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1/11 Happy to share our TICS paper on using the flexibility of one of the most basic cognitive functions, perception, to understand one of the most complex cognitive dysfunctions, psychiatric conditions (also my first formal work in computational psychiatry 🎉) 📄: www.cell.com/trends/cogni... 🧵 : 👇

Computational models are a key part of science but discovering new ones is hard! DataDIVER discovers concise models from data, which surface new mechanistic ideas and clear predictions for future experiments From Google Deepmind Neuroscience Lab + collaborators www.biorxiv.org/content/10.6...

New Annual Review with @nathanieldaw.bsky.social: “Planning in the Brain: It's Not What You Think It Is.” We argue that the brain's 'planning' machinery is mostly used for learning from simulated experience, and that thinking prospectively at decision time is just one special case of this process.

Super proud to have contributed to this amazing team effort, led by @sreejan.bsky.social and @botoscsabi.bsky.social! We asked: Which AI models learn to play video games like humans, comparing both behavior and internal representations. The answer surprised us! Check out our paper and post below

Perceptual multistability, observed across species and sensory modalities, offers valuable insights into numerous cognitive functions and dysfunctions. For instance, differences in temporal dynamics and information integration during percept formation often distinguish clinical from nonclinical populations. Computational psychiatry can elucidate these variations through two primary approaches: (i) Bayesian modeling, which treats perception as an unconscious inference, and (ii) an active, information-seeking perspective (e.g., reinforcement learning), which frames perceptual switches as internal actions. Our synthesis aims to leverage multistability to bridge these computational psychiatry subfields, linking human and animal studies as well as connecting behavior to underlying neural mechanisms. Perceptual multistability emerges as a promising noninvasive tool for clinical applications, facilitating translational research and enhancing our mechanistic understanding of cognitive processes and their impairments.

New preprint w/ @fredcallaway.bsky.social! How does the brain decide which computations to run? We combine rational meta-reasoning with a meta-learning algorithm to build a recurrent network that learns to select computations. www.biorxiv.org/content/10.6...

Only one week left until our PhD application deadline @mpicybernetics.bsky.social. If you’re interested in the intersection of RL, RNNs, and analyses of neural & behavioral data from novel, cross-species foraging experiments, make sure to submit your application by June 15! #NeuroJobs More info👇

Jeee 🐦‍⬛ I am very proud of our joint effort with @sreejan.bsky.social on the project "Reason to Play" LRMs show human-like rule discovery, and their hidden states predict human brain activity during gameplay 10x better than previous methods Interactive demo + paper: botcs.github.io/reason-to-pl...

24-Hour Final Call for BAMB! 2026 ⏳ Join us in Barcelona (July 12–23) and learn from our expert faculty: @meganakpeters.bsky.social @marcelomattar.bsky.social @khamascience.bsky.social @thecharleywu.bsky.social Apply now: www.bambschool.org

Two hallmarks of biological computation are its flexibility and efficiency. These features are often attributed to cognitive control processes that balance external utility against computational cost. However, how the brain could implement such adaptive control remains unknown. Here, we provide one possible answer by combining the computational theory of rational meta-reasoning with a meta-learning algorithm recently proposed as a model of prefrontal cortex. This yields a recurrent neural network model that learns to select computations. In simple choice tasks, the model approximates the algorithms and representations of optimal symbolic models and reproduces neural dynamics observed in macaque orbitofrontal cortex. In multi-step planning tasks, the model replicates key behavioral signatures of human planning strategies and captures human neural dynamics associated with step-by-step mental simulation. Our framework unifies meta-reasoning and meta-learning by showing that learning to reason can be understood as learning to learn from information generated by one’s own cognitive operations, providing a mechanistic account of how adaptive control of thought can be implemented in neural systems. ### Competing Interest Statement The authors have declared no competing interest.

New preprint! 🚨 Work w/ Sangita Dandekar & Clayton Curtis at NYU during my PhD. We show how prefrontal & visual cortices coordinate to keep working memories alive via localized & long-range β-band dynamics. 🧠 www.biorxiv.org/content/10.6... (1/5)

If these research directions resonate with you and you're interested in joining our team, we now have 2 open PhD positions! More information about the positions: nextcloud.tuebingen.mpg.de/index.php/s/... More information about our research: www.kyb.tuebingen.mpg.de/906930/natur... #NeuroJobs