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Meera Ray

@meeraray.bsky.social
65 followers 330 following 0 posts

Compsci PhD Student @ Penn State. Computational cognitive science, applied statistics/ML, and social critical takes on AI. Trekkie and spicy food lover.

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Reposted by Meera Ray
Jane Han @jane-han.bsky.social · 19/12/2024
🚨 New paper out with @samnastase.bsky.social and @haxbylab.bsky.social! We use representational similarity analysis to test how well behavioral, semantic, and visual models capture cortical representational geometries when viewing naturalistic action videos: doi.org/10.1101/2024...
Behaviorally-relevant features of observed actions dominate cortical representational geometry in natural vision available at bioRxiv!
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Cian O'Donnell @cianodonnell.bsky.social · 24/12/2024
Delighted @jajmca.bsky.social's abstract was accepted for COSYNE 2025, for his work on linking neural circuit structure to function via drosophila-connectome-based reservoir computing. With @conjh.bsky.social, John Wade and myself.
Schematic diagram of reservoir computing neural network, with drosophila larva connectome overlaid as reservoir connectivity.
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THiCC PI @profdancy.bsky.social · 14/12/2024
Excited for this work!
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Jessica Hullman @jessicahullman.bsky.social · 10/12/2024
I'm seeking a postdoc to work with me and @kenholstein.bsky.social on evaluating AI/ML decision support for human experts: statmodeling.stat.columbia.edu/2024/12/10/p... P.S. I'll be at NeurIPS Thurs-Mon. Happy to talk about this position or related mutual interests! Please repost 🙏
statmodeling.stat.columbia.edu
Postdoc position at Northwestern on evaluating AI/ML decision support | Statistical Modeling, Causal Inference, and Social Science
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Iris van Rooij 💭 @irisvanrooij.bsky.social · 16/08/2024
🚨Our paper `Reclaiming AI as a theoretical tool for cognitive science' is now forthcoming in the journal Computational Brain & Behaviour. (Preprint: osf.io/preprints/ps...) Below a thread summary 🧵1/n #metatheory #AGI #AIhype #cogsci #theoreticalpsych #criticalAIliteracy
The idea that human cognition is, or can be understood as, a form of computation is a useful conceptual tool for cognitive science. It was a foundational assumption during the birth of cognitive science as a multidisciplinary field, with Artificial Intelligence (AI) as one of its contributing fields. One conception of Al in this context is as a provider of computational tools (frameworks, concepts, formalisms, models, proofs, simulations, etc.) that support theory building in cognitive science. The contemporary field of Al, however, has taken the theoretical possibility of explaining human cognition as a form of computation to imply the practical feasibility of realising human(-like or -level) cognition in factual computational systems; and, the field frames this realisation as a short-term inevitability. Yet, as we formally prove herein, creating systems with human(-like or -level) cognition is intrinsically computationally intractable.
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Kevin Mitchell @wiringthebrain.bsky.social · 28/11/2024
A wonderful paper from @tonyzador.bsky.social and colleagues: Encoding innate ability through a genomic bottleneck www.pnas.org/doi/10.1073/...
pnas.org
Encoding innate ability through a genomic bottleneck | PNAS
Animals are born with extensive innate behavioral capabilities, which arise from neural circuits encoded in the genome. However, the information ca...
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Meike van der Heijden @meikeesther.bsky.social · 27/11/2024
NIH funded scientists right now.
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Anna Schapiro @annaschapiro.bsky.social · 25/11/2024
Very excited that this paper led by @cybellesmith.bsky.social is now out in JoCN!! We show how humans and recurrent neural nets can learn statistics unfolding at multiple timescales rapidly and concurrently. direct.mit.edu/jocn/article...
direct.mit.edu
Rapid Learning of Temporal Dependencies at Multiple Timescales
Abstract. Our environment contains temporal information unfolding simultaneously at multiple timescales. How do we learn and represent these dynamic and overlapping information streams? We investigate...
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Harrison Ritz @hritz.bsky.social · 18/10/2024
🎉New project 🎉 doi.org/10.1101/2024... How do we navigate between brain states when we switch tasks? Are dynamics driven by control, or passive decay of the prev task? We shed light on this classic debate by comparing high-d linear dynamical systems fit to EEGs and RNNs during task-switching 🌀
doi.org
Humans actively reconfigure neural task states
The ability to switch between different tasks is a core component of adaptive cognition, but a mechanistic understanding of this capacity has remained elusive. Longstanding questions over whether task...
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Neuroskeptic @neuroskeptic.bsky.social · 22/11/2024
You know the "🔹AI Overview" you get on Google Search? I discovered today that it's repeating as fact something I made up 7 years ago as a joke. "Kyloren syndrome" is a fictional disease I invented as part of a sting operation to prove that you can publish any nonsense in predatory journals...
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Nicole Rust @nicolecrust.bsky.social · 21/11/2024
I've been asked (re the book), "Are there bits of other fields that neuroscientists have overlooked?" Yes! Taken's theorem and work by George Sugihara. TL;DR: You can predict the future of a complex system even if you don't know all the causal variables. www.quantamagazine.org/chaos-theory...
quantamagazine.org
A Twisted Path to Equation-Free Prediction | Quanta Magazine
Complex natural systems defy analysis using a standard mathematical toolkit, so one ecologist is throwing out the equations.
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Alex Hanna @alexhanna.bsky.social · 21/11/2024
"When institutions are using AI as intended, or when institutions have undergone a seismic reformation that makes them more aligned with using AI in a certain way, what good does a demand-side proposal do?" Must-read review from Ed.
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Federico Adolfi @fedeadolfi.bsky.social · 17/11/2024
Did you know that you can ignore constant factors and a bunch of other stuff? Big-O if true
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Dr Abeba Birhane @abeba.blacksky.app · 02/08/2024
New paper where Mel Andrews, Andrew Smart, and I outline how machine learning is laundering long discredited harmful pseudoscience and offer resources for research best practices to developers/practitioners www.cell.com/patterns/ful...
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Berna Devezer @devezer.bsky.social · 14/11/2024
Finally our editorial for the "What makes a good theory?" special issue is out (co-edited with @irisvanrooij.bsky.social, @jcskewes.bsky.social, Sashank Varma, and Todd Wareham). It's an interdisciplinary issue and the contents are transdisciplinary. #philsci #metasci #cogsci #ai #psych 🧪
link.springer.com
What Makes a Good Theory? Interdisciplinary Perspectives - Computational Brain & Behavior
Computational Brain & Behavior -
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xuan (ɕɥɛn / sh-yen) @xuanalogue.bsky.social · 19/11/2024
In some recent work we extended the original assistance game formalism to handle natural language, resulting in Language-Augmented Assistance Games. We then "solve" these games using an inverse planning algorithm, inferring goals from human instructions & natural language: arxiv.org/abs/2402.17930
arxiv.org
Pragmatic Instruction Following and Goal Assistance via Cooperative Language-Guided Inverse Planning
People often give instructions whose meaning is ambiguous without further context, expecting that their actions or goals will disambiguate their intentions. How can we build assistive agents that foll...
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Alex Lew @alexlew.bsky.social · 19/11/2024
This is a very cool integration of LLMs + Bayesian methods. LLMs serve as *likelihoods*: how likely would the human be to have issued this (English) command, given a particular (symbolic) plan? No generation, just scoring :) A Bayesian agent can then resolve ambiguity in really sensible ways
Examples of the CLIPS agent from Zhi-Xuan, Ying et al. 2024 resolving ambiguity in human instructions.
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Denise J. Cai, Ph.D. @denisejcai.bsky.social · 19/11/2024
🌟 Cai Lab Nature (@natureportfolio.bsky.social) paper alert! In work led by Joe Zaki, we find that rest periods after learning not only stabilize new memories BUT ALSO integrate new memories with older ones from days past! (1/10) Read it here: www.nature.com/articles/s41...
Nature paper article with title and abstract
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Griffiths Computational Cognitive Science Lab @cocoscilab.bsky.social · 18/11/2024
(1/5) Very excited to announce the publication of Bayesian Models of Cognition: Reverse Engineering the Mind. More than a decade in the making, it's a big (600+ pages) beautiful book covering both the basics and recent work: mitpress.mit.edu/978026204941...
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Iris van Rooij 💭 @irisvanrooij.bsky.social · 18/11/2024
I do not understand why not more of my colleagues in the field of AI are worried about this. Why are they not worried about how AI hype discredits the entire field?
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Carl T. Bergstrom @carlbergstrom.com · 17/11/2024
So many people, CS researchers included, think that you can explore how an LLM works by simply asking it to tell you what it is doing or "thinking". Here @jennhu.bsky.social provides an excellent illustration of how that approach fails even at the most basic level.
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