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Reposted by Ellie Holton

Dan Mirea @danmirea.bsky.social · 10d

🚨Out now in @cp-trendscognsci.bsky.social 🚨

We explore the use of cognitive theories/models with real-world data for understanding mental health.

We review emerging studies and discuss challenges and opportunities of this approach.

With @yaelniv.bsky.social and @eriknook.bsky.social

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annecollins.bsky.social @annecollins.bsky.social · Sep 9

Check out @gaiamolinaro.bsky.social 's new paper!

Gaia Molinaro @gaiamolinaro.bsky.social · Sep 9

📢 New preprint!
How do humans learn from arbitrary, abstract goals? We show that, when goal spaces can be compressed, costly working-memory processes give way to internalized reward functions, enabling efficient goal-dependent reinforcement learning. @annecollins.bsky.social arxiv.org/abs/2509.06810

Reward function compression facilitates goal-dependent reinforcement learning

Reinforcement learning agents learn from rewards, but humans can uniquely assign value to novel, abstract outcomes in a goal-dependent manner. However, this flexibility is cognitively costly, making l...

arxiv.org

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annecollins.bsky.social @annecollins.bsky.social · Aug 29

Check out @tifenpan.bsky.social 's just published paper! we demonstrate how to use RNNs to infer latent variables from cognitive models, even when standard methods don't work easily.

Ti-Fen Pan @tifenpan.bsky.social · Aug 29

New paper out in Behavioral Research Methods! We introduce a simulation-based method using RNNs to infer trial-varying latent variables from computational cognitive models.
Link: doi.org/10.3758/s134...
#ComputationalCognitiveModeling #SBI

Latent variable sequence identification for cognitive models with neural network estimators - Behavior Research Methods

Extracting time-varying latent variables from computational cognitive models plays a key role in uncovering the dynamic cognitive processes that drive behaviors. However, existing methods are limited ...

doi.org

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Bonan Zhao (赵博囡) @bonan.bsky.social · Aug 17

My Lab at the University of Edinburgh🇬🇧 has funded PhD positions for this cycle!

We study the computational principles of how people learn, reason, and communicate.

It's a new lab, and you will be playing a big role in shaping its culture and foundations.

Spread the words!

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Jess Thompson @tsonj.bsky.social · Aug 9

New preprint by William D'Alessandro and myself:

The promise and peril of AI surrogacy in psychological research

osf.io/preprints/ps...

OSF

osf.io

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Denis Lan @denislan.bsky.social · Aug 7

My first PhD paper - with @lhuntneuro.bsky.social and @summerfieldlab.bsky.social - is now out in @plosbiology.org! We ask: how do humans (and deep neural networks) navigate flexibly even in unfamiliar environments, such as a new city? Link: plos.io/45uSwNm 🧵 (1/6)

Cartoon image of me looking at a map, with a stadium behind me and a hotel and ferris wheel across the river in the background. I am thinking about going to the ferris wheel

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Qihong (Q) Lu @qlu.bsky.social · May 8

I’m thrilled to announce that I will start as a presidential assistant professor in Neuroscience at the City U of Hong Kong in Jan 2026!
I have RA, PhD, and postdoc positions available! Come work with me on neural network models + experiments on human memory!
RT appreciated!
(1/5)

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Nico Schuck @nicoschuck.bsky.social · Mar 19

Does Sleeping on a idea work? Nice to see our work led by the amazing @anikaloewe.bsky.social and @maritpetzka.bsky.social featured together with other studies in the WaPo:

www.washingtonpost.com/wellness/202...

Our preprint www.biorxiv.org/content/10.1...

Does sleeping on an idea work? Here’s what science says.

Scientists are finding experimental evidence that the transition between wakefulness and sleep is a portal for creative thought.

www.washingtonpost.com

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Elliott Wimmer @elliottwimmer.bsky.social · Mar 19

We are excited to post a new preprint with Shiyi Liang @shiyiliang.bsky.social:

'Reinforcement learning is positively associated with anhedonia symptoms' osf.io/preprints/ps...
(a bit late here – a version was online back in December)

@mpc-comppsych.bsky.social

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osf.io

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Kate Nussenbaum @katenuss.bsky.social · Mar 6

New preprint 📝 - another fun collaboration with @arikahn.bsky.social, @licezhang.bsky.social, @nathanieldaw.bsky.social, @hartleylabnyu.bsky.social

We ask: Why do children and adults often derive different representations of their environments from the same experiences? 🧠👶🔎

osf.io/preprints/ps...

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osf.io

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Levi Kumle @levikumle.bsky.social · Feb 28

Excited to see this latest paper from my PhD out! Huge thanks to everyone who contributed!

Adaptive Behaviour & Cognition Lab @abclab.bsky.social · Feb 28

Newest from the lab out now! ☀️

We explored how long-term memory contributes to naturalistic memory-guided behaviour – not because we have to, but because we choose to.

Find out more: www.cell.com/current-biol...

Long-term memory facilitates spontaneous memory usage through multiple pathways

Many natural behaviors require the flexible coordination of sampling sensory information from the external environment and the use of information from memory. Kumle et al. show that long-term memory s...

www.cell.com

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Ellie Holton @eleanor-holton.bsky.social · Feb 25

Thanks Mirela!!

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Ellie Holton @eleanor-holton.bsky.social · Feb 24

Thanks!!

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Ellie Holton @eleanor-holton.bsky.social · Feb 24

Ah yes true, they would probably relearn Rule A with feedback but I expect they would retain good generalisation (to untrained test examples from the same task) unlike the splitter group

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Ellie Holton @eleanor-holton.bsky.social · Feb 24

Thanks! Great question, our task isn’t long enough to tell but I’d love to know this too - my prediction would be that more splitters would become lumpers (ie learn to generalise with time and/or sleep like “grokking”) but very interested if you’d predict otherwise!

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Ellie Holton @eleanor-holton.bsky.social · Feb 24

Our work bridges psychology & AI, showing that continual learning is shaped by how knowledge is organised, with shared trade-offs across both systems.

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Ellie Holton @eleanor-holton.bsky.social · Feb 24

We could capture this mixture of behaviour by tweaking the training regime of ANNs, (‘rich’ vs. ‘lazy’) shifting them towards shared representations (enabling transfer/generalisation but at the cost of interference) versus separated representations. (7/8)

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Ellie Holton @eleanor-holton.bsky.social · Feb 24

While humans learning similar tasks showed more interference than those learning dissimilar tasks, this wasn’t the case for everyone. Some people avoided interference, but they were also worse at transfer to new tasks & at generalisation within a task! (6/8)

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Ellie Holton @eleanor-holton.bsky.social · Feb 24

In ANNs, this can be explained by whether tasks share solutions. Similar tasks were learned by adapting existing representations which are corrupted in the process. Dissimilar tasks were learned as orthogonal representations, reducing interference. (5/8)

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Ellie Holton @eleanor-holton.bsky.social · Feb 24

When Task A & B were similar (‘Near’), both humans & ANNs learned faster—but at a cost. Greater transfer across tasks led to higher interference compared to learning dissimilar tasks (‘Far’) (4/8)

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Ellie Holton @eleanor-holton.bsky.social · Feb 24

We taught humans and ANNs two sequential rule-learning tasks (Task A then Task B), and then re-tested their knowledge of the first (Task A). We studied how patterns of transfer to Task B, and interference on return to Task A, differed as a function of task similarity (3/8)

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Ellie Holton @eleanor-holton.bsky.social · Feb 24

Artificial neural networks often struggle to learn new tasks without overwriting previous ones, while humans seamlessly integrate new knowledge throughout life. But could the principles governing continual learning in both systems be more alike than we think? (2/8)

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Ellie Holton @eleanor-holton.bsky.social · Feb 24

New preprint out with @summerfieldlab.bsky.social! When does new learning interfere with existing knowledge? We compare continual learning in humans and artificial neural networks, revealing similar patterns of transfer & catastrophic interference (1/8) osf.io/preprints/ps...

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