Steven Scholte
@neurosteven.bsky.social
Happy dad | Fascinated by perception | Anti-realist | Chair CCN2025 | #UvA #MidLevelVision #CognitiveAI
An idea to post an expanded version on biorxiv.org?
bioRxiv
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biorxiv.org
September 10, 2025 at 12:52 PM
An idea to post an expanded version on biorxiv.org?
That is deal with the statistical structure of the world on the basis of learning experience (sampling + evolution). To jump to human behaviour would be, I think, mixing up 2, or potentially 3 levels of observation.
September 10, 2025 at 12:51 PM
That is deal with the statistical structure of the world on the basis of learning experience (sampling + evolution). To jump to human behaviour would be, I think, mixing up 2, or potentially 3 levels of observation.
DCNNs here confirm our ideas of vision at a neuroscience level and potentially expand these with a broader view of what these filters are and how they can emerge. Beyond that, it suggests a theory of what vision does at this stage.
September 10, 2025 at 12:51 PM
DCNNs here confirm our ideas of vision at a neuroscience level and potentially expand these with a broader view of what these filters are and how they can emerge. Beyond that, it suggests a theory of what vision does at this stage.
I guess the theory that you would abstract from results such as these is that feedforward vision is dominated by features that naturally emerge through hierarchical processing and show these features can emerge in a convolutional tree.
September 10, 2025 at 12:51 PM
I guess the theory that you would abstract from results such as these is that feedforward vision is dominated by features that naturally emerge through hierarchical processing and show these features can emerge in a convolutional tree.
A substantial amount of the neural activity that can be explained relates to these processes, and this part (my 5 cents, this data) is (the bulk) of what is explained by DCNNs. But a necessary part of processing for vision outside the lab.
September 9, 2025 at 8:02 AM
A substantial amount of the neural activity that can be explained relates to these processes, and this part (my 5 cents, this data) is (the bulk) of what is explained by DCNNs. But a necessary part of processing for vision outside the lab.
For the model to classify objects it does a lot of 'stuff'. For instance, without a background a shallow network suffices (Seijdel et al., 2020, scholar.google.com/citations?vi...). Natural images force these networks to do a lot more than what we would label object recognition.
scholar.google.com
September 9, 2025 at 8:02 AM
For the model to classify objects it does a lot of 'stuff'. For instance, without a background a shallow network suffices (Seijdel et al., 2020, scholar.google.com/citations?vi...). Natural images force these networks to do a lot more than what we would label object recognition.
It has been surprising for me the last 7 years how easy it is to find signature of texture processing (Loke et al., 2024) and scene segmentation (Seijdel et al., 2020, 2021) in DCNNs and how little of it seems to relate to subsequent steps. We have really been trying.
September 8, 2025 at 8:13 PM
It has been surprising for me the last 7 years how easy it is to find signature of texture processing (Loke et al., 2024) and scene segmentation (Seijdel et al., 2020, 2021) in DCNNs and how little of it seems to relate to subsequent steps. We have really been trying.
DNNs still capture an impressive amount of variance. The most parsimonious account I think is that DNNs model the initial encoding well but miss, or perform differently, subsequent steps of object recognition.
September 8, 2025 at 8:13 PM
DNNs still capture an impressive amount of variance. The most parsimonious account I think is that DNNs model the initial encoding well but miss, or perform differently, subsequent steps of object recognition.
Great work from my PhD Jessica Loke, together with @lynnkasorensen.bsky.social , @irisgroen.bsky.social and Nathalie Cappaert.
September 8, 2025 at 6:32 PM
Great work from my PhD Jessica Loke, together with @lynnkasorensen.bsky.social , @irisgroen.bsky.social and Nathalie Cappaert.
Take-home: High DNN-brain correlations may reflect shared texture processing rather than object understanding. This challenges how we interpret brain-AI alignment studies and suggests new directions for computational models. www.biorxiv.org/content/10.1...
Shared texture-like representations, not global form, underlie deep neural network alignment with human visual processing
Deep neural networks (DNNs) are a leading computational framework for understanding neural visual processing. A standard approach for evaluating their similarity to brain function uses DNN activations...
www.biorxiv.org
September 8, 2025 at 6:32 PM
Take-home: High DNN-brain correlations may reflect shared texture processing rather than object understanding. This challenges how we interpret brain-AI alignment studies and suggests new directions for computational models. www.biorxiv.org/content/10.1...
Trajectories make it visual:
🔵 Texture path → high alignment, low object info (upper-left quadrant)
🔴/🟢 Natural & object-only paths → more object info but no extra alignment.
This explains why better object recognition ≠ better brain prediction.
🔵 Texture path → high alignment, low object info (upper-left quadrant)
🔴/🟢 Natural & object-only paths → more object info but no extra alignment.
This explains why better object recognition ≠ better brain prediction.
September 8, 2025 at 6:32 PM
Trajectories make it visual:
🔵 Texture path → high alignment, low object info (upper-left quadrant)
🔴/🟢 Natural & object-only paths → more object info but no extra alignment.
This explains why better object recognition ≠ better brain prediction.
🔵 Texture path → high alignment, low object info (upper-left quadrant)
🔴/🟢 Natural & object-only paths → more object info but no extra alignment.
This explains why better object recognition ≠ better brain prediction.
Cross-prediction: texture features predict brain responses to natural scenes almost as well as features from the originals themselves.
Local image statistics = the common representational currency between artificial and biological vision.
Local image statistics = the common representational currency between artificial and biological vision.
September 8, 2025 at 6:32 PM
Cross-prediction: texture features predict brain responses to natural scenes almost as well as features from the originals themselves.
Local image statistics = the common representational currency between artificial and biological vision.
Local image statistics = the common representational currency between artificial and biological vision.
The key dissociation:
• EEG encodes object category across all conditions.
• But object info does not drive DNN–brain alignment.
• Peak alignment occurs when object info is minimal (texture condition).
• EEG encodes object category across all conditions.
• But object info does not drive DNN–brain alignment.
• Peak alignment occurs when object info is minimal (texture condition).
September 8, 2025 at 6:32 PM
The key dissociation:
• EEG encodes object category across all conditions.
• But object info does not drive DNN–brain alignment.
• Peak alignment occurs when object info is minimal (texture condition).
• EEG encodes object category across all conditions.
• But object info does not drive DNN–brain alignment.
• Peak alignment occurs when object info is minimal (texture condition).
Three versions of each image:
🔴Natural scenes
🔵Texture-synthesized (global summaries of local stats only; no recognizable objects)
🟢Object-only (objects without backgrounds). Counterintuitive result: strongest DNN–brain alignment for texture-only images!
🔴Natural scenes
🔵Texture-synthesized (global summaries of local stats only; no recognizable objects)
🟢Object-only (objects without backgrounds). Counterintuitive result: strongest DNN–brain alignment for texture-only images!
September 8, 2025 at 6:32 PM
Three versions of each image:
🔴Natural scenes
🔵Texture-synthesized (global summaries of local stats only; no recognizable objects)
🟢Object-only (objects without backgrounds). Counterintuitive result: strongest DNN–brain alignment for texture-only images!
🔴Natural scenes
🔵Texture-synthesized (global summaries of local stats only; no recognizable objects)
🟢Object-only (objects without backgrounds). Counterintuitive result: strongest DNN–brain alignment for texture-only images!
Our response is due in 3 weeks. Pondering.
September 7, 2025 at 5:18 PM
Our response is due in 3 weeks. Pondering.
Lynn Flannery, Kerry Miller, Jeff Wilson, Kevin Koenrades, Brenda Klappe and our Volunteers: Nina Fitzmaurice, Ole Jürgensen, Denise Kittelmann, Elif Ayten
Maithe van Noort, Mohanna Hoveyda, Caroline Harbison, Yamil Vidal, Sotirios Panagiotou,Sofie Wahlberg, Danting Meng, Mobina Tousian.
Maithe van Noort, Mohanna Hoveyda, Caroline Harbison, Yamil Vidal, Sotirios Panagiotou,Sofie Wahlberg, Danting Meng, Mobina Tousian.
August 17, 2025 at 3:06 PM
Lynn Flannery, Kerry Miller, Jeff Wilson, Kevin Koenrades, Brenda Klappe and our Volunteers: Nina Fitzmaurice, Ole Jürgensen, Denise Kittelmann, Elif Ayten
Maithe van Noort, Mohanna Hoveyda, Caroline Harbison, Yamil Vidal, Sotirios Panagiotou,Sofie Wahlberg, Danting Meng, Mobina Tousian.
Maithe van Noort, Mohanna Hoveyda, Caroline Harbison, Yamil Vidal, Sotirios Panagiotou,Sofie Wahlberg, Danting Meng, Mobina Tousian.
@claires012345.bsky.social @mheilbron.bsky.social Angela Radulescu @neuroprinciplist.bsky.social @dotadotadota.bsky.social Tyler bonnen Sneha Aenugu @hannesmehrer.bsky.social @debyee.bsky.social Julian Kosciessa @anne-urai.bsky.social @mdhk.net @tdado.bsky.social Shauney Wilson, Shawna Lampkin,
mehrer.bsky.social
mehrer.bsky.social
August 17, 2025 at 3:06 PM
@claires012345.bsky.social @mheilbron.bsky.social Angela Radulescu @neuroprinciplist.bsky.social @dotadotadota.bsky.social Tyler bonnen Sneha Aenugu @hannesmehrer.bsky.social @debyee.bsky.social Julian Kosciessa @anne-urai.bsky.social @mdhk.net @tdado.bsky.social Shauney Wilson, Shawna Lampkin,
But could not have run without @lauragwilliams.bsky.social @jaspervdb.bsky.social @achterbrain.bsky.social @niklasmuller.bsky.social @eringrant.me @pebenjamters.bsky.social @shahabbakht.bsky.social @judithfan.bsky.social @jfeather.bsky.social Jiahui Guo @tknapen.bsky.social @lampinen.bsky.social
August 17, 2025 at 3:06 PM
a reception in Hotel Arena, an epic-party in Ijver (with about 40% of attendees on the dance floor), and above all 929 community members who brought a lot of energy and hopefully had, on multiple dimensions a fantastic conference.
So proud to be, together with @irisgroen.bsky.social chair.
So proud to be, together with @irisgroen.bsky.social chair.
LinkedIn
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lnkd.in
August 17, 2025 at 3:06 PM
a reception in Hotel Arena, an epic-party in Ijver (with about 40% of attendees on the dance floor), and above all 929 community members who brought a lot of energy and hopefully had, on multiple dimensions a fantastic conference.
So proud to be, together with @irisgroen.bsky.social chair.
So proud to be, together with @irisgroen.bsky.social chair.