Thanks Sam! Main takeaways: 1) Ground-truth vs. predictive model selection differ under noisy and scarce data—for prediction, oversimplified models may work better in avoiding overfitting. 2) When humans decide between externally provided, prefitted predictive models, they're undersensitive to 1).

@gershbrain.bsky.social and I have a new paper in PLOS Comp Bio! We study how two cognitive constraints—action consideration set size & policy complexity—interact in context-dependent decision making, and how humans exploit their synergy to reduce behavioral suboptimality. osf.io/preprints/ps...

Looking forward to sharing our work at #cogsci2025! We aim at getting one step closer to a domain-general formulation of mental costs via policy compression. Come see my presentation at "Talks 35: Reasoning". It is scheduled at 16:44 PST, August 1 at Nob Hill C! gershmanlab.com/pubs/LiuGers...

This is an unusual one: the project started in 2009, with Trevor Holland collecting data in an auditory-visual integration paradigm with many conditions within subjects. But modeling it remained challenging over the years until Luigi Acerbi and Shuze Liu took it on. osf.io/preprints/ps...

@weijima.bsky.social, @lacerbi.bsky.social, Trevor, and I have a new preprint on Bayesian models of multisensory perception! We systematically inspect the underexplored degrees of freedom in Bayesian models, squeezing out their best capability in capturing human behavior. osf.io/preprints/ps...

@lucylai.bsky.social, @gershbrain.bsky.social, Bilal Bari, and I have a new paper out in JEP:G! We study the time and memory costs of policy compression—a resource-rational framework for decision making, focusing on how state-dependent our policies ought to be. psycnet.apa.org/record/2026-...

@gershbrain.bsky.social, @yangxiang.bsky.social, and I have a new project out in preprint form! osf.io/preprints/ps... Here are the main takeaways: (1/6)