Thrilled to see our TinyRNN paper in @nature! We show how tiny RNNs predict choices of individual subjects accurately while staying fully interpretable. This approach can transform how we model cognitive processes in both healthy and disordered decisions. doi.org/10.1038/s415...

Despite the world being on fire, I can't help but be thrilled to announce that I'll be starting as an Assistant Professor in the Cognitive Science Program at Dartmouth in Fall '26. I'll be recruiting grad students this upcoming cycle—get in touch if you're interested!

yesterday, my postdoc funding (salary and research funds) was cancelled by the National Science Foundation, effective immediately. I received the same generic, vaguely threatening, typo-ridden email as many of my colleagues who have had their awards terminated recently. (1/n)

You already know the Ship of Theseus, but do you also know the Ship of Thesis? It was constructed out of 3 loosely related papers which tell the tales of heroic quests and mythic psychodrama. It was only constructed once and then allowed to sink into oblivion.

One neat direction I've heard of is @psychboyh.bsky.social & Robert Wilson's work using LLMs to process think-aloud data. 2024.ccneuro.org/pdf/67_Paper...

Shout out to my incredible co-authors: Sophia Sanborn (@naturecomputes.bsky.social), Mark Ho (@markkho.bsky.social), Fred Callaway (@fredcallaway.bsky.social), Nathaniel Daw (@nathanieldaw.bsky.social), and Tom Griffiths (@cocoscilab.bsky.social).

Check out our preprint thread for more details!

bsky.app/profile/did:...

My paper on hierarchical plans is out in Cognition!🎉

tldr: We ask participants to generate hierarchical plans in a programming game. People prefer to reuse beyond what standard accounts predict, which we formalize as induction of a grammar over actions.

authors.elsevier.com/a/1kBQr2Hx2x...

Lightbot was originally built by Danny Yaroslavski. We build on an open-source version by Laurent Haan (github.com/haan/Lightbot).

Animation in first post was inspired by M. C. Escher’s Ascending and Descending and the game Monument Valley.

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Huge thanks to my incredible co-authors: Sophia Sanborn, @markkho.bsky.social, @fredcallaway.bsky.social, @nathanieldaw.bsky.social, & @cocoscilab.bsky.social. I am incredibly grateful for their insights and support!

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For more details, check out the paper!

Preprint: arxiv.org/abs/2311.18644

Playable demo: carlos.correa.me/cocosci-ligh...

Experiment code: github.com/cgc/cocosci-...

Analysis code: github.com/cgc/lightbot...

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We find that people have a bias towards reuse, beyond the reuse that naturally occurs when minimizing program length. Drawing from theories of word learning, we account for this by modeling participants' program-writing as if they were creating & using an action grammar.

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Importantly, a program isn't just a sequence of instructions. Participants can define and use parts of programs (called processes), making it possible to write shorter, more compact programs.

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We use a process-tracing paradigm where people create hierarchical plans. Based on the educational game Lightbot, research participants drag and drop instructions to write programs. Lightbot follows these instructions, with the goal of activating all lights.

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Note: This thread is a repost from X (formerly twitter): twitter.com/_cgcorrea/st... The thread over there has several animations, which are only stills here.

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Human behavior is hierarchically structured. But what determines *which* hierarchies people use? In a preprint, we run an experiment where people create programs that correspond to hierarchies, finding that people prefer structures with more reuse.

arxiv.org/abs/2311.18644

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