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!

We fitted the data with a generative model of sliding based on statistical decision theory, and participants even seem to account for their personal variability. Subjects with higher variability undershoot more:

Statistical decision theory predicts that if people maximize their task outcome but also account for how the friction impacts the puck, they should undershoot the target more the higher the penalty but also the larger the distance. This is also what we find in the data:

Participants controlled the puck's initial velocity, to hit the green area to receive a positive score but avoid a penalty from hitting the red area. This process is subject to signal dependent noise, which is further transformed by the physics of friction, determining the final puck position.

Hello Bluesky,
with my first post, I would like to announce that my first first-author paper is out now!

Have you ever wondered whether people account for Newtonian physics when interacting with objects?

Together with @dominikstrb.bsky.social and @c-rothkopf.bsky.social we show that they do!