Andrea de Varda

Happy to share that our paper “Mixture of Cognitive Reasoners: Modular Reasoning with Brain-Like Specialization” (aka MiCRo) has been accepted to #ICLR2026!! 🎉

See you in Rio 🇧🇷 🏝️

Bridge AI and linguistics with the Computational and Theoretical Modelling of Language and Cognition (CLC) track at @cimecunitrento.bsky.social!
Apply to our MSc in Cognitive Science
First-call deadline for non-EU applicants: March 4, 2026.

ℹ️ corsi.unitn.it/en/cognitive-science
#cimec_unitrento #AI

Semantic reasoning takes place largely outside the language network The brain's language network is often implicated in the representation and manipulation of abstract semantic knowledge. However, this view is inconsistent with a large body of evidence suggesting that...

The last chapter of my PhD (expanded) is finally out as a preprint!

“Semantic reasoning takes place largely outside the language network” 🧠🧐

www.biorxiv.org/content/10.6...

What is semantic reasoning? Read on! 🧵👇

In collaboration with @tomlamarra.bsky.social Andrea Amelio Ravelli @chiarasaponaro.bsky.social @beatricegiustolisi.bsky.social @mariannabolog.bsky.social

Some words sound like what they mean. In IconicITA we show that the (psycho)linguistic factors that modulate which words are most iconic are similar between English and Italian. Lots more details in the paper!

Great work led by Daria & Greta showing that diverse agreement types draw on shared units (even across languages)!

What does it mean to understand language? Language understanding entails not just extracting the surface-level meaning of the linguistic input, but constructing rich mental models of the situation it describes. Here we propose that because pr...

What does it mean to understand language? We argue that the brain’s core language system is limited, and that *deeply* understanding language requires EXPORTING info to other brain regions.
w/ @neuranna.bsky.social @evfedorenko.bsky.social @nancykanwisher.bsky.social
arxiv.org/abs/2511.19757
1/n🧵👇

I'd love to watch this, is there a recording?

Computational psycho/neurolinguistics is lots of fun, but most studies only focus on English. If you think cross-linguistic evidence matters for understanding the language system, consider submitting an abstract to MMMM 2026!

Why does this alignment emerge? There are similarities in how reasoning models and humans learn: first by observing worked examples (pretraining), then by practicing with feedback (RL). In the end, just like humans, they allocate more effort to harder problems. (6/6)

Token count also captures differences across tasks. Avg. token count predicts avg. RT across domains (r = 0.97, left), and even item-level RTs across all tasks (r = 0.92 (!!), right). (5/6)

We found that the number of reasoning tokens generated by the model reliably correlates with human RTs within each task (mean r = 0.57, all ps < .001). (4/6)

Large reasoning models can solve many reasoning problems, but do their computations reflect how humans think?
We compared human RTs to DeepSeek-R1’s CoT length across seven tasks: arithmetic (numeric & verbal), logic (syllogisms & ALE), relational reasoning, intuitive reasoning, and ARC (3/6)

Neural networks are powerful in-silico models for studying cognition: LLMs and CNNs already capture key behaviors in language and vision. But can they also capture the cognitive demands of human reasoning? (2/6)

Our paper “The cost of thinking is similar between large reasoning models and humans” is now out in PNAS! 🤖🧠
w/ @fepdelia.bsky.social, @hopekean.bsky.social, @lampinen.bsky.social, and @evfedorenko.bsky.social
Link: www.pnas.org/doi/10.1073/... (1/6)

Top: A syntax tree for the sentence "the doctor by the lawyer saw the artist". Bottom: A continuous vector.

🤖🧠I'll be considering applications for PhD students & postdocs to start at Yale in Fall 2026!

If you are interested in the intersection of linguistics, cognitive science, & AI, I encourage you to apply!

PhD link: rtmccoy.com/prospective_...
Postdoc link: rtmccoy.com/prospective_...

Human-like fleeting memory improves language learning but impairs reading time prediction in transformer language models Human memory is fleeting. As words are processed, the exact wordforms that make up incoming sentences are rapidly lost. Cognitive scientists have long believed that this limitation of memory may, para...

New preprint! w/@drhanjones.bsky.social

Adding human-like memory limitations to transformers improves language learning, but impairs reading time prediction

This supports ideas from cognitive science but complicates the link between architecture and behavioural prediction
arxiv.org/abs/2508.05803

Can't wait for #CCN2025! Drop by to say hi to me / collaborators!

Evidence from Formal Logical Reasoning Reveals that the Language of Thought is not Natural Language Humans are endowed with a powerful capacity for both inductive and deductive logical thought: we easily form generalizations based on a few examples and draw conclusions from known premises. Humans al...

Is the Language of Thought == Language? A Thread 🧵
New Preprint (link: tinyurl.com/LangLOT) with @alexanderfung.bsky.social, Paris Jaggers, Jason Chen, Josh Rule, Yael Benn, @joshtenenbaum.bsky.social, ‪@spiantado.bsky.social‬, Rosemary Varley, @evfedorenko.bsky.social
1/8

The BLiMP-NL dataset consists of 84 Dutch minimal pair paradigms covering 22 syntactic phenomena, and comes with graded human acceptability ratings & self-paced reading times. An example minimal pair: A. Ik bekijk de foto van mezelf in de kamer (I watch the photograph of myself in the room; grammatical) B. Wij bekijken de foto van mezelf in de kamer (We watch the photograph of myself in the room; ungrammatical) Differences in human acceptability ratings between sentences correlate with differences in model syntactic log-odds ratio scores.

Next week I’ll be in Vienna for my first *ACL conference! 🇦🇹✨

I will present our new BLiMP-NL dataset for evaluating language models on Dutch syntactic minimal pairs and human acceptability judgments ⬇️

🗓️ Tuesday, July 29th, 16:00-17:30, Hall X4 / X5 (Austria Center Vienna)

I'm sharing a Colab notebook on using large language models for cognitive science! GitHub repo: github.com/MarcoCiappar...

It's geared toward psychologists & linguists and covers extracting embeddings, predictability measures, comparing models across languages & modalities (vision). see examples 🧵

Cracking arbitrariness: A data-driven study of auditory iconicity in spoken English - Psychonomic Bulletin & Review Auditory iconic words display a phonological profile that imitates their referents’ sounds. Traditionally, those words are thought to constitute a minor portion of the auditory lexicon. In this articl...

📢 New paper out! We show that auditory iconicity is not marginal in English: word sounds often resemble real-world sounds. Using neural networks and sound similarity measures, we crack the myth of arbitrariness.
Read more: link.springer.com/article/10.3...

@andreadevarda.bsky.social

Many LM applications may be formulated as text generation conditional on some (Boolean) constraint.

Generate a…
- Python program that passes a test suite.
- PDDL plan that satisfies a goal.
- CoT trajectory that yields a positive reward.
The list goes on…

How can we efficiently satisfy these? 🧵👇

The cerebellar components of the human language network The cerebellum's capacity for neural computation is arguably unmatched. Yet despite evidence of cerebellar contributions to cognition, including language, its precise role remains debated. Here, we sy...

New paper! 🧠 **The cerebellar components of the human language network**

with: @hsmall.bsky.social @moshepoliak.bsky.social @gretatuckute.bsky.social @benlipkin.bsky.social @awolna.bsky.social @aniladmello.bsky.social and @evfedorenko.bsky.social

www.biorxiv.org/content/10.1...

1/n 🧵

PINEAPPLE, LIGHT, HAPPY, AVALANCHE, BURDEN

Some of these words are consistently remembered better than others. Why is that?
In our paper, just published in J. Exp. Psychol., we provide a simple Bayesian account and show that it explains >80% of variance in word memorability: tinyurl.com/yf3md5aj

The extended language network: Language selective brain areas whose contributions to language remain to be discovered Although language neuroscience has largely focused on core left frontal and temporal brain areas and their right-hemisphere homotopes, numerous other areas - cortical, subcortical, and cerebellar - ha...

Excited to share new work on the language system!

Using a large fMRI dataset (n=772) we comprehensively search for language-selective regions across the brain. w/
Aaron Wright, @benlipkin.bsky.social, and @evfedorenko.bsky.social

Link to the preprint: biorxiv.org/content/10.1...
Thread below!👇🧵

A language network in the individualized functional connectomes of over 1,000 human brains doing arbitrary tasks A century and a half of neuroscience has yielded many divergent theories of the neurobiology of language. Two factors that likely contribute to this situation include (a) conceptual disagreement…

New brain/language study w/ @evfedorenko.bsky.social! We applied task-agnostic individualized functional connectomics (iFC) to the entire history of fMRI scanning in the Fedorenko lab, parcellating nearly 1200 brains into networks based on activity fluctuations alone. doi.org/10.1101/2025... . 🧵

Conceptual Combination in Large Language Models: Uncovering Implicit Relational Interpretations in Compound Words With Contextualized Word Embeddings Large language models (LLMs) have been proposed as candidate models of human semantics, and as such, they must be able to account for conceptual combination. This work explores the ability of two LLM...

1/n Happy to share a new paper with Calogero Zarbo & Marco Marelli! How well do LLMs represent the implicit meaning of familiar and novel compounds? How do they compare with simpler distributional semantics models (DSMs; i.e., word embeddings)?
doi.org/10.1111/cogs...

To the brain, Esperanto and Klingon appear the same as English or Mandarin MIT research finds the brain’s language-processing network also responds to artificial languages such as Esperanto and languages made for TV, such as Klingon on “Star Trek” and High Valyrian and Dothr...

So excited to have our work on conlangs out in PNAS: www.pnas.org/doi/10.1073/... Congrats, Saima, Maya, and the rest of the crew -- well done!
Here is the MIT news story:
news.mit.edu/2025/esperan...

New preprint w/ @jennhu.bsky.social @kmahowald.bsky.social : Can LLMs introspect about their knowledge of language?
Across models and domains, we did not find evidence that LLMs have privileged access to their own predictions. 🧵(1/8)