
3D Vision + Visual Experience / NOMIS Fellow @ItalianAcademy / Presidential Scholar @columbiacss / @KriegeskorteLab + @ZuckermanBrain
New York, NY
Joined December 2017
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Please vote for the Linton Color Constancy Illusion in this year's Illusion of the Year Competition!
Link: vote.illusionoftheyear.com/
Description: One of the central questions in cognitive psychology is how we distinguish perception (what we visually experience) from cognition (what we think or judge about what we see). Color constancy illusions are generally thought to fall on the perception side of this divide: changing the surrounding context of a disk appears to change the color of the disk itself. But is this really a change in visual experience, or merely a change in how we interpret and judge the color? The Linton Color Constancy Illusion challenges the traditional view, suggesting that this effect reflects our cognition rather than our perception.
Paul Linton retweeted
It was awesome to sit down with Ned Block (@De_dicto) at @eleosai's conference in Berkeley to explore why Ned is extremely skeptical today's AIs could be conscious. We'll have a number of luminary interviews like this coming out over the next few months!
youtu.be/am0uENy849w?si=9gwE…
Paul Linton retweeted
Very excited to share the most important discovery of my career yet: your brain switches between a conscious and an unconscious mode ~15 times every second. Consciousness may depend not on where in the brain activity occurs but when.
Find out more below.
bit.ly/3WvA6di
1/ We discovered something totally unexpected about the neural structure of consciousness that overturns decades of thinking on its biological basis and brings us significantly closer to the recipe for generating conscious awareness.
New preprint: biorxiv.org/content/10.64898… (led by @HesseJanis, with @FrankLanfranchi, @_conor_mcgrory, @Yuelin_Shi, and @EngelTatiana)
Paul Linton retweeted
How does the brain group a scene into objects?
Exciting new preprint by Sami Mollard! GammaNet’s recurrent dynamics predict human grouping times without training on reaction-time data.
arxiv.org/abs/2610.05419
#Neuroscience #PerceptualGrouping

ALT Four panels show GammaNet progressively grouping the cued person in a photograph of two people at a restaurant. At T = 0, a red dot cues the man's arm. Yellow highlighting spreads across his arm, torso and head at T = 5, 10 and 15. The caption reads: The network progressively groups the cued person, shown in yellow. T indicates network processing steps.
Paul Linton retweeted
From a NeuroAI perspective, it's really annoying that feedforward neural nets rule the world. Unrolling equivalence notwithstanding, what inductive biases does recurrence buy us? Looking forward to reading this.
1/5
New preprint: what does lateral recurrence add to visual representations?
Recurrence reuses the same weights over time: more computation, no new parameters.
Does that extra computation refine representations? If so, which ones?
doi.org/10.64898/2026.10.02.…
Paul Linton retweeted
1/5
New preprint: what does lateral recurrence add to visual representations?
Recurrence reuses the same weights over time: more computation, no new parameters.
Does that extra computation refine representations? If so, which ones?
doi.org/10.64898/2026.10.02.…
Paul Linton retweeted
Journal of Illusion
journalofillusion.net/index.…
Paul Linton retweeted
Nice NY Times article on infant consciousness by @carlzimmer, mentioning Topun Austin, @LorinaNaci, @joel_frohlich, and me.
Are Babies Conscious?
nytimes.com/2026/10/05/scien…
Paul Linton retweeted
Happy to share a new book chapter I wrote with Romeo Chua, Dan Weeks, and Tim Welsh: Perceptual–Motor Interaction: Some Implications for Human–Computer Interaction, in the Human–Computer Interaction Handbook.
Paul Linton retweeted
We’re hiring a tenure-stream Assistant Professor at the University of Waterloo’s School of Optometry & Vision Science!
Please share with anyone who might be interested!
ofas.uwaterloo.ca/job-detail…
Paul Linton retweeted
This paper is now published!
cell.com/iscience/fulltext/S…
And here is a news digest from Yahoo! Tech:
tech.yahoo.com/ai/articles/a…
New preprint from our lab!
Systematic image perturbations reveal persistent gaps between human and machine vision.
biorxiv.org/content/10.64898…
We compared humans and >200 DNNs in their visual cue usage during object recognition, and reveal systematic differences.
Paul Linton retweeted
Very excited to ramp up our work working with Tom! We're looking for an exceptional postdoctoral researcher (details below) to work jointly with Tom and Basis on MARA project--our effort to build robotic agents that actively learn models of the physical world.
Paul Linton retweeted
Recordings for our CCN 2026 satellite at ZI are now available on the event page!
Modeling and Understanding Human Brain Computation at Scale
CCN 2026 Satellite Event, August 2, 2026
kriegeskorte-lab.github.io/c…
Thanks to all the speakers: Yao Wang, Paul Scotti @humanscotti, Andrew Luo, Iris Groen, Colin Conwell, Ratan Murty @apurvaratan, Jim DiCarlo @JamesJDiCarlo, Shalmali Joshi, and Martin Schrimpf @martin_schrimpf
Panelists: Patrick Mineault @patrickmineault, Teon Brooks @teonbrooks, Shalmali Joshi @shalmali_joshi_, and Niko Kriegeskorte @KriegeskorteLab
And co-organizers: Pinyuan Feng @Pinyuan3, Fan Cheng @LibraCheng, Andrew Luo, Niko Kriegeskorte and everyone else in the @KriegeskorteLab!
Excited to announce CCN @CogCompNeuro Satellite Event:
Modeling and Understanding Human Brain Computation at Scale
We explore how we can best leverage the many recent advances with neural network modeling and brain foundation models toward theoretical insight and benefit for humanity.
Sunday, August 2, 2026
@ZuckermanBrain
Organized with @Pinyuan3, @LibraCheng, Andrew Luo, and @KriegeskorteLab
Paul Linton retweeted
When I interview graduate students at Harvard, a key trait I look for is perseverance: the ability to pursue scientific discovery relentlessly despite setbacks.
Mengmi Zhang has always been one of the clearest examples of why.
When I first met her during her PhD, I was amazed by her energy and independence. She independently came with 5 excellent ideas that she wanted to go after - highly unusual for a PhD student. And all 9 of them were really good : )
What stood out even more was what happened when the obvious paths stopped working. In every PhD there is a time when things are not working as well or as fast as the student imagined. Those moments separate the woman and the girl, men and the boy.
For one project, we wanted to understand something humans do almost intuitively: finding Waldo. How do we search a cluttered scene for an object, even when its appearance changes or when we have never searched for that particular object before?
We collected enormous amounts of eye movement data across increasingly difficult scenes. But none of the existing models was able to explain the behavior well enough. State-of-the-art work struggled to bridge the gap between low-level and high-level visual features.
Mengmi did not give up. She connected with the leading experts in visual search, pulled ideas from adjacent fields, and developed a new computational model of her own. Her model capitalized on the power of bottom-up architectures but also captured the guidance provided by top-down signals, linking vision and higher cognition. This work became our 2018 paper “Finding Any Waldo” published in Nature Communications.
That persistence remained constant throughout the years we worked together. Across her PhD and subsequent postdoc with me, we collaborated on more than a dozen publications spanning AI, computer vision, computational neuroscience, and cognition.
Today, Mengmi runs her own lab at NTU in Singapore, studying how insights from human cognition and neuroscience can help build more capable AI systems. And we continue to collaborate, as peers.
Mengmi is also a lead organizer of the Brains, Minds and Machines summer course with a selected cadre of 35 top scholars from all over the world. Students love her dedication, wits, and passion for science.
One of the most rewarding parts of my career is seeing my mentees grow into independent scientists and leaders of their own.
I was especially proud to see Mengmi be recognized as MIT Technology Review’s Innovators Under 35.
Congratulations, Mengmi. So proud. Can’t wait to see what you accomplish next.
🤖 Made with AI
Paul Linton retweeted
Thrilled to be named one of MIT Tech Review’s 2026 Innovators Under 35 — Global List! 🎉I asked ChatGPT to create this profile graphic to celebrate 😊 Grateful to all my mentors, students &postdocs who have been part of this journey: ter.li/0nzrhrve
#MITTR35 #NeuroAI #AI
🤖 Made with AI
Paul Linton retweeted
on a boat in the galapagos islands: a talk on anthropic's j-space and the global workspace theory of consciousness, followed by a useful exchange with @standehaene and others.
youtube.com/watch?v=yGN7Hpy_…
Paul Linton retweeted
So pleased to see this Phenomenal Report out in the Journal of Illusion!
A wonderful initiative by @AkiyoshiKitaoka, and special thanks to @kawabe_takahiro as editor
Full Text:
journalofillusion.net/index.…
PDF:
journalofillusion.net/index.…
We are happy to announce that our latest article has been published in the Journal of Illusion.
Linton , P. (2026). Linton stereo illusion. Journal of Illusion, 6. doi.org/10.47691/joi.v6.1121…
Movie 4.
(To watch this demo, red-blue stereo glasses are required.)
To be continued...
So pleased to see this Phenomenal Report out in the Journal of Illusion!
A wonderful initiative by @AkiyoshiKitaoka, and special thanks to @kawabe_takahiro as editor
Full Text:
journalofillusion.net/index.…
PDF:
journalofillusion.net/index.…
We are happy to announce that our latest article has been published in the Journal of Illusion.
Linton , P. (2026). Linton stereo illusion. Journal of Illusion, 6. doi.org/10.47691/joi.v6.1121…
Movie 4.
(To watch this demo, red-blue stereo glasses are required.)
To be continued...
Paul Linton retweeted
An important future direction in Neuro-AI is distilling brain activity into increasingly powerful computational backbones.
The resulting networks will perform the tasks as before or even better (if you believe in general task solution information being embedded in the brain data) and they will also serve as models of the brain computation after fusing the brain representation and with task computation.
Using lightweight (both parameter and data efficient) LORA fine tuning is a step in the right direction.
Great work by Josh @zfjoshying and the MIND lab team on executing this idea and showing it achieves SOTA on the Algonauts 2025 brain encoding challenge leaderboard!
Check out the thread below to see why we are excited about this approach!
New blogpost out 📄
We show that scaling parameter-efficient finetuning (PEFT) for brain encoding is very powerful! 🧠
1st place on CNeuroMod/Algonauts 2025 leaderboard🥇
with ~5% of trainable parameters and trains 100x faster
macaron.im/mindlab/research/…


























