Constantin Rothkopf

The Reinforcement Learning workshop at U Mannheim was a lot of fun and highly recommended if you are looking for an engaging exchange of ideas, thanks to the organizers: Leif Döring, @theo-vincent.bsky.social, @claireve.bsky.social, and Simon Weißmann! www.wim.uni-mannheim.de/doering/conf...

To model the perceptual process, it is first necessary to model quantitatively how the action of pouring changes the liquid level. The physics of liquids is not trivial. This work allows simulating the liquid's behaviors in pouring, a requirement for then considering perception.

Want to model and simulate human behavior in tasks when interacting with liquids? Check out our work Niteesh Midlagajn & Constantin Rothkopf, Learning Particle Dynamics Subject to Rigid Body Manipulations Using Graph Neural Networks, presented at @logconference.bsky.social

Are you looking for a PhD in machine learning? 🧠
ELIZA is hiring a Doctoral Researcher at TU Darmstadt!

Apply by Jan 5: www.career.tu-darmstadt.de/tu-darmstadt...

ℹ️ Learn more about ELIZA: eliza.school

@tuda.bsky.social, @cs-tudarmstadt.bsky.social, @ellis.eu, @daadworldwide.bsky.social

#AI

🚀🎓We have exciting opportunities at the PhD and Postdoc level, including in the new Simons Collaboration on Ecological Neuroscience, a 10-year initiative spanning a powerful international network of labs working across species and disciplines - please reach out if interested
bsky.app/profile/simo...

Matthias Schultheis is presenting our latest work on understanding the behavior of bounded agents in more naturalistic tasks at #NeurIPS2025: What do you know? Bayesian knowledge inference for navigating agents with Jana-Sophie Schönfeld and Heinz Koeppl
neurips.cc/virtual/2025...

Tobias Niehues @tobnie.bsky.social is presenting our work with @dominikstrb.bsky.social 'Amortized Bayesian decision-making for inferring decision-making parameters from behavior' at the Amortized ProbML Workshop and the @ellis.eu UnConference. Please come by our poster!

🚀 Join @tuda.bsky.social and @hessianai.bsky.social as Professor for Ethical & Safe AI to advance the algorithmic foundations of ethical and safe AI and to shape “Reasonable AI."

Apply now and make an impact where AI meets society.
👉 buff.ly/bIvq7Mb

🗞️TIP: ProLOEWE👱🏼🧑🏼‍🦰 with Kristian Kersting+Constantin @c-rothkopf.bsky.social, who bring together modern #AI + #cognitivescience➡️@tuda.bsky.social within LOEWE-WhiteBox. Who, together with their team, have succeeded in obtaining 2 clusters of #excellence with #RAI + #TAM ➡️ proloewe.de/en/proloewe-...

Call for Applications: Fully Funded PhD Positions in the Cluster of Excellence “Reasonable Artificial Intelligence (RAI) | hessian.AI The Cluster of Excellence Reasonable Artificial Intelligence (RAI) invites applications for several fully funded PhD positions starting in early 2026. RAI conducts cutting-edge research on AI systems ...

Join our world-class #ClusterOfExcellence on Reasonable AI as a PhD student in one of the four labs. More info, particularly on the Challenging AI with Cognitive Science Lab (CAI) & application 👉 hessian.ai/call-for-app...

ELLIS PhD Program: Call for Applications 2025 A virtual info session for prospective PhD candidates will be held on October 1st with information about the whole PhD program, the call for applications, application materials, and timeline.

Only one day left to apply to the European Laboratory for Learning & Intelligent Systems (ELLIS) and work with us
@ellis.eu
ellis.eu/news/ellis-p...

If neuroscience needs behavior, then it also needs control theory ... but that's not the only reason: check out the workshop at #Bernsteinconference
bernstein-network.de/bernstein-co...

Looking forward to meeting you #ECVP2025 Mainz this week, including collaborative work with @tobnie.bsky.social @dominikstrb.bsky.social @ookenfooken.bsky.social @fatatai.bsky.social @tsawallis.bsky.social @mamassian.bsky.social @guidomaiello.bsky.social @mariaeckstein.bsky.social and many others

Upper left sketch shows the problem description the paper tackles, which is the decision-making problem that the subject needs to solve versus the inverse problem about the behavioral parameters that the researcher wants to infer. Bottom left sketch shows probabilistic graphical model of the behavior as formalized in our framework. Right panel shows the results of the paper. From top to bottom it shows example data, results of the model comparison, inferred cost functions and inferred prior beliefs of the subject. Five tasks are organized in columns by which cost function described subjects' behavior in the respective task best. We found three different cost functions, None of which are quadratic.

I'm presenting our work "Revisiting Cost Functions in Sensorimotor Decision-Making" at #CCN2025!

Stop by our poster (@dominikstrb.bsky.social‬, @c-rothkopf.bsky.social‬) and learn more about how to rethink common modeling assumptions.

📅 When: Friday, August 15, 2pm–5pm
📍 Where: De Brug, Poster C1

Happy to announce that I am presenting a poster today at #CogSci25: Physical reasoning during motor learning aids people at transferring mass, but not motor control mappings.

This is joint work with Dominik Ürüm, @mariaeckstein.bsky.social and @c-rothkopf.bsky.social

Find out more at P3-T-192!

Learning rational temporal eye movement strategies | PNAS During active behavior humans redirect their gaze several times every second within the visual environment. Where we look within static images is h...

Lots of trade offs in active visual behaviors, e.g. trading gaze switches for task performance www.pnas.org/doi/abs/10.1... or trading blinking for task performance www.pnas.org/doi/abs/10.1...

We have an open PhD position in an exciting @dfg.de - @ageinves.bsky.social project to further develop continuous psychophysics in collaboration with Joan-Lopez Moliner.

Excited to share that our paper got accepted at #ICML2025!! 🎉

We challenge Vision-Language Models like OpenAI’s o1 with Bongard problems, classic visual reasoning challenges and uncover surprising shortcomings.

Check out the paper: arxiv.org/abs/2410.19546
& read more below 👇

College of Liberal Arts | The University of Texas at Austin Liberal Arts at UT offers over 40 majors and many top-ranked graduate programs in the social sciences and humanities taught by 750 faculty.

Happy to contribute to the Natural Environments Tasks and Intelligence (NETI) workshop at UT Austin with a talk on "Computational elements of goal-directed sensorimotor behavior". You can follow the live-stream at the workshop's website liberalarts.utexas.edu/cps/neti-wor...

Very excited to be part of the Simons collaboration on ecological neuroscience @simonsfoundation.org together with this fantastic team! Theory-driven investigation of where representations for perception, cognition, and action in ecological tasks come from. POMDPs FTW. Stay tuned for job openings...

If you wanna find out how to overcome Gaussian distribution and quadratic cost assumptions in Bayesian decision-making models AND how to perform inference over their parameters, swing by our poster at #ICLR2025 in Singapore!
📅 When: Friday, April 25, 10am–12:30pm
📍 Where: Halls 3 + 2B, Poster #61

More work still to appear ...

How to infer an individual’s knowledge about the dynamics of an environment? Approximate BAMDP planning model for uncertainty over transitions & efficient replanning, as well as an approximate knowledge inference method given the behavior of an agent based on the planning model and Gibbs sampling

Straub∗, D., Schultheis∗, M., Koeppl, H., & Rothkopf, C. A. (2023). Probabilistic inverse optimal control for non-linear partially observable systems disentangles perceptual uncertainty and behavioral costs. NeurIPS.

Schultheis∗, M., Straub∗, D., & Rothkopf, C. A. (2021). Inverse Optimal Control Adapted to the Noise Characteristics of the Human Sensorimotor System. NeurIPS.

How to infer model parameters in sensorimotor control tasks? Dynamics may be stochastic and non-linear, the agent’s beliefs and controls may be unobserved, and beyond costs we may want to infer perceptual noises, beliefs, dynamics, and control-- this includes partial observations and unknown plant

M. Schultheis, C.A. Rothkopf, H. Koeppl (2022). Reinforcement learning with non-exponential discounting. NeurIPS.

We developed a theory of continuous-time model-based reinforcement learning generalized to arbitrary discount functions. This formulation covers non-exponential random termination times and includes solving the inverse problem of learning the discount function from decision data

How to model and estimate non-exponential time preferences? Commonly in reinforcement learning, rewards are discounted over time using an exponential function to model time preference. In contrast, in economics and psychology, it has been shown that humans often adopt a hyperbolic discounting scheme

Matthias was co-advised together with Heinz Koeppl.