🚀 We are hiring! 🚀
🔍 Join us as a Postdoctoral Researcher (fully-funded) at the Helmholtz Institute for Human-Centered AI in Munich.
03.11.2025 10:24
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🚀 We are hiring! 🚀
🔍 Join us as a Postdoctoral Researcher (fully-funded) at the Helmholtz Institute for Human-Centered AI in Munich.
What influences whether people have fun with a task?
Our paper “Leveling up fun: learning progress, expectations and success influence enjoyment in video games” with @thecharleywu.bsky.social and @ericschulz.bsky.social now in Scientific Reports!
rdcu.be/eI069
Paper summary below 1/4
Also happy to announce that our Automated scientific minimization of regret paper got accepted to the AI4Science workshop at #NeurIPS - arxiv.org/abs/2505.17661 with @marcelbinz.bsky.social, @akjagadish.bsky.social & @ericschulz.bsky.social
New in @pnas.org: doi.org/10.1073/pnas...
We study how humans explore a 61-state environment with a stochastic region that mimics a “noisy-TV.”
Results: Participants keep exploring the stochastic part even when it’s unhelpful, and novelty-seeking best explains this behavior.
#cogsci #neuroskyence
I don't know much about those fields specifically, but there are some examples www.nature.com/articles/s42..., arxiv.org/abs/2504.096..., www.sciencedirect.com/science/arti...
Happy to announce our paper got accepted to #NeurIPS!
@akjagadish.bsky.social @marvinmathony.bsky.social @ericschulz.bsky.social & Tobi Ludwig
arxiv.org/abs/2502.00879
congratulations dude!!!!! 🐣🐣🐣
Excited to see our Centaur project out in @nature.com.
TL;DR: Centaur is a computational model that predicts and simulates human behavior for any experiment described in natural language.
Does the brain learn by gradient descent?
It's a pleasure to share our paper at @cp-cell.bsky.social, showing how mice learning over long timescales display key hallmarks of gradient descent (GD).
The culmination of my PhD supervised by @laklab.bsky.social, @saxelab.bsky.social and Rafal Bogacz!
Preprint update, co-led with @akjagadish.bsky.social, with @marvinmathony.bsky.social, Tobias Ludwig and @ericschulz.bsky.social!
🚨 New in Nature Human Behavior! 🚨
Binary climate data visuals amplify perceived impact of climate change.
Both graphs in this image reflect equivalent climate change trends over time, yet people consistently perceive climate change as having a greater impact in the right plot than the left.
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We are looking for two PhD students at our institute in Munich.
Both postions are open-topic, so anything between cognitive science and machine learning is possible.
More information: hcai-munich.com/PhDHCAI.pdf
Feel free to share broadly!
hear hear
very happy to be presenting this at @cosynemeeting.bsky.social
Son-Of-A-Bitch Mouse Solves Maze Researchers Spent Months Building
Son-Of-A-Bitch Mouse Solves Maze Researchers Spent Months Building
theonion.com/son-of-...
Every experience is unique 🌟 light shifts, angles change, yet we recognize objects effortlessly. How do our minds do this? And (how) do they differ from machines? In our new preprint with @ericschulz.bsky.social, we review human generalization and compare it to machine generalization: osf.io/k6ect
About a month late posting this, but here's a new project with @ericschulz.bsky.social, @akjagadish.bsky.social, @marvinmathony.bsky.social and Tobias Ludwig
We are using LLMs to propose cognitive models in learning and decision making data. Presenting this work at RLDM!
arxiv.org/abs/2502.00879
Scatterplot titled “Empirical Evidence of Ideological Targeting in Federal Layoffs: Agencies seen as liberal are significantly more likely to face DOGE layoffs.” • The x-axis represents Perceived Ideological Leaning of federal agencies, ranging from -2 (Most Liberal) to +2 (Most Conservative), based on survey responses from over 1,500 federal executives. • The y-axis shows Agency Size (Number of Staff) on a logarithmic scale from 1,000 to 1,000,000. Each point represents a federal agency: • Red dots indicate agencies that experienced DOGE layoffs. • Gray dots indicate agencies with no layoffs. Key Observations: • Liberal-leaning agencies (left side of the plot) are disproportionately represented among red dots, indicating higher layoff rates. • Notable targeted agencies include: • HHS (Health & Human Services) • EPA (Environmental Protection Agency) • NIH (National Institutes of Health) • CFPB (Consumer Financial Protection Bureau) • Dept. of Education • USAID (U.S. Agency for International Development) • The National Nuclear Security Administration (DOE), despite its conservative leaning (+1 on the scale), is an exception among targeted agencies. • A notable outlier: the Department of Veterans Affairs (moderately conservative) also faced layoffs despite its size. Takeaway: The figure visually demonstrates that DOGE layoffs disproportionately targeted liberal-leaning agencies, supporting claims of ideological bias. The pattern reveals that layoffs were not driven by agency size or budget alone but were strongly associated with perceived ideology. Source: Richardson, Clinton, & Lewis (2018). Elite Perceptions of Agency Ideology and Workforce Skill. The Journal of Politics, 80(1).
The DOGE firings have nothing to do with “efficiency” or “cutting waste.” They’re a direct push to weaken federal agencies perceived as liberal. This was evident from the start, and now the data confirms it: targeted agencies overwhelmingly those seen as more left-leaning. 🧵⬇️
Check out our new work from Jennifer Senta, with Sonia Bishop, looking at how physiological anxiety relates to impairments in both working memory and reinforcement learning processes
www.biorxiv.org/content/10.1...
Apologies for the lack of tags for folks w Bluesky accounts, I still don’t know how this thing works, I fear my inner boomer is showing
Second preprint (with Anne Collins and Sonia Bishop) explores different anxiety-related deficits in RL and working memory:
www.biorxiv.org/content/10.1...
Not one, but TWO cool preprints by Jennifer Senta!
First preprint (with Anne Collins, Peter Dayan and Sonia Bishop) has a really cool use of modeling aimed at dissociating mechanisms underlying depression and anxiety-related phenotypes:
osf.io/preprints/ps...
In our latest article, published in @pnas.org and led by @marcelbinz.bsky.social and Stephan Alaniz, we got together four diverse groups of scientists to reflect on how LLMs should affect science. From treating them like co-authors to using other tools instead, many interesting arguments emerged.
What do you suppose they are talking about?
👏👏👏👏👏
Would it violate the ethics protocol to reveal participant’s name? 🐈
Hi new followers! 👋 My lab has lots of projects underway studying why *mental behavior* goes off the rails — leading to thinking patterns like rumination and worry 🌀 — and how we can make it more effective.
* Sketch of our approach: tinyurl.com/r3tvmbn9
* Lab website: www.translational-lab.com
Even a good advisor and a nice lab likely won't make a difference if one can't check off the 3 points above (and probably a few others, but who has time) and say 'I am fine with all of this and I can do it'.
4. A super common advice one always hears is "Find a good advisor, it is what makes or breaks a grad school experience" and "Lab culture is important". This is true. However, seems like the 3 points above are precursors to this being something that actually matters.
