The brain uses dimensionality reduction as a built-in feature of its wiring. Network geometry shapes the transformation of task representations across the human cortex www.nature.com/articles/s41... #neuroscience

Network geometry shapes the transformation of task representations across the human cortex - Nature Neuroscience

The dimensionality of intrinsic brain connectivity systematically shapes how task representations compress and expand across the cortex, and activity flow modeling shows the active role of network geo...

nature.com

👇 one of my favourite brain regions Functional architecture of the human insula | Nature www.nature.com/articles/s41...

Functional architecture of the human insula - Nature

A functional architecture of the human insula links cytoarchitecture to directed and asymmetric electrophysiological interactions, with mechanistic implications for cognitive modulation of pain and in...

nature.com

Our reward function compression work is now out in Nature Communications! 🎉 In it, we present and empirically test a computational framework that formalizes the trade-offs between goal flexibility and learning efficiency. @annecollins.bsky.social @natureportfolio.nature.com doi.org/10.1038/s414...

How do minds think about themselves, and how do we study it? New review in @natrevneuro.nature.com www.nature.com/articles/s41...

How can we design experiments that make computational models disagree? One section of our new @natrevneuro.nature.com Review with @kriegeskorte.bsky.social and @heikoschuett.bsky.social examines studies that used stimulus sets designed to elicit distinct predictions from competing models. 1/16

New paper from the BCM Neurosurgery research team! "Neural basis of compositional control" led by @assiachericoni.bsky.social and @justfineneuro.bsky.social ! 🧵 www.nature.com/articles/s41...

Neural basis of compositional control - Nature

Behaviour of human participants in a prey-pursuit task reflects dynamic blending of goal-specific control policies, with hippocampus estimating latent states, anterior cingulate cortex orchestrating p...

nature.com

1/ I am thrilled to begin sharing the latest release of pyEM, a Python package I built for fitting cognitive computational models to behavioral data using hierarchical expectation maximization (EM) with maximum a posteriori estimation (MAP). github.com/shawnrhoads/...

Is brain circuit targeted TMS better than standard clinical TMS for treating depression? New study led by Joe Taylor @braincircuits.bsky.social out today in @jamapsychiatry.com says yes! Connectivity- vs Scalp-Based Targeting of Accelerated TMS for Depression jamanetwork.com/journals/jam...

Connectivity- vs Scalp-Based Targeting of Accelerated TMS for Depression

This randomized clinical trial estimates the effect size of connectivity- vs scalp-based targeting of accelerated transcranial magnetic stimulation for treatment-resistant depression.

jamanetwork.com

Our new paper is out this week in Nature Neuroscience! www.nature.com/articles/s41... We built a BCI that works with the brain's natural geometry — and we found that people could learn to play a video game with their brains in <1 hr of training. This efficiency is groundbreaking & here's why:

Human learning of noninvasive brain–computer interfaces via manifold geometry - Nature Neuroscience

Busch et al. use nonlinear neural manifolds to help humans gain rapid control over a noninvasive brain–computer interface, allowing them to learn how to play a video game with real-time fMRI neurofeed...

nature.com

🎉New paper out today in Nature Reviews Psychology🎉 with @mjdahl.bsky.social, @mariamaly.bsky.social, and @thiasmittner.bsky.social. We've been working on a unified framework for attentional states and the dynamics of transitions between them. 🧵

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 article out in @natcomms.nature.com Reference-point dependent reinforcement learning in humans and rats By Lachlan Ferguson, Magda Soukupova (co-first), Seb Bouret, me and Shauna Parkes. www.nature.com/articles/s41...

OFC encodes option values while LPFC tracks spatial locations during both overt (eye movements) and covert (internal) attention. Effects of overt and covert attention on decision-making dynamics in prefrontal cortex doi.org/10.64898/202... #neuroscience

New preprint as my first post! "Monosynaptic connections link functionally similar regions in human cortex." We use electrical stimulation + fMRI in epilepsy patients to map whole-brain monosynaptic connectivity at 42 cortical sites. doi.org/10.64898/202... 1/n

Our brains don’t think in a steady stream. They rhythmically “scan” different alternatives, using attention cycles to sample and compare choices. Rhythmic sampling of multiple decision alternatives in the human brain www.nature.com/articles/s41... #neuroscience

Rhythmic sampling of multiple decision alternatives in the human brain - Nature Communications

How humans process competing information when making multi-alternative decisions remains unclear. Here, the authors show that the brain resolves the trade-off between “evaluating within” and “comparin...

nature.com

🎉New positions!🎉 Postdoc and Research technician openings in our lab @yale for computational psychiatry research. Technician position a great stepping stone to grad school. More info: rutledgelab.org/positions Work w/big data from our smartphone apps and large clinical samples happinessquest.app

Postdoc and Research Technician Positions — Rutledge Lab

Now hiring a postdoc and a research technician with an interest in neuroeconomics or computational psychiatry.

rutledgelab.org

Coordinated human prefrontal dynamics sustain task-state representations during learning www.biorxiv.org/content/10.6...

Delighted to share our new publication in @apertureohbm.bsky.social - the "Human Single-Neuron Pipeline" for managing human single-neuron research projects! It's an open-source resource of new & existing tools & resources curated for HSN projects: hsnpipeline.github.io

Aperture Neuro@apertureohbm.bsky.social · 6mo ago

Donoghue et al. introduce the Human Single-Neuron Pipeline, an open source processing pipeline for collecting and analyzing human single-neuron data: doi.org/10.52294/001... @ohbmossig.bsky.social @ohbmofficial.bsky.social #OpenDatasets #SpecialIssues

👋 Happy to see this paper published in PNAS www.pnas.org/doi/10.1073/pnas.2529176123 Asking: is curiosity a homeostatic drive, or a policy learned through reinforcement? How can we tell, and why does it matter? 🧵👇 With Jane Mok, @chrisbaldassano.bsky.social, Caroline Marvin, and Daphna Shohamy 🙏

Learning reinforces curiosity for related information | PNAS

Human curiosity is dynamic, however the principles governing its fluctuations remain debated. Here, we test two competing hypotheses about how past...

pnas.org

Yaniv Abir@yanivabir.bsky.social · 2y ago

What drives human curiosity? Is it a need to balance stimulation — or something we learn over time? In our 🚨 new preprint, we show that learning reinforces curiosity, especially for related content. osf.io/9bw6j_v2 w/ Jane Mok, @chrisbaldassano.bsky.social , Caroline Marvin, Daphna Shohamy 🧵👇

www.nature.com/articles/s41...

Clarifying the conceptual dimensions of representation in neuroscience

Nature Reviews Neuroscience - Appeals to representation are widespread, despite neuroscientists’ uncertainty about what kind of findings count as evidence for such claims. In this...

nature.com

Excited to share our new Nature Methods paper on DeepCor, a contrastive autoencoder for denoising fMRI. DeepCor separates neural signal from noise at the voxel level, works for single participants, and outperforms CompCor by up to 215% on real data. 1/7 doi.org/10.1038/s415...

DeepCor: denoising fMRI data with contrastive autoencoders - Nature Methods

DeepCor is a deep-learning-based denoising approach for task-based and resting-state fMRI data that can be used even for single participants.

doi.org

📢 New preprint out now! In three samples (N=594), including a U.S.-representative cohort and a test-retest cohort, we formalize social motivation, learning, and homeostatic control within a single computational framework and identify a phenotype linked to chronic loneliness doi.org/10.31234/osf...

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.

Planning in the Brain: It&apos;s Not What You Think It Is

The neuroscience of planning has long been analogized to search algorithms in artificial intelligence (AI), which simulate future actions to guide immediate choices. We argue that advances in both neu...

annualreviews.org

What makes adaptive decision-making possible? New preprint connecting cognitive, social, biological, and computational views! w/ Dorst, FeldmanHall, @smfleming.bsky.social, @catehartley.bsky.social, Gottlieb, Lejarraga, Müller-Trede, @angelaradulescu.bsky.social, and Rosati osf.io/preprints/ps...

Posting on behalf of Brendan: Common feature selection practices may capture only the “tip of the iceberg” of distributed brain–behavior relationships Specifically, typically discarded features can predict behavior just as well as top-ranked ones, yet produce diverging interpretations

Nature Human Behaviour@nathumbehav.nature.com · 6mo ago

Feature selection leads to divergent neurobiological interpretations of brain-based machine learning biomarkers

New preprint from my lab! We study how reinforcement learning & selective attention interact. To do so, we built a set of models describing different ways that value & reward prediction error can modulate top-down attention. We compare model outcomes to monkey data from a color value learning task

bioRxiv Neuroscience@biorxiv-neursci.bsky.social · 7mo ago

Modulation of feature attention by reward prediction error explains value learning behavior https://www.biorxiv.org/content/10.64898/2026.04.10.717847v1

📣🔥 Early bird registration now open for 2026 Computational Psychiatry Conference cpconf.org at Yale July 14-16. Late-breaking abstracts now open (deadline: May 8) Trainee pre-conference (July 13) registration open (free!) See you in New Haven! #CPConf2026

Computational Psychiatry Conference

New Haven, USA (July 14-16, 2026)

cpconf.org

New this year: free trainee pre-conference on July 13 organized by @cehaeffner.bsky.social @adanyajohnson.bsky.social Pre-conference tickets are free for main conference attendees but space is limited to 75 people so register early! www.cpconf.org/faq-cpconf20...

🧵 I gave Claude two things: a short paper (doi.org/10.1073/pnas...) and a raw behavioural dataset with 3 lines of variable descriptions. Then I asked it to fit three computational RL models described only by equations in the manuscript. No code, no toolbox, no guidance on the fitting procedure. 1/3

Excited to share our new paper proposing a new computational model accounting for the dynamic interaction btw drug #craving and #decision-making, led by Kaustubh Kulkarni, MD/PhD rdcu.be/fab6B #AddictionScience #Neuroscience #ComputationalPsychiatry #BehavioralScience