

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This paper cites How we learn: Why brains learn better than any machine.

Disentangling the Factors of Convergence between Brains and Computer Vision Models How we learn: Why brains learn better than any machine

Reference 10

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Observation edf4d1b8-df29-4a60-a28b-1e458f6e40a6 · outbound

This paper cites Imagenet: A large-scale hierarchical image database.

Disentangling the Factors of Convergence between Brains and Computer Vision Models Imagenet: A large-scale hierarchical image database

Reference 11

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This paper cites How does the brain solve visual object recognition? Neuron, 73 0 (3): 0 415--434, 2012.

Disentangling the Factors of Convergence between Brains and Computer Vision Models How does the brain solve visual object recognition? Neuron, 73 0 (3): 0 415--434, 2012

Reference 12

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This paper cites Kietzmann, Emily Allen, Yihan Wu, Thomas Naselaris, Kendrick Kay, and Ian Charest.

Disentangling the Factors of Convergence between Brains and Computer Vision Models Kietzmann, Emily Allen, Yihan Wu, Thomas Naselaris, Kendrick Kay, and Ian Charest

Reference 13

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This paper cites Seeing it all: Convolutional network layers map the function of the human visual system.

Disentangling the Factors of Convergence between Brains and Computer Vision Models Seeing it all: Convolutional network layers map the function of the human visual system

Reference 14

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This paper cites Dermatologist-level classification of skin cancer with deep neural networks.

Disentangling the Factors of Convergence between Brains and Computer Vision Models Dermatologist-level classification of skin cancer with deep neural networks

Reference 15

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This paper cites Emergence of language in the developing brain.

Disentangling the Factors of Convergence between Brains and Computer Vision Models Emergence of language in the developing brain

Reference 16

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This paper cites Distributed hierarchical processing in the primate cerebral cortex.

Disentangling the Factors of Convergence between Brains and Computer Vision Models Distributed hierarchical processing in the primate cerebral cortex

Reference 17

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Disentangling the Factors of Convergence between Brains and Computer Vision Models Freesurfer

Reference 18

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Disentangling the Factors of Convergence between Brains and Computer Vision Models Gifford, Maya A

Reference 19

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correction dated 2025-11-18. Source: crossref record 10.1038/s41562-025-02370-8->10.1038/s41562-025-02252-z:correction, observed 2026-07-11T02:58:23.028534+00:00. This notice travels one citation hop only.

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Disentangling the Factors of Convergence between Brains and Computer Vision Models Meg and eeg data analysis with mne-python

Reference 20

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Disentangling the Factors of Convergence between Brains and Computer Vision Models Unresolved cited work

Reference 21

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Disentangling the Factors of Convergence between Brains and Computer Vision Models Nastase, and Ariel Goldstein

Reference 22

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Disentangling the Factors of Convergence between Brains and Computer Vision Models things-meg

Reference 23

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This paper cites THINGS -data, a multimodal collection of large-scale datasets for investigating object representations in human brain and behavior.

Disentangling the Factors of Convergence between Brains and Computer Vision Models THINGS -data, a multimodal collection of large-scale datasets for investigating object representations in human brain and behavior

Reference 24

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Disentangling the Factors of Convergence between Brains and Computer Vision Models Similar patterns of cortical expansion during human development and evolution

Reference 25

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Disentangling the Factors of Convergence between Brains and Computer Vision Models The Platonic Representation Hypothesis

Reference 26

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Disentangling the Factors of Convergence between Brains and Computer Vision Models Characterizing the dynamics of mental representations: the temporal generalization method

Reference 27

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Disentangling the Factors of Convergence between Brains and Computer Vision Models Deep neural networks: A new framework for modeling biological vision and brain information processing

Reference 28

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Disentangling the Factors of Convergence between Brains and Computer Vision Models Representational similarity analysis-connecting the branches of systems neuroscience

Reference 29

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Disentangling the Factors of Convergence between Brains and Computer Vision Models Feature-space selection with banded ridge regression

Reference 30

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Disentangling the Factors of Convergence between Brains and Computer Vision Models De Lorenci, Seung Eun Yi, Th \'e o Moutakanni, Piotr Bojanowski, Camille Couprie, Juan C

Reference 31

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Disentangling the Factors of Convergence between Brains and Computer Vision Models Mahner, Lukas Muttenthaler, Umut G\" u c l\" u , and Martin N

Reference 32

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Disentangling the Factors of Convergence between Brains and Computer Vision Models Neuromaps: structural and functional interpretation of brain maps

Reference 33

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Disentangling the Factors of Convergence between Brains and Computer Vision Models Spoerer, Nikolaus Kriegeskorte, and Tim C

Reference 34

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Disentangling the Factors of Convergence between Brains and Computer Vision Models Toward a realistic model of speech processing in the brain with self-supervised learning

Reference 35

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Disentangling the Factors of Convergence between Brains and Computer Vision Models Encoding and decoding in fMRI

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Disentangling the Factors of Convergence between Brains and Computer Vision Models Modality-Agnostic fMRI Decoding of Vision and Language

Reference 37

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Disentangling the Factors of Convergence between Brains and Computer Vision Models Pedregosa, G

Reference 38

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Disentangling the Factors of Convergence between Brains and Computer Vision Models Brain-like emergent properties in deep networks: impact of network architecture, datasets and training, 2024

Reference 39

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Disentangling the Factors of Convergence between Brains and Computer Vision Models You Only Look Once: Unified, Real-Time Object Detection

Reference 40

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Disentangling the Factors of Convergence between Brains and Computer Vision Models Majaj, Rishi Rajalingham, Elias B

Reference 41

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Disentangling the Factors of Convergence between Brains and Computer Vision Models Seeliger, M

Reference 42

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Disentangling the Factors of Convergence between Brains and Computer Vision Models Human electromagnetic and haemodynamic networks systematically converge in unimodal cortex and diverge in transmodal cortex

Reference 43

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Disentangling the Factors of Convergence between Brains and Computer Vision Models Alignment between Brains and AI: Evidence for Convergent Evolution across Modalities, Scales and Training Trajectories

Reference 44

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Disentangling the Factors of Convergence between Brains and Computer Vision Models Unresolved cited work

Reference 45

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Disentangling the Factors of Convergence between Brains and Computer Vision Models Representations in vision and language converge in a shared, multidimensional space of perceived similarities

Reference 46

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Disentangling the Factors of Convergence between Brains and Computer Vision Models Solomon, K

Reference 47

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Disentangling the Factors of Convergence between Brains and Computer Vision Models Brain encoding models based on multimodal transformers can transfer across language and vision

Reference 48

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Disentangling the Factors of Convergence between Brains and Computer Vision Models Many-two-one: Diverse representations across visual pathways emerge from a single objective

Reference 49

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Disentangling the Factors of Convergence between Brains and Computer Vision Models Prince, Rosa Cao, and Daniel LK Yamins

Reference 50

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Disentangling the Factors of Convergence between Brains and Computer Vision Models SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features

Reference 51

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Disentangling the Factors of Convergence between Brains and Computer Vision Models The wu-minn human connectome project: an overview

Reference 52

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Disentangling the Factors of Convergence between Brains and Computer Vision Models When Representations Align: Universality in Representation Learning Dynamics

Reference 53

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Disentangling the Factors of Convergence between Brains and Computer Vision Models Scipy 1.0: fundamental algorithms for scientific computing in python

Reference 54

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Disentangling the Factors of Convergence between Brains and Computer Vision Models Butterfly effects in perceptual development: A review of the ‘adaptive initial degradation’ hypothesis

Reference 55

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Disentangling the Factors of Convergence between Brains and Computer Vision Models Using goal-driven deep learning models to understand sensory cortex

Reference 56

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Disentangling the Factors of Convergence between Brains and Computer Vision Models Performance-optimized hierarchical models predict neural responses in higher visual cortex

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Disentangling the Factors of Convergence between Brains and Computer Vision Models Frank, James J

Reference 59

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Disentangling the Factors of Convergence between Brains and Computer Vision Models @esa (Ref

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Pith citing papers

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A Unified Geometric Space for Topological Alignment Between Transformer-Based Models and Human Brain Networks cites this paper.

A Unified Geometric Space for Topological Alignment Between Transformer-Based Models and Human Brain Networks Disentangling the Factors of Convergence between Brains and Computer Vision Models

Reference 19

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Revisiting the Platonic Representation Hypothesis: An Aristotelian View cites this paper.

Revisiting the Platonic Representation Hypothesis: An Aristotelian View Disentangling the Factors of Convergence between Brains and Computer Vision Models

Reference 2017

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Self-Distillation of Hidden Layers for Self-Supervised Representation Learning Disentangling the Factors of Convergence between Brains and Computer Vision Models

Reference 54

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CanViT: Toward Active-Vision Foundation Models cites this paper.

CanViT: Toward Active-Vision Foundation Models Disentangling the Factors of Convergence between Brains and Computer Vision Models

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Toward Aristotelian Medical Representations: Backpropagation-Free Layer-wise Analysis for Interpretable Generalized Metric Learning on MedMNIST cites this paper.

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Misalignment Between Backpropagation and the Hierarchy of Brain Responses to Images cites this paper.

Misalignment Between Backpropagation and the Hierarchy of Brain Responses to Images Disentangling the Factors of Convergence between Brains and Computer Vision Models

Reference 25

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What Makes Linguistic Representations Good Models of High-Level Visual Perception in the Human Brain? Disentangling the Factors of Convergence between Brains and Computer Vision Models

Reference 21

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IRIS: A Visual Cortex-Inspired Framework for Analyzing Orientation Selectivity in Vision Transformers cites this paper.

IRIS: A Visual Cortex-Inspired Framework for Analyzing Orientation Selectivity in Vision Transformers Disentangling the Factors of Convergence between Brains and Computer Vision Models

Reference 2025

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IRIS: A Visual Cortex-Inspired Framework for Analyzing Orientation Selectivity in Vision Transformers cites this paper.

IRIS: A Visual Cortex-Inspired Framework for Analyzing Orientation Selectivity in Vision Transformers Disentangling the Factors of Convergence between Brains and Computer Vision Models

Reference 2025

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