Wanna compare dynamics across different systems? See our fast and furious method developed by us (@armanbehrad.bsky.social @mmdtaha.bsky.social, @neuroprinciplist.bsky.social) together with our wonderful collaborators! You can already use our code: github.com/CMC-lab/fast...

My new lab in Singapore is seeking a full-time RA to help investigate how exploratory cognitive states shape environmental representations (cognitive maps). The position is funded until late 2027, and comes with a postdoc-level salary package. Please forward! careers.sutd.edu.sg/job/Singapor...

Proud of the work from hmc-lab.com & collaborators @ #CogSci2025 this year, but sad I cant be there myself @hanqizhou.bsky.social @davidnagy.bsky.social @alexthewitty.bsky.social @stepalminteri.bsky.social @brendenlake.bsky.social @kefang.bsky.social @rdhawkins.bsky.social @meanwhileina.bsky.social

SCMs often assume overly-dense causal graphs in dynamic settings 👉⚽🥎, since any object interaction is a potential causal edge, making them hard to scale. In joint work with @turanorujlu.bsky.social @cgumbsch.bsky.social & Martin Butz we propose a new Causal Process Model to tackle this. Thread👇

Reframing attention as an RL problem for causal discovery AI models like GNNs & Transformers can struggle with dynamic causal reasoning. Our work introduces the Causal Process Model (CPM), which reframes attention as an RL problem. Agents dynamically build sparse causal graphs.