Douglas Guilbeault

We are honored to receive this recognition for our work and to see it spurring generative conversation 🙏 computational social science is increasingly poised to reveal and help us address pervasive social distortions in internet culture and AI. Thank you for your interest!

Very interesting! Thanks for sharing. I'd love to pick up on this thread when our paths cross (hopefully sooner than later!)

Thanks Laura! Very interesting comment. I agree that variance is an important and underrepresented angle in this discourse. What are some of the valuable questions and/or insights that you think can from shifting focus toward variance?

I am without the words to describe how thrilled our team is to see our paper selected for the cover of @Nature! As the tagline captures, we hope our study empowers others to see how tools in computational social science can provide critical "reality checks" for our social world! @nature.com

YouTube video by nature video Would ChatGPT hire you? Your age and gender matter

I am incredibly honored to share that the Nature News team has released this fantastic video summarizing our paper and sharing my extended interview with Shamini Bundell. Shamini did a wonderful job!

www.youtube.com/watch?v=BGT7...

It's true: The internet skews the reality of women (and men) in the workforce Age and gender biases are baked into what we see online, a large new study confirms.

Excellent coverage of our Nature paper (out today) by Mother Jones; their piece situates our findings in the broader context of evolving gender stereotypes over time and ongoing issues with biases in AI:

www.motherjones.com/media/2025/1...

How stereotypes shape AI – and what that means for the future of hiring Hear the biggest stories from the world of science | 08 October 2025

I’m delighted to appear in this week’s episode of the @nature.com podcast, where we discuss our paper and its implications for the role AI is playing in reinforcing social biases on a large scale (e.g., via the biases ChatGPT introduces into hiring processes): www.nature.com/articles/d41...

YouTube video by Douglas Guilbeault Age and Gender Distortion in Online Media and Large Language Models

The tldr of our study is captured in this short 3 minute video produced by Emma Richard:

www.youtube.com/watch?v=4Vdw...

Age and gender distortion in online media and large language models - Nature Stereotypes of age-related gender bias are socially distorted, as evidenced by the age gap in the representations of women and men across various media and algorithms, despite no systematic age differences in the workforce.

🚨I'm thrilled to share our newest paper in
@nature.com

“Age and Gender Distortion in Online Media and Large Language Models”

with my amazing collaborators Solene Delecourt and Bhargav Srinivasa Desikan!

nature.com/articles/s41...

See our 3 minute summary video in the comments😄

Emergent Meaning: Networks, AI, and the Geometry of Our Collective Mind with Douglas Guilbeault

I had a great time speaking w @douglasguilbeault.bsky.social about his research on my podcast "Yes, this". We discussed AI, LLMs, AlphaGo, social contagions, categorization, mass influence, the new science of culture, and much more. Link here: open.spotify.com/episode/18R0...

Emergent Meaning: Networks, AI, and the Geometry of Our Collective Mind with Douglas Guilbeault

I had a great time speaking w Julia Bunte-Mein
about my research on her podcast "Yes, this". We discussed AI, LLMs, AlphaGo, social contagions, categorization, mass influence, the new science of culture, and much more. Link here: open.spotify.com/episode/18R0...

Presented our work, “Cultural and Cognitive Insights from 2.6 Billion Drawings,” at @ic2s2.bsky.social this morning. Big thanks to Mauro Martino, Nima Dehmamy, @douglasguilbeault.bsky.social, @lajello.bsky.social, and @baronca.bsky.social for the collaboration ✨

Stay tuned for more results!

An interesting direction for future research will be to explore whether (and ideally how) incorporating multimodal representations into LLMs shapes their capacity to emulate human embodied metaphorical reasoning, especially about metaphors relating to vision.

Divergences in color perception between deep neural networks and humans Deep neural networks (DNNs) are increasingly proposed as models of human vision, bolstered by their impressive performance on image classification and…

This work also connects to our prior work demonstrating systematic differences in color perception btwn vision classifiers (based on deep neural networks & transformers) and humans. www.sciencedirect.com/science/arti...

Large language models without grounding recover non-sensorimotor but not sensorimotor features of human concepts - Nature Human Behaviour Xu et al. find that large language models not only align with human representations in non-sensorimotor domains but also diverge in sensorimotor ones, with additional visual training associated with e...

This work nicely complements a paper published last week showing that text-based LLMs struggle to recover sensorimotor aspects of human concepts: www.nature.com/articles/s41...

Huge kudos to Ethan Nadler for leading this effort, and for our fantastic global team of collaborators: Sofronia Ringold, Tom Williamson, Antoine Pepin, Iulia-Maria Comşa, Karim Jerbi, Srini Narayanan, and Lisa Aziz-Zadeh.

This was a massive interdisciplinary effort – including physicists, neuroscientists, social scientists, and AI researchers from @Google DeepMind.

This has implications for the ongoing conversation around whether and to what extent LLMs can be treated as meaningful emulators of human cognition for psychological and/or sociological studies.

This suggests perceptual experience plays a role in metaphorical reasoning. It further suggests that LLMs are limited in their ability to recover the embodied aspects of metaphorical reasoning from statistical correlations among words alone.

We show that LLMs’ struggle to reason coherently about novel color metaphors and are less likely to reference embodied experience in their interpretations. By contrast, painters exhibit the highest rate of embodied reasoning when interpreting novel color metaphors.

𝐃𝐨 𝐋𝐋𝐌𝐬 𝐡𝐚𝐯𝐞 𝐬𝐲𝐧𝐞𝐬𝐭𝐡𝐞𝐬𝐢𝐚? In a forthcoming paper in 𝐶𝑜𝑔𝑛𝑖𝑡𝑖𝑣𝑒 𝑆𝑐𝑖𝑒𝑛𝑐𝑒, we show yes! But this synesthesia deviates significantly from the color associations of ppl, incl. colorseeing, colorblind, and painters.

osf.io/preprints/ps...

Hyacinth's Fall Like the flower blooming from Hyacinthus's blood, something new emerges from grief. (Guitar samples by metachemist)

One of my latest choons already has 1K streams. Nice little surprise. A mournful drum and bass track in collaboration with @douglasguilbeault.bsky.social (aka metachemist). Nothing's better than getting a like or comment on SoundCloud!

Title page of paper: To Improve Literacy, Improve Equality in Education, not Large Language Models

New 💫

Letter to the Editor in Cognitive Science, by @samhforbes.bsky.social & @olivia.science

lnkd.in/eea8Cwmd

Data-Driven, Interactive Map Shows Local Economic Impact of Cuts to Federal Funding for Health Research

Researchers at Annenberg and @umdscience.bsky.social show that proposed NIH funding cuts lead to an estimated $16 billion in economic loss and 68,000 jobs lost nationwide. Learn about their project and view the interactive map here:

Thank you for your interest! We welcome any and all feedback.

We are deeply grateful to the authors of Centola & Baronchelli (2015) and Centola et al. (2018) for sharing their experimental data and for enriching discussion. We are also grateful to colleagues in the broader community who have given us helpful feedback, as highlighted in our acknowledgements.

Our framework not only provides more accurate predictions of behavioral data, but it also does so with a simple, highly interpretable mathematical model that synthesizes findings across the social sciences (from linguistics, psychology, economics, and sociology).

Our findings highlight the benefit of adopting a theoretically-driven, psychologically-grounded angle toward modeling human social learning.

Despite their many achievements, the accelerating embrace of machine learning (and LLMs specifically) as a framework for simulating human learning is likely to maintain this blind spot, since such models are designed to emulate human behavior through a brute force, statistically optimized approach.

One key takeaway in our view is that the currently in vogue optimization-based models of human cognition and behavior may systematically overlook some of the fundamentally simple and categorical processes that characterize human social learning as satisficing.