We're looking for volunteers for our #CCN2026 community event's live problem-solving demos! Join a 10min think-aloud session solving an everyday planning task or playing a video game, and help kick off our panel discussions. 🎬 🙌 Please sign up here if you're interested: forms.gle/D7HNwFE1pet9...

CCN 2026 Naturalistic Problem Solving event interest form

Please fill out this form if you're interested in attending our community event at CCN 2026! No sign-up required to join, but an RSVP helps us get a headcount. Time: Wednesday, 5th August 2026, 16:0...

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Join us at both conferences this summer! Our #CogSci2026 workshop features a series of invited talks on problem representations and abstractions. Our #CCN2026 community event opens a broader discussion on naturalistic problem solving: frameworks, tasks, methods, and where the field should go next.

Do people solve hard problems by first building a mental model, then searching for the best solution? With @gershbrain.bsky.social, our new manuscript suggests a different route: people often start by retrieving cached solutions, and scaffold representations around them. osf.io/preprints/ps...

Join us at both conferences this summer! Our #CogSci2026 workshop features a series of invited talks on problem representations and abstractions. Our #CCN2026 community event opens a broader discussion on naturalistic problem solving: frameworks, tasks, methods, and where the field should go next.

Mark Ho@markkho.bsky.social · 3mo ago

How do human minds make sense of big, messy problems? 😵‍💫🌀 How do we distill complexity into something simple enough to solve? 🤔💡 We’ll be tackling these questions (and more!) at two workshops on task representations, abstractions, and construals #CogSci2026 #CCN2026 🧵 framing-the-problem.github.io

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).

Sam Gershman@gershbrain.bsky.social · 8mo ago

New work from @liushuze.bsky.social and @yangxiang.bsky.social osf.io/preprints/ps... People violate Occam's razor when selecting between predictive models. This is surprising given past research (including my own) showing a preference for simplicity.

@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)