1/N: Dear cognitive map fans, I’d like to share a model I’ve been working on for a while (clearing backlog :). I show how a vector navigation architecture (VNA) and a “positional inference network” (PIN) can build Universal Cognitive Maps (UCMs) for abstract spaces.
www.biorxiv.org/content/10.6...
Now out in Hippocampus. Fei Wang‘s model of Trace Vector Cells and intra-subiculum processing, consistent with know effects in CA1.
onlinelibrary.wiley.com/share/CPZPYM...
Human hippocampal theta–gamma coupling coordinates sequential planning during navigation
Impressive study from Dan Bush's Lab at UCL:
www.pnas.org/doi/10.1073/...
1/6 Happy to share our new paper with @grassocamille.bsky.social and @virginievanw.bsky.social: "Nested contextual change and the temporal compression of episodic memory". www.biorxiv.org/content/10.6...
1/9 New paper with @gonzalezgarcia.bsky.social and @lindedomingo.bsky.social : “Characterising semantic prioritisation in visual working memory.”
Core question: when we hold visual info briefly in mind, what gets accessed first: perceptual details or semantic meaning?
New preprint 🚨
Across multiple tasks, we show that higher-level info is more readily accessible in WM before evidence accumulation begins. Attention then boosts perceptual detail.
www.biorxiv.org/content/10.6...
A lot of fun with my colleagues @ckerren.bsky.social and @gonzalezgarcia.bsky.social
Our new preprint is out on bioRxiv! @doellerlab.bsky.social
We show that eye-movement sequences actively organize information by aligning with underlying structure and flexibly adapting to cognitive demands in working memory.
www.biorxiv.org/content/10.6...
Our paper is out in @natneuro.nature.com!
www.nature.com/articles/s41...
We develop a geometric theory of how neural populations support generalization across many tasks.
@zuckermanbrain.bsky.social
@flatironinstitute.org
@kempnerinstitute.bsky.social
1/14
Interested in cognitive maps? Wondering how you should best train your participants? Luckily for you, we have investigated just that! Very excited to share a preprint from my PhD in which we show how initial training curriculum affects the cognitive maps we form
osf.io/preprints/ps...
[1/6]
Super thrilled to finally share the results of our team effort, from the lab of the one and only @ayab.bsky.social. Hopefully it will stir interesting discussions about how the brain balances continuity and segmentation
Final paper of my PhD 🤗
www.nature.com/articles/s44...
There is growing interest in how cognitive control may improve value-based decision making.
However, we find that a recent paper overestimated the role of control in their task, leading to erroneous interpretations of dACC recordings.
Finally: the fantastic #registeredreport from bsky-less Roni Tibon is out: www.nature.com/articles/s41... showing less difference between #episodic vs. #semantic #memory than one might have thought.
Proud to have contributed a tiny part to this great paper.
🚨 New paper out in Science Advances 🚨
With @suryagayet.bsky.social and @peelen.bsky.social, in two fMRI studies we investigate mental object rotations that are driven by the scene context, rather than purely by cognitive operations. 🧵 www.science.org/doi/10.1126/...
How do we achieve few-shot generalization? New work led by @fabianrenz.bsky.social dives into the role of replay in learning and using structure to generalize reward. Dream team effort with Shany Grossman @nathanieldaw.bsky.social Peter Dayan & @doellerlab.bsky.social
www.biorxiv.org/content/10.6...
Our new paper, now published in Cell Reports, asks how the brain adaptively shapes its representations according to the statistical structure of the environment to overcome the limits of working memory capacity.
www.cell.com/cell-reports...
Efficient coding in working memory is adapted to the structure of the environment
doi.org/10.1016/j.ce...
#neuroscience
super excited to share my preprint with @meganakpeters.bsky.social stimulus familiarity shapes hierarchical structure learning and metacognitive dynamics🚀😊!!
osf.io/preprints/ps...
Stacking Variational Bayesian Monte Carlo paper in TMLR.
1/ Excited to share our new work published in Transactions on Machine Learning Research (TMLR), Stacking Variational Bayesian Monte Carlo (S-VBMC)!
New preprint: Inference over hidden contexts shapes the geometry of conceptual knowledge for flexible behaviour.
In this pre-reg study, our core claim was that we don’t just learn stimulus-reward. We infer hidden context and that inference re-wires attention and neural state space on the fly.
1/8
Our holiday celebrations are especially happy this year, with a new paper out: "Global and Local Deviance Effects in the Processing of Temporal Patterns", about how we detect and keep track of temporal regularities.
Congrats to @duniagiomo.bsky.social and the whole team!🎉
doi.org/10.1111/nyas...
We’re very happy to share that our work on 3D spatial memory was published in PNAS just before the end of the year! 🎉
Link: www.pnas.org/doi/10.1073/...
(1/8)
New preprint!
www.biorxiv.org/content/10.6...
As you are silently reading this, you may experience a little voice in your head. How is it represented in the brain, and what purpose does it serve? Our new study answers the questions.
Together with @adriendoerig.bsky.social and Radek Cichy.(1/8)
Our new preprint on promises and caveats of the extraction of M/EEG activity from ROIs is out (see more details in the reposted thread): www.biorxiv.org/content/10.6...
Huge thanks to @studenova.bsky.social, Ruben Eguinoa, Guido Nolte, @sparsity.bsky.social, Arno Villringer, and Vadim Nikulin!
🧠 New paper alert (the 1st one from our new lab)!
Led by 1st author & VR wizard @jaquent.bsky.social
@natcomms.nature.com
How do our brains distinguish novel from familiar places as we explore our environments, e.g., a new city?
🔗 doi.org/10.1038/s414...
🧵 Thread below with key findings ⬇️
New article by @jaquent.bsky.social and co:
Graded encoding of spatial novelty scales in the human brain
www.nature.com/articles/s41...
New preprint! Have you ever wondered, what are these fuzzy simplicial sets, the theoretical framework behind e.g. UMAP? Here we show that you may simply see them as marginal distributions over simplicial sets. This provides a generative model for UMAP. (1/2)
arxiv.org/abs/2512.03899
Thank you, Jacob! Appreciate it!
😂 Yeah, I was having a hard time too, squeezing everything every time. I'll send you an email!
For the neural network, the situation is different: it has no spatial priors, so the key variable is the feature similarity between tasks. By manipulating that similarity, we make the tasks more or less similar, which mirrors the similarity manipulation in the human experiment.
If the concern is that the effect might reflect only a spatial bias, I don’t think that’s the case, because the rule itself is nonlinear.
