If you've reached so far and find this research exciting: my lab is recruiting a postdoc and PhD students!

➡️Postdoc applications: apply.interfolio.com/170656

➡️PhD applications: as.nyu.edu/psychology/g...

10/10

Taken together, these studies further support the hypothesis that efficient compression is a fundamental principle underlying language, cognition, and intelligence more generally!

9/n

This further supports the generality of the IB principle and its applicability across the lexicon, and shows that action abstractions in language may be highly efficient

escholarship.org/uc/item/48w0...
w/ Thomas Langlois @rplevy.bsky.social @nidhise.bsky.social

8/n

We test this by considering a cross-linguistic dataset of locomotion naming 🏃‍♀️💃🚶
We find that even in this challenging dynamical multi-modal domain, systems of semantic categories across languages are significantly efficient

7/n

3️⃣ Our evidence supporting the IB framework for semantics has so far been based on static inputs like adjectives (e.g., color), nouns (e.g., objects), or function words (e.g., pronouns). Can this theory also apply to action verbs, referring to dynamic multi-modal inputs?

6/n

This suggests that bilinguals must satisfy additional constraints while operating under the same pressure for efficiency as monolinguals

escholarship.org/uc/item/4128...
w/ Maya Taliaferro, Nathaniel Imel, Esti Blanco-Elorrieta

5/n

2️⃣ Bilinguals employ two different category systems, but in practice they converge on systems that differ from monolinguals. Is this a sign bilinguals depart from efficiency to satisfy other (e.g., learnability) constraints? No, bilinguals still maintain optimality!

4/n

individual learners seem to have an inductive bias toward maintaining optimally compressed representations, even without an explicit need to communicate

escholarship.org/uc/item/63d7...
w/ Nathaniel Imel, Jennifer Culbertson, @simonkirby.bsky.social

3/n

1️⃣ We've previously shown converging evidence that semantic categories across languages achieve near-optimal compression via the Information-Bottleneck principle. But how do languages become near-optimal?
By revisiting human iterated language learning data, we find that

2/n

w/ @alisongopnik.bsky.social @norijacoby.bsky.social

#Workshop at #CogSci2025
Information Theory and Cognitive Science

🗓️ Wednesday, July 30
📍 Pacifica C - 8:30-10:00
🗣️ Noga Zaslavsky, Thomas A Langlois, Nathaniel Imel, Clara Meister, Eleonora Gualdoni, and Daniel Polani
🧑‍💻 underline.io/events/489/s...

Nathaniel Imel, Jennifer Culbertson, @simonkirby.bsky.social & @nogazs.bsky.social:
Iterated language learning is shaped by a drive for optimizing lossy compression (Talks 37: Language and Computation 3, 1 August @ 16:22; blurb below) (2/)