Sebastian Michelmann

Disrupted Sleep: Mechanisms Linking Sleep Deprivation, Neurovascular Dysfunction, and Metabolic Pathways at Leeds Beckett University on FindAPhD.com PhD Project - Disrupted Sleep: Mechanisms Linking Sleep Deprivation, Neurovascular Dysfunction, and Metabolic Pathways at Leeds Beckett University, listed on FindAPhD.com

Please repost! Fully funded four-year PhD studentship opportunity on sleep deprivation and neurovascular dysfunction on the BBSRC Yorkshire Bioscience Doctoral Training Partnership, including annual stipend, research costs and home tuition fees tinyurl.com/ms7v2pcx

🚨 New preprint!
One idea, many ways to say it – but does your brain track those options while you speak?
Using LLMs, we put this to the test.
www.biorxiv.org/content/10.1...
We show for the 1st time that the brain represents multiple alternatives simultaneously in both listening and speaking.
🧵

Unfolding event structure distorts subjective time Our experience of time is often distorted in striking ways. Although prior work has shown that boundaries between events can shape temporal perception…

Our experience of time is powerfully shaped by boundaries between events (i.e., going from one meeting to the next). But what about time *within an event*? In new work, we find reliable distortions of time based on internal event structure (e.g., beginnings, middles, and ends)! tinyurl.com/n8mn2sn7

I’m excited to share my recent preprint on a neural network model of free recall that learns multiple memory strategies including the memory palace!

www.biorxiv.org/content/10.1...

We’re hiring!

@sucholutsky.bsky.social and I are seeking a postdoc and RA for a project on trust in AI systems with folks at NYU, Princeton, BU, and Cornell

Positions open until filled. Apply soon! Please share 🔁

postdoc: apply.interfolio.com/175495

RA: apply.interfolio.com/175497

Does AI improve or undercut academic scholarship?

A new study finds that academics who use AI increased both the quantity & quality of their academic scholarship and it appears to reduces inequality (helping junior and non-english speaking scholars the most).
arxiv.org/pdf/2510.02408

CDS’ Grace Lindsay Launches YouTube Channel on “AI for the Planet” Grace Lindsay’s new YouTube series turns climate-AI research into five-minute videos for a wide audience.

CDS Asst. Prof. @neurograce.bsky.social has launched a YouTube channel, “5 Minute Papers on AI for the Planet,” translating climate-AI research into short video explainers, inspired by her course at CDS, “Machine Learning for Climate Change.”

nyudatascience.medium.com/cds-grace-li...

I don't know that it works perfectly, but I have to say that the Asta search tool from @ai2.bsky.social is exactly what I want from an AI-powered research search tool for scientists: Describe a style of experiment or work and see if there are papers that have done that.
asta.allen.ai/chat

The dependence of children’s generalization on episodic memory varies with age and level of abstraction - Nature Communications Children’s ability to generalize from episodic memories varies by both age and the level of abstraction. Here, the authors show that lower level generalization increasingly depends on episodic memory with age, whereas higher level generalization shows no such relationship.

Thrilled to see this paper out! It's the culmination of a project begun in the depths of the pandemic with Sabrina Karjack and @zoengo.bsky.social . We continue our exploration of how children generalize when their episodic memory is not yet mature.
www.nature.com/articles/s41...

If you're interested in the cognitive neuroscience of memory feel free to email me!

I do experimental psychology, brain imaging (fMRI and MEG) and a bit of modelling. Lab is doing stuff on forgetting, aging, schemas, and event boundaries, but we're not limited to that.

#psychscisky #neuroskyence

I'm recruiting grad students!! 🎓

The CoDec Lab @ NYU (codec-lab.github.io) is looking for PhD students (Fall 2026) interested in computational approaches to social cognition & problem solving 🧠

Applications through Psych (tinyurl.com/nyucp) are due Dec 1. Reach out with Qs & please repost! 🙏

Principles for proper peer review

doi.org/10.21428/8e6...

Job alert!🚨
Join us @uab.cat to investigate human memory representations with intracranial recordings, eye-tracking, immersive VR and deep learning. This is a fully funded, four-year PhD position at the Prediction and Memory Lab.
Feel free to reach out if you have any questions!

I'm recruiting PhD students to join my new lab in Fall 2026! The Shared Minds Lab at @usc.edu will combine deep learning and ecological human neuroscience to better understand how we communicate our thoughts from one brain to another.

Careers at Drexel - Human Resources

The MAC lab at Drexel is looking for a new post-doc to work on NIH-funded projects investigating the intersection of prior knowledge and long-term memory consolidation. Please pass along to any interested lab members! careers.drexel.edu/cw/en-us/job...

Congratulations, Josh!!! 🎉🎊🙌

8x8 grid depicting the approach to stimulus creation. Feature pairs are on the axes and images are in the cells. The x-axis represents the high-level feature pairs: setting (green) and object (teal). For example, the first column of images all depict “truck” (object) in “field” (setting) rendered in various textures and patterns. The y-axis represents low-level feature pairs: texture (blue) and pattern (purple). For example, the first row of images all depict different objects and settings rendered as if drawn with crayon (texture) and containing large horizontal edges (pattern).

Excited to release the SPOT grid: a new image set that factorially crosses scene-object & texture-pattern pairings.

We hope these stimuli will be useful to researchers aiming to (partially) disentangle the contributions of lower- and higher-level visual features to behavior & brain activity.

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Assistant Professor - Cognitive Sciences University of California, Irvine is hiring. Apply now!

Come work with us! UC Irvine Cognitive Sciences is looking for a new Assistant Professor to join our team: recruit.ap.uci.edu/JPF09896

I'm not on the committee, but happy to talk if you're interested.

(end) Regarding (4), we already have a discussion point that speaks to this. You can constrain Dxy to a value other than 0, so if there is a known correlation r in your data, you can define your constraining space as wx’*Dxy*wy = r

Tensor Canonical Correlation Analysis for Multi-view Dimension Reduction Canonical correlation analysis (CCA) has proven an effective tool for two-view dimension reduction due to its profound theoretical foundation and success in practical applications. In respect of multi...

(1) Hi @ar0mcintosh.bsky.social I think a tensor version of CRM should be feasible; similar work has been done with CCA (arxiv.org/abs/1502.02330). @schottdorflab.bsky.social what are your thoughts on this?

(7) I would say that partial correlation is a closer analogy to CRM than semi-partial because we find weights for both sides. Technically, CRM is rather steering the solution away from variance shared with the confound than regressing it out.

(6) It is true that CRM would find a lower bound of maximal correlation because the max includes the confound and should be higher.

(5) There are many other situations where the strongest correlations are *not* of interest. E.g., ERPs have typical shapes; if you wanted to find a condition specific waveform, you could estimate Cxy between ERPs from the same condition and compute Dxy from ERPs belonging to different conditions.

(4) Another straightforward choice for Dxy is to recompute the same cross-covariance matrix as in Cxy but on data bandpass filtered around the line-noise band (we use this in example 3 in the paper).

(3) The key to using CRM is to find a useful Dxy. This can be different data (e.g., from a baseline period), or the same pseudo-randomized data where only the association of interest is eliminated.

(2) CRM is different because it adds the constraint that the projected cross-covariance wx’*Dxy*wy should remain zero. The motivating idea is that not all correlations are of interest, so if we can narrow down what the noise/confound signal looks like, we can meaningfully restrict our optimization.

(1) regarding the relationship to PLS: my understanding is that PLS and CCA can be viewed as very similar optimization problems and even as the same problem if the variance of the projected data is 1. Agoston Mihalik (Mihalik et al. 2022) has a great comparison of CCA vs. PLS.

Hi @hritz.bsky.social, @ar0mcintosh.bsky.social
, @pascualmarqui.bsky.social ,and @martinhebart.bsky.social
Thank you all for your interest and for these great comments! I will try to answer your questions below:

Our latest project find shared representations while controlling for confounds is out www.biorxiv.org/content/10.1... Check @s-michelmann.bsky.social 's thread for the executive summary. Code in python and matlab: github.com/s-michelmann... — Now is play time 👨‍💻

Canonical Representational Mapping for Cognitive Neuroscience https://www.biorxiv.org/content/10.1101/2025.09.01.673485v1