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Reposted by Qihong (Q) Lu

Kris Jensen @kristorpjensen.bsky.social · 14d

I’m super excited to finally put my recent work with @behrenstimb.bsky.social on bioRxiv, where we develop a new mechanistic theory of how PFC structures adaptive behaviour using attractor dynamics in space and time!

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

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Hugo Spiers @hugospiers.bsky.social · 15d

www.thelancet.com/journals/lan...

New landscape of the diagnosis of Alzheimer's disease

Alzheimer's disease involves a drastic departure from the cognitive, functional, and behavioural trajectory of normal ageing, and is both a dreaded and highly prevalent cause of disability to individu...

www.thelancet.com

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Andrew Lampinen @lampinen.bsky.social · 16d

Why does AI sometimes fail to generalize, and what might help? In a new paper (arxiv.org/abs/2509.16189), we highlight the latent learning gap — which unifies findings from language modeling to agent navigation — and suggest that episodic memory complements parametric learning to bridge it. Thread:

Latent learning: episodic memory complements parametric learning by enabling flexible reuse of experiences

When do machine learning systems fail to generalize, and what mechanisms could improve their generalization? Here, we draw inspiration from cognitive science to argue that one weakness of machine lear...

arxiv.org

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Joachim Baumann @joachimbaumann.bsky.social · 26d

🚨 New paper alert 🚨 Using LLMs as data annotators, you can produce any scientific result you want. We call this **LLM Hacking**.

Paper: arxiv.org/pdf/2509.08825

$We present our new preprint titled "Large Language Model Hacking: Quantifying the Hidden Risks of Using LLMs for Text Annotation". We quantify LLM hacking risk through systematic replication of 37 diverse computational social science annotation tasks. For these tasks, we use a combined set of 2,361 realistic hypotheses that researchers might test using these annotations. Then, we collect 13 million LLM annotations across plausible LLM configurations. These annotations feed into 1.4 million regressions testing the hypotheses. For a hypothesis with no true effect (ground truth $p > 0.05$), different LLM configurations yield conflicting conclusions. Checkmarks indicate correct statistical conclusions matching ground truth; crosses indicate LLM hacking -- incorrect conclusions due to annotation errors. Across all experiments, LLM hacking occurs in 31-50\% of cases even with highly capable models. Since minor configuration changes can flip scientific conclusions, from correct to incorrect, LLM hacking can be exploited to present anything as statistically significant.$

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Brenden Lake @brendenlake.bsky.social · Sep 8

Our new lab for Human & Machine Intelligence is officially open at Princeton University!

Consider applying for a PhD or Postdoc position, either through Computer Science or Psychology. You can register interest on our new website lake-lab.github.io (1/2)

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Iris van Rooij 💭 @irisvanrooij.bsky.social · Sep 7

Cognitive scientists and AI researchers make a forceful call to reject “uncritical adoption" of AI in academia
www.bloodinthemachine.com/p/cognitive-...

Cognitive scientists and AI researchers make a forceful call to reject “uncritical adoption" of AI in academia

A new paper calls on academia to repel rampant AI in university departments and classrooms.

www.bloodinthemachine.com

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Sreejan Kumar @sreejan.bsky.social · Sep 6

I'm excited to share that my new postdoctoral position is going so well that I submitted a new paper at the end of my first week! www.biorxiv.org/content/10.1... A thread below

Sensory Compression as a Unifying Principle for Action Chunking and Time Coding in the Brain

The brain seamlessly transforms sensory information into precisely-timed movements, enabling us to type familiar words, play musical instruments, or perform complex motor routines with millisecond pre...

www.biorxiv.org

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Qihong (Q) Lu @qlu.bsky.social · Sep 5

Key-value memory network can learn to represent event memories by their causal relations to support event cognition!
Congrats to @hayoungsong.bsky.social on this exciting paper! So fun to be involved!

Hayoung Song @hayoungsong.bsky.social · Sep 5

How does the brain🧠 make causal inferences and use memories to understand narratives🎬?

We built an RNN🤖 with key-value episodic memory that learns causal relationships between events and retrieves memories like humans do!

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

w/ @qlu.bsky.social, Tan Nguyen &👇

A neural network with episodic memory learns causal relationships between narrative events

Humans reflect on past memories to make sense of an ongoing event. Past work has shown that people retrieve causally related past events during comprehension, but the exact process by which this causa...

www.biorxiv.org

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Dirk Gütlin @gutlin.bsky.social · Aug 30

Representations of stimulus features in the ventral hippocampus

🧠🟦

www.cell.com/neuron/fullt...

Representations of stimulus features in the ventral hippocampus

The ventral hippocampus (vHPC) controls emotional response to environmental cues, yet the mechanisms are unclear. Biane et al. examine how positive and negative experiences are encoded by vHPC ensembl...

www.cell.com

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Brad Wyble @bwyble.bsky.social · Aug 28

Our new study (Titled: Memory Loves Company) asks whether working memory hold more when objects belong together.

And yes, when everyday objects are paired meaningfully (Bow-Arrow), people remember them better than when they’re unrelated (Glass-Arrow). (mini thread)

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Xinchi Yu @xinchiyu.bsky.social · Jun 27

Now out in print at @jephpp.bsky.social ! doi.org/10.1037/xhp0...

Yu, X., Thakurdesai, S. P., & Xie, W. (2025). Associating everything with everything else, all at once: Semantic associations facilitate visual working memory formation for real-world objects. JEP:HPP.

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Nicole Rust @nicolecrust.bsky.social · Aug 26

Who else argues (in print) that we should eliminate the category of "emotion" as an explanatory target?

I know of Griffiths:
press.uchicago.edu/ucp/books/bo...

And Moors:
www.cambridge.org/core/books/d...

What Emotions Really Are

In this provocative contribution to the philosophy of science and mind, Paul E. Griffiths criticizes contemporary philosophy and psychology of emotion for failing to take in an evolutionary perspectiv...

press.uchicago.edu

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The Transmitter @thetransmitter.bsky.social · Aug 20

When powering fMRI studies, sample size is king, but scan duration can also be a powerful tool, improving phenotypic prediction and cost-efficiency, a new analysis shows

By @claudia-lopez.bsky.social

#neuroskyence

www.thetransmitter.org/fmri/longer-...

Longer fMRI brain scans boost reliability—but only to a point

Around 30 minutes of imaging per person seems to be the “sweet spot” for linking functional connectivity differences to traits in an accurate and cost-effective way.

www.thetransmitter.org

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Simon W Davis @woodforbrains.bsky.social · Aug 19

Cortico-hippocampal interactions underlie schema-supported memory encoding in older adults

New paper led by @shenyanghuang.bsky.social!
academic.oup.com/cercor/artic...

Older adults' memory benefits from richer semantic contexts. We found connectivity patterns supporting this semantic scaffolding.

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Mariam Aly @mariamaly.bsky.social · Aug 9

Successful prediction of the future enhances encoding of the present.

I am so delighted that this work found a wonderful home at Open Mind. The peer review journey was a rollercoaster but it *greatly* improved the paper.

direct.mit.edu/opmi/article...

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Felipe De Brigard @felipedebrigard.bsky.social · Aug 6

This was a fun paper to write, and one that fits nicely with some recent work I've been doing on the role of counterfactual simulation in memory encoding. link.springer.com/article/10.3...

Episodic details are better remembered in plausible relative to implausible counterfactual simulations - Psychonomic Bulletin & Review

People often engage in episodic counterfactual thinking, or mentally simulating how the experienced past might have been different from how it was. A commonly held view is that mentally simulating alt...

link.springer.com

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Hyunwoo Gu @hyunwoogu.bsky.social · Jul 29

Excited to share that our paper is now out in Neuron @cp-neuron.bsky.social (dlvr.it/TM9zJ8).

Our perception isn't a perfect mirror of the world. It's often biased by our expectations and beliefs. How do these biases unfold over time, and what shapes their trajectory? A summary thread. (1/13)

Attractor dynamics of working memory explain a concurrent evolution of stimulus-specific and decision-consistent biases in visual estimation

People exhibit biases when perceiving features of the world, shaped by both external stimuli and prior decisions. By tracking behavioral, neural, and mechanistic markers of stimulus- and decision-rela...

dlvr.it

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Qihong (Q) Lu @qlu.bsky.social · Jul 28

Congrats again! Cody!!

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Qihong (Q) Lu @qlu.bsky.social · Jul 28

Take a look if you are interested in the differences between LLM memory-augmentation vs human episodic memory!
And let us know if you have any feedback!

Cody Dong @codydong.bsky.social · Jul 26

My first, first author paper, comparing the properties of memory-augmented large language models and human episodic memory, out in @cp-trendscognsci.bsky.social!

authors.elsevier.com/a/1lV174sIRv...

Here’s a quick 🧵(1/n)

authors.elsevier.com

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Harrison Ritz @hritz.bsky.social · Jul 27

We put out this preprint a couple months ago, but I really wanted to replicate our findings before we went to publication.

At first, what we found was very confusing!

But when we dug in, it revealed a fascinating neural strategy for how we switch between tasks

doi.org/10.1101/2024.09.29.615736

🧵

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Oxford Population Health @oxpop.bsky.social · Jul 22

@nichols.bsky.social collaborated with researchers at the National University of Singapore on a recent study published in @nature.com on how longer duration fMRI brain scans reduce costs and improve prediction accuracy for AI models. Read more about the study below 👇

Thomas Yeo @bttyeo.bsky.social · Jul 17

1/11 Excited to share our @Naturestudy led by @leonooi.bsky.social @csabaorban.bsky.social @shaoshiz.bsky.social

AI performance is known to scale with logarithm of sample size (Kaplan 2020), but in many domains, sample size can be # participants or # measurements...

doi.org/10.1038/s415...

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Sebastian Michelmann @s-michelmann.bsky.social · Jul 19

Fantastic work by our (now former) lab manager Liv Christiano. We assess the test-retest reliability of OPM and compare it to fMRI and iEEG. 🧠📄🧵

Olivia Christiano @oliviachristiano.bsky.social · Jul 19

How reliable is OPM-MEG, and how does it compare to other neuroimaging modalities? 🤔

In a new preprint with ‪@s-michelmann.bsky.social‬, we evaluate the reliability of OPM-MEG within & between individuals, and compare it to fMRI & iEEG during repeated movie viewing. 🧠

📄 doi.org/10.1101/2025...

Reliability and signal comparison of OPM-MEG, fMRI & iEEG in a repeated movie viewing paradigm

Optically pumped magnetometers (OPMs) offer a promising advancement in noninvasive neuroimaging via magnetoencephalography (MEG), but establishing their reliability and comparability to existing metho...

doi.org

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Zhenglong Zhou @neurozz.bsky.social · Jul 16

Excited to share a new preprint w/ @annaschapiro.bsky.social! Why are there gradients of plasticity and sparsity along the neocortex–hippocampus hierarchy? We show that brain-like organization of these properties emerges in ANNs that meta-learn layer-wise plasticity and sparsity. bit.ly/4kB1yg5