Postdoc at MIT BCS, interested in language(s) and thought in humans and LMs
Joined March 2022
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New preprint! 🤖🧠
The cost of thinking is similar between large reasoning models and humans
👉 osf.io/preprints/psyarxiv/m2…
w/ Ferdinando D'Elia, @AndrewLampinen, and @ev_fedorenko (1/6)
Andrea de Varda retweeted
If you've noticed a distinct dialect emerging in frontier AI models and struggled to understand what they're talking about, you're not crazy!
We turned to psycholinguistics to investigate what makes the writing style so weird, and why this might be happening
🚨New preprint! 🧵
Andrea de Varda retweeted
New blog post! I wrote about why I think it's useful to use metaphorical (and anthropomorphic) terms to describe AI. I start by describing the role of metaphor in language and cognition, and how metaphors can help us to efficiently understand relations and make predictions 1/2
ALT in defense of metaphor: why metaphorical descriptions of AI are useful, even if they are sometimes imperfect. a watercolor of an MLP, with a thought bubble showing a path to a goal.
Ha, for a second I panicked that "advisors" had an irregular plural that I never learned 😅 Thanks, will take it up with STAT!
Andrea de Varda retweeted
Congrats to @devarda_a, named one of the 2026 #STATWunderkinds by @statnews!
Andrea is doing groundbreaking work in @ev_fedorenko + @roger_p_levy labs to understand how brains and machines process language.
Learn more: statnews.com/wunderkinds/and…
@mitbrainandcog @ScienceMIT
Thrilled to be one of @statnews's 2026 #STATWunderkinds for my work on language processing and reasoning in 🧠 and 🤖
Big thanks to my current/previous advisors @ev_fedorenko @roger_p_levy Marco Marelli and everyone I get to work with!
Andrea de Varda retweeted
Why do neural networks become modular? Our paper is now out in Nature Machine Intelligence! @NaturePortfolio We show that multi-task learning promotes modularity under limited capacity and task demands help explain features of brain architecture in addition to the spatial constraints.
Great thanks to all the co-authors. @WHYWYH007 @yifannnwu @DaniSBassett @marcelomattar
rdcu.be/YJ12NBxvrHsa
Check out CogGym, led by @LanceYing42! 258 experiments from 100 cogsci papers (!!) in one format. Main takeaway: models are getting more human-like, but way slower than improvements on math and coding
Have we built machines that think like humans? 🧠🤖
In a new preprint, we propose CogGym, which compares AI and human judgments across 258 commonsense-reasoning experiments from 100 cognitive-science papers.
Paper: arxiv.org/abs/2609.21259
Platform: coggym.org
ALT CogGym workflow diagram: research experiments are standardized in EML by an LLM agent with human review, then rendered for human participants and computational models. Replicated and original human data are compared with model responses in a shared analysis.
Andrea de Varda retweeted
📄👀 Check out our new survey on Eye Tracking and NLP, where we reflect on past work in this space, and outline a roadmap for what's next.
Not needless to say - it's fully human written. The humans are Lena Jäger, David Reich, Omer Shubi and me. zenodo.org/records/22933169
Andrea de Varda retweeted
📢 The CORE COGNITION lab (core-cognition.github.io) is looking to hire another PhD student, working on automated scientific discovery and knowledge representation in cognitive science.
Starting date: March to September 2027.
🙏 Please share widely!