Adrien Doerig
@adriendoerig.bsky.social
Cognitive computational neuroscience, machine learning, psychophysics & consciousness.
Currently Professor at Freie Universität Berlin, also affiliated with the Bernstein Center for Computational Neuroscience.
Currently Professor at Freie Universität Berlin, also affiliated with the Bernstein Center for Computational Neuroscience.
Pinned
High-level visual representations in the human brain are aligned with large language models - Nature Machine Intelligence
Doerig, Kietzmann and colleagues show that the brain’s response to visual scenes can be modelled using language-based AI representations. By linking brain activity to caption-based embeddings from lar...
www.nature.com
🚨 Finally out in Nature Machine Intelligence!!
"Visual representations in the human brain are aligned with large language models"
🔗 www.nature.com/articles/s42...
"Visual representations in the human brain are aligned with large language models"
🔗 www.nature.com/articles/s42...
Reposted by Adrien Doerig
Is the “standard workflow” holding back fMRI analysis?
Mass-univariate analysis is still the bread-and-butter: intuitive, fast… and chronically overfitted. Add harsh multiple-comparison penalties, and we patch the workflow with statistical band-aids. No wonder the stringency debates never die.
Mass-univariate analysis is still the bread-and-butter: intuitive, fast… and chronically overfitted. Add harsh multiple-comparison penalties, and we patch the workflow with statistical band-aids. No wonder the stringency debates never die.
November 18, 2025 at 10:13 PM
Is the “standard workflow” holding back fMRI analysis?
Mass-univariate analysis is still the bread-and-butter: intuitive, fast… and chronically overfitted. Add harsh multiple-comparison penalties, and we patch the workflow with statistical band-aids. No wonder the stringency debates never die.
Mass-univariate analysis is still the bread-and-butter: intuitive, fast… and chronically overfitted. Add harsh multiple-comparison penalties, and we patch the workflow with statistical band-aids. No wonder the stringency debates never die.
Reposted by Adrien Doerig
Excited to share my first paper: Model–Behavior Alignment under Flexible Evaluation: When the Best-Fitting Model Isn’t the Right One (NeurIPS 2025). link below.
November 20, 2025 at 2:05 PM
Excited to share my first paper: Model–Behavior Alignment under Flexible Evaluation: When the Best-Fitting Model Isn’t the Right One (NeurIPS 2025). link below.
Very cool work, go check it out!
Rich, brain-like scene representations built from acrive vision
Rich, brain-like scene representations built from acrive vision
🚨New Preprint!
How can we model natural scene representations in visual cortex? A solution is in active vision: predict the features of the next glimpse! arxiv.org/abs/2511.12715
+ @adriendoerig.bsky.social , @alexanderkroner.bsky.social , @carmenamme.bsky.social , @timkietzmann.bsky.social
🧵 1/14
How can we model natural scene representations in visual cortex? A solution is in active vision: predict the features of the next glimpse! arxiv.org/abs/2511.12715
+ @adriendoerig.bsky.social , @alexanderkroner.bsky.social , @carmenamme.bsky.social , @timkietzmann.bsky.social
🧵 1/14
Predicting upcoming visual features during eye movements yields scene representations aligned with human visual cortex
Scenes are complex, yet structured collections of parts, including objects and surfaces, that exhibit spatial and semantic relations to one another. An effective visual system therefore needs unified ...
arxiv.org
November 18, 2025 at 2:53 PM
Very cool work, go check it out!
Rich, brain-like scene representations built from acrive vision
Rich, brain-like scene representations built from acrive vision
This seems very cool, @singerjohannes.bsky.social decidedly did loads of cool stuff before I got lucky and he joined our group
New preprint led by @pablooyarzo.bsky.social together with @kohitij.bsky.social, Diego Vidaurre & Radek Cichy.
Using EEG + fMRI, we show that when humans recognize images that feedforward CNNs fail on, the brain recruits cortex-wide recurrent resources.
www.biorxiv.org/content/10.1... (1/n)
Using EEG + fMRI, we show that when humans recognize images that feedforward CNNs fail on, the brain recruits cortex-wide recurrent resources.
www.biorxiv.org/content/10.1... (1/n)
www.biorxiv.org
November 7, 2025 at 12:56 PM
This seems very cool, @singerjohannes.bsky.social decidedly did loads of cool stuff before I got lucky and he joined our group
Ask a brain anything!
This is incredible work led by Victoria Bosch @initself.bsky.social at the Kietzmann Lab @timkietzmann.bsky.social, developing interactive and flexible brain decoding.
I'm excited to see where this goes next.
This is incredible work led by Victoria Bosch @initself.bsky.social at the Kietzmann Lab @timkietzmann.bsky.social, developing interactive and flexible brain decoding.
I'm excited to see where this goes next.
Introducing CorText: a framework that fuses brain data directly into a large language model, allowing for interactive neural readout using natural language.
tl;dr: you can now chat with a brain scan 🧠💬
1/n
tl;dr: you can now chat with a brain scan 🧠💬
1/n
November 3, 2025 at 3:48 PM
Ask a brain anything!
This is incredible work led by Victoria Bosch @initself.bsky.social at the Kietzmann Lab @timkietzmann.bsky.social, developing interactive and flexible brain decoding.
I'm excited to see where this goes next.
This is incredible work led by Victoria Bosch @initself.bsky.social at the Kietzmann Lab @timkietzmann.bsky.social, developing interactive and flexible brain decoding.
I'm excited to see where this goes next.
Reposted by Adrien Doerig
New preprint!
"Non-commitment in mental imagery is distinct from perceptual inattention, and supports hierarchical scene construction"
(by Li, Hammond, & me)
link: doi.org/10.31234/osf...
-- the title's a bit of a mouthful, but the nice thing is that it's a pretty decent summary
"Non-commitment in mental imagery is distinct from perceptual inattention, and supports hierarchical scene construction"
(by Li, Hammond, & me)
link: doi.org/10.31234/osf...
-- the title's a bit of a mouthful, but the nice thing is that it's a pretty decent summary
October 14, 2025 at 1:22 PM
New preprint!
"Non-commitment in mental imagery is distinct from perceptual inattention, and supports hierarchical scene construction"
(by Li, Hammond, & me)
link: doi.org/10.31234/osf...
-- the title's a bit of a mouthful, but the nice thing is that it's a pretty decent summary
"Non-commitment in mental imagery is distinct from perceptual inattention, and supports hierarchical scene construction"
(by Li, Hammond, & me)
link: doi.org/10.31234/osf...
-- the title's a bit of a mouthful, but the nice thing is that it's a pretty decent summary
Reposted by Adrien Doerig
Over the past year, my lab has been working on fleshing out theory + applications of the Platonic Representation Hypothesis.
Today I want to share two new works on this topic:
Eliciting higher alignment: arxiv.org/abs/2510.02425
Unpaired learning of unified reps: arxiv.org/abs/2510.08492
1/9
Today I want to share two new works on this topic:
Eliciting higher alignment: arxiv.org/abs/2510.02425
Unpaired learning of unified reps: arxiv.org/abs/2510.08492
1/9
October 10, 2025 at 10:13 PM
Over the past year, my lab has been working on fleshing out theory + applications of the Platonic Representation Hypothesis.
Today I want to share two new works on this topic:
Eliciting higher alignment: arxiv.org/abs/2510.02425
Unpaired learning of unified reps: arxiv.org/abs/2510.08492
1/9
Today I want to share two new works on this topic:
Eliciting higher alignment: arxiv.org/abs/2510.02425
Unpaired learning of unified reps: arxiv.org/abs/2510.08492
1/9
Reposted by Adrien Doerig
🧠 New preprint: we show that model-guided microstimulation can steer monkey visual behavior.
Paper: arxiv.org/abs/2510.03684
🧵
Paper: arxiv.org/abs/2510.03684
🧵
October 7, 2025 at 3:22 PM
🧠 New preprint: we show that model-guided microstimulation can steer monkey visual behavior.
Paper: arxiv.org/abs/2510.03684
🧵
Paper: arxiv.org/abs/2510.03684
🧵
Reposted by Adrien Doerig
Awesome work by @jorge-morales.bsky.social and team, using LLMs to suggest that propositional reasoning might be enough to solve classic imagery tasks! ✨
Imagine an apple 🍎. Is your mental image more like a picture or more like a thought? In a new preprint led by Morgan McCarty—our lab's wonderful RA—we develop a new approach to this old cognitive science question and find that LLMs excel at tasks thought to be solvable only via visual imagery. 🧵
Artificial Phantasia: Evidence for Propositional Reasoning-Based Mental Imagery in Large Language Models
This study offers a novel approach for benchmarking complex cognitive behavior in artificial systems. Almost universally, Large Language Models (LLMs) perform best on tasks which may be included in th...
arxiv.org
October 1, 2025 at 6:32 AM
Awesome work by @jorge-morales.bsky.social and team, using LLMs to suggest that propositional reasoning might be enough to solve classic imagery tasks! ✨
Reposted by Adrien Doerig
So excited to see this preprint released from the lab into the wild.
Charlotte has developed a theory for how learning curriculum influences learning generalization.
Our theory makes straightforward neural predictions that can be tested in future experiments. (1/4)
🧠🤖 🧠📈 #MLSky
Charlotte has developed a theory for how learning curriculum influences learning generalization.
Our theory makes straightforward neural predictions that can be tested in future experiments. (1/4)
🧠🤖 🧠📈 #MLSky
🚨 New preprint alert!
🧠🤖
We propose a theory of how learning curriculum affects generalization through neural population dimensionality. Learning curriculum is a determining factor of neural dimensionality - where you start from determines where you end up.
🧠📈
A 🧵:
tinyurl.com/yr8tawj3
🧠🤖
We propose a theory of how learning curriculum affects generalization through neural population dimensionality. Learning curriculum is a determining factor of neural dimensionality - where you start from determines where you end up.
🧠📈
A 🧵:
tinyurl.com/yr8tawj3
The curriculum effect in visual learning: the role of readout dimensionality
Generalization of visual perceptual learning (VPL) to unseen conditions varies across tasks. Previous work suggests that training curriculum may be integral to generalization, yet a theoretical explan...
tinyurl.com
September 30, 2025 at 2:35 PM
So excited to see this preprint released from the lab into the wild.
Charlotte has developed a theory for how learning curriculum influences learning generalization.
Our theory makes straightforward neural predictions that can be tested in future experiments. (1/4)
🧠🤖 🧠📈 #MLSky
Charlotte has developed a theory for how learning curriculum influences learning generalization.
Our theory makes straightforward neural predictions that can be tested in future experiments. (1/4)
🧠🤖 🧠📈 #MLSky
Reposted by Adrien Doerig
I wanted to add some thoughts to this excellent blog post, not detailed, maybe wrong, maybe useful:
1. Unique variance is easy to interpret as a lower bound of what a variable explains (the upper bound being either what the variable explains alone or what the other variables cannot explain uniquely)
1. Unique variance is easy to interpret as a lower bound of what a variable explains (the upper bound being either what the variable explains alone or what the other variables cannot explain uniquely)
Variance partitioning is used to quantify the overlap of two models. Over the years, I have found that this can be a very confusing and misleading concept. So we finally we decided to write a short blog to explain why.
@martinhebart.bsky.social @gallantlab.org
diedrichsenlab.org/BrainDataSci...
@martinhebart.bsky.social @gallantlab.org
diedrichsenlab.org/BrainDataSci...
September 12, 2025 at 1:57 PM
I wanted to add some thoughts to this excellent blog post, not detailed, maybe wrong, maybe useful:
1. Unique variance is easy to interpret as a lower bound of what a variable explains (the upper bound being either what the variable explains alone or what the other variables cannot explain uniquely)
1. Unique variance is easy to interpret as a lower bound of what a variable explains (the upper bound being either what the variable explains alone or what the other variables cannot explain uniquely)
Reposted by Adrien Doerig
Variance partitioning is used to quantify the overlap of two models. Over the years, I have found that this can be a very confusing and misleading concept. So we finally we decided to write a short blog to explain why.
@martinhebart.bsky.social @gallantlab.org
diedrichsenlab.org/BrainDataSci...
@martinhebart.bsky.social @gallantlab.org
diedrichsenlab.org/BrainDataSci...
September 10, 2025 at 4:58 PM
Variance partitioning is used to quantify the overlap of two models. Over the years, I have found that this can be a very confusing and misleading concept. So we finally we decided to write a short blog to explain why.
@martinhebart.bsky.social @gallantlab.org
diedrichsenlab.org/BrainDataSci...
@martinhebart.bsky.social @gallantlab.org
diedrichsenlab.org/BrainDataSci...
Reposted by Adrien Doerig
🧠 New preprint: Why do deep neural networks predict brain responses so well?
We find a striking dissociation: it’s not shared object recognition. Alignment is driven by sensitivity to texture-like local statistics.
📊 Study: n=57, 624k trials, 5 models doi.org/10.1101/2025...
We find a striking dissociation: it’s not shared object recognition. Alignment is driven by sensitivity to texture-like local statistics.
📊 Study: n=57, 624k trials, 5 models doi.org/10.1101/2025...
September 8, 2025 at 6:32 PM
🧠 New preprint: Why do deep neural networks predict brain responses so well?
We find a striking dissociation: it’s not shared object recognition. Alignment is driven by sensitivity to texture-like local statistics.
📊 Study: n=57, 624k trials, 5 models doi.org/10.1101/2025...
We find a striking dissociation: it’s not shared object recognition. Alignment is driven by sensitivity to texture-like local statistics.
📊 Study: n=57, 624k trials, 5 models doi.org/10.1101/2025...
Reposted by Adrien Doerig
What determines where specialisation for sensory information occurs in the cortex? We observe a spatial competition between primary sensory areas and the Default Mode Network, using our new Spatial Component Decomposition method. Work by @ulysse-klatzmann.bsky.social , with Bazin & Daniel Margulies
Spatial layout of visual specialization is shaped by competing default mode and sensory networks https://www.biorxiv.org/content/10.1101/2025.09.08.674858v1
September 9, 2025 at 8:51 AM
What determines where specialisation for sensory information occurs in the cortex? We observe a spatial competition between primary sensory areas and the Default Mode Network, using our new Spatial Component Decomposition method. Work by @ulysse-klatzmann.bsky.social , with Bazin & Daniel Margulies
Reposted by Adrien Doerig
Preprint alert! 🚨
1/ How does deep sleep reshape our memories? Our new study shows that slow-wave sleep (SWS) reorganises episodic memory networks, shifting recall from the parietal cortex to the anterior temporal lobe (ATL). With Polina Perzich and @bstaresina.bsky.social . A thread below👇
1/ How does deep sleep reshape our memories? Our new study shows that slow-wave sleep (SWS) reorganises episodic memory networks, shifting recall from the parietal cortex to the anterior temporal lobe (ATL). With Polina Perzich and @bstaresina.bsky.social . A thread below👇
Slow wave sleep supports the reorganisation of episodic memory networks https://www.biorxiv.org/content/10.1101/2025.03.24.644966v1
March 25, 2025 at 5:49 PM
Preprint alert! 🚨
1/ How does deep sleep reshape our memories? Our new study shows that slow-wave sleep (SWS) reorganises episodic memory networks, shifting recall from the parietal cortex to the anterior temporal lobe (ATL). With Polina Perzich and @bstaresina.bsky.social . A thread below👇
1/ How does deep sleep reshape our memories? Our new study shows that slow-wave sleep (SWS) reorganises episodic memory networks, shifting recall from the parietal cortex to the anterior temporal lobe (ATL). With Polina Perzich and @bstaresina.bsky.social . A thread below👇
Reposted by Adrien Doerig
A transformation from vision to imagery in the human brain. Intriguing new preprint by Roy & Naselaris et al for anyone interested in mental imagery!
www.biorxiv.org/content/10.1...
www.biorxiv.org/content/10.1...
A transformation from vision to imagery in the human brain
Extensive work has shown that the visual cortex is reactivated during mental imagery, and that models trained on visual data can predict imagery activity and decode imagined stimuli. These findings ma...
www.biorxiv.org
September 4, 2025 at 9:28 AM
A transformation from vision to imagery in the human brain. Intriguing new preprint by Roy & Naselaris et al for anyone interested in mental imagery!
www.biorxiv.org/content/10.1...
www.biorxiv.org/content/10.1...
Reposted by Adrien Doerig
🧠 “You never forget how to ride a bike”, but how is that possible?
Our study proposes a bio-plausible meta-plasticity rule that shapes synapses over time, enabling selective recall based on context
Our study proposes a bio-plausible meta-plasticity rule that shapes synapses over time, enabling selective recall based on context
Context selectivity with dynamic availability enables lifelong continual learning
“You never forget how to ride a bike”, – but how is that possible? The brain is able to learn complex skills, stop the practice for years, learn other…
www.sciencedirect.com
September 4, 2025 at 4:00 PM
🧠 “You never forget how to ride a bike”, but how is that possible?
Our study proposes a bio-plausible meta-plasticity rule that shapes synapses over time, enabling selective recall based on context
Our study proposes a bio-plausible meta-plasticity rule that shapes synapses over time, enabling selective recall based on context
Reposted by Adrien Doerig
New paper with @ManuKirberg 💭
Is “unconscious mental imagery” real? The evidence is weaker than it seems. We explain why—and how to move the debate forward.
🔗 www.sciencedirect.com/science/arti...
Is “unconscious mental imagery” real? The evidence is weaker than it seems. We explain why—and how to move the debate forward.
🔗 www.sciencedirect.com/science/arti...
Aphantasia and the unconscious imagery hypothesis
Until recently, mental imagery has largely been regarded as an exclusively conscious phenomenon. However, recent empirical results suggest that mental…
www.sciencedirect.com
September 4, 2025 at 5:12 AM
New paper with @ManuKirberg 💭
Is “unconscious mental imagery” real? The evidence is weaker than it seems. We explain why—and how to move the debate forward.
🔗 www.sciencedirect.com/science/arti...
Is “unconscious mental imagery” real? The evidence is weaker than it seems. We explain why—and how to move the debate forward.
🔗 www.sciencedirect.com/science/arti...
Reposted by Adrien Doerig
🔍 Large language models, similar to those behind ChatGPT, can predict how the human brain responds to visual stimuli
New study by @adriendoerig.bsky.social @freieuniversitaet.bsky.social with colleagues from Osnabrück, Minnesota and @umontreal-en.bsky.social
Read the whole story 👉 bit.ly/3JXlYmO
New study by @adriendoerig.bsky.social @freieuniversitaet.bsky.social with colleagues from Osnabrück, Minnesota and @umontreal-en.bsky.social
Read the whole story 👉 bit.ly/3JXlYmO
September 2, 2025 at 7:01 AM
🔍 Large language models, similar to those behind ChatGPT, can predict how the human brain responds to visual stimuli
New study by @adriendoerig.bsky.social @freieuniversitaet.bsky.social with colleagues from Osnabrück, Minnesota and @umontreal-en.bsky.social
Read the whole story 👉 bit.ly/3JXlYmO
New study by @adriendoerig.bsky.social @freieuniversitaet.bsky.social with colleagues from Osnabrück, Minnesota and @umontreal-en.bsky.social
Read the whole story 👉 bit.ly/3JXlYmO
Reposted by Adrien Doerig
What makes humans similar or different to AI? In a paper out in @natmachintell.nature.com led by @florianmahner.bsky.social & @lukasmut.bsky.social, w/ Umut Güclü, we took a deep look at the factors underlying their representational alignment, with surprising results.
www.nature.com/articles/s42...
www.nature.com/articles/s42...
Dimensions underlying the representational alignment of deep neural networks with humans - Nature Machine Intelligence
An interpretability framework that compares how humans and deep neural networks process images has been presented. Their findings reveal that, unlike humans, deep neural networks focus more on visual ...
www.nature.com
June 23, 2025 at 8:03 PM
What makes humans similar or different to AI? In a paper out in @natmachintell.nature.com led by @florianmahner.bsky.social & @lukasmut.bsky.social, w/ Umut Güclü, we took a deep look at the factors underlying their representational alignment, with surprising results.
www.nature.com/articles/s42...
www.nature.com/articles/s42...
Reposted by Adrien Doerig
Our target discussion article out in Cognitive Neuroscience! It will be followed by peer commentary and our responses. If you would like to write a commentary, please reach out to the journal! 1/18 www.tandfonline.com/doi/full/10.... @cibaker.bsky.social @susanwardle.bsky.social
August 29, 2025 at 6:43 PM
Our target discussion article out in Cognitive Neuroscience! It will be followed by peer commentary and our responses. If you would like to write a commentary, please reach out to the journal! 1/18 www.tandfonline.com/doi/full/10.... @cibaker.bsky.social @susanwardle.bsky.social
Reposted by Adrien Doerig
So happy to see this work out! 🥳
Huge thanks to our two amazing reviewers who pushed us to make the paper much stronger. A truly joyful collaboration with @lucasgruaz.bsky.social, @sobeckerneuro.bsky.social, and Johanni Brea! 🥰
Tweeprint on an earlier version: bsky.app/profile/modi... 🧠🧪👩🔬
Huge thanks to our two amazing reviewers who pushed us to make the paper much stronger. A truly joyful collaboration with @lucasgruaz.bsky.social, @sobeckerneuro.bsky.social, and Johanni Brea! 🥰
Tweeprint on an earlier version: bsky.app/profile/modi... 🧠🧪👩🔬
August 25, 2025 at 4:18 PM
So happy to see this work out! 🥳
Huge thanks to our two amazing reviewers who pushed us to make the paper much stronger. A truly joyful collaboration with @lucasgruaz.bsky.social, @sobeckerneuro.bsky.social, and Johanni Brea! 🥰
Tweeprint on an earlier version: bsky.app/profile/modi... 🧠🧪👩🔬
Huge thanks to our two amazing reviewers who pushed us to make the paper much stronger. A truly joyful collaboration with @lucasgruaz.bsky.social, @sobeckerneuro.bsky.social, and Johanni Brea! 🥰
Tweeprint on an earlier version: bsky.app/profile/modi... 🧠🧪👩🔬
Reposted by Adrien Doerig
🚨We believe this is a major step forward in how we study hippocampus function in healthy humans.
Using novel behavioral tasks, fMRI, RL & RNN modeling, and transcranial ultrasound stimulation (TUS), we demonstrate the causal role of hippocampus in relational structure learning.
Using novel behavioral tasks, fMRI, RL & RNN modeling, and transcranial ultrasound stimulation (TUS), we demonstrate the causal role of hippocampus in relational structure learning.
August 28, 2025 at 2:00 PM
🚨We believe this is a major step forward in how we study hippocampus function in healthy humans.
Using novel behavioral tasks, fMRI, RL & RNN modeling, and transcranial ultrasound stimulation (TUS), we demonstrate the causal role of hippocampus in relational structure learning.
Using novel behavioral tasks, fMRI, RL & RNN modeling, and transcranial ultrasound stimulation (TUS), we demonstrate the causal role of hippocampus in relational structure learning.
Reposted by Adrien Doerig
Very happy to see this preprint out! The amazing @danwang7.bsky.social was on fire sharing this work at #ECVP2025, gathering loads of attention, and here you can find the whole thing!
Using RIFT we reveal how the competition between top-down goals and bottom-up saliency unfolds within visual cortex.
Using RIFT we reveal how the competition between top-down goals and bottom-up saliency unfolds within visual cortex.
August 28, 2025 at 10:09 AM
Very happy to see this preprint out! The amazing @danwang7.bsky.social was on fire sharing this work at #ECVP2025, gathering loads of attention, and here you can find the whole thing!
Using RIFT we reveal how the competition between top-down goals and bottom-up saliency unfolds within visual cortex.
Using RIFT we reveal how the competition between top-down goals and bottom-up saliency unfolds within visual cortex.
Reposted by Adrien Doerig
Job alert 🚨 Fully funded PhD position available in our Maastricht lab! Are you interested in predictive processing, individual differences, and computational modelling of behavioural and neural data? Please apply! #NeuroJobs vacancies.maastrichtuniversity.nl/job/Maastric...
PhD Candidate: cognitive computational neuroscience of individual differences
PhD Candidate: cognitive computational neuroscience of individual differences
vacancies.maastrichtuniversity.nl
August 28, 2025 at 9:11 AM
Job alert 🚨 Fully funded PhD position available in our Maastricht lab! Are you interested in predictive processing, individual differences, and computational modelling of behavioural and neural data? Please apply! #NeuroJobs vacancies.maastrichtuniversity.nl/job/Maastric...