Jonathan Nicholas

I am totally pumped about this new work . "Task-trained RNNs" are a powerful and influential framework in neuroscience, but have lacked a firm theoretical footing. This work provides one, and makes direct contact with the classical theory of random RNNs:
www.biorxiv.org/content/10.6...

Episodic memory encoding fluctuates at a theta rhythm of 3–10 Hz Nature Human Behaviour - Biba et al. show that episodic memory encoding fluctuates at a theta rhythm of 3–10 Hz.

I am excited to share my first paper, showing that episodic memory formation is theta rhythmic, is now published in Nature Human Behavior! Check it out here: rdcu.be/e6pzS. Thanks to my PI, Katherine Duncan, and to my collaborators for their support on this journey! Stay tuned for iEEG follow up 🧠

The inferred value of unchosen options spreads to related items in memory Counterfactual thinking — considering what could have come of choosing the other path — can facilitate inference. Previous studies have demonstrated t…

📢New paper out today in @cognitionjournal.bsky.social!

Does the value of an unchosen option — inferred through counterfactual reasoning — spread to related items in memory, similar to how the value of a chosen option — acquired through direct experience — does?

In short, yes!

Book cover. A silhouette of a person's head filled with colorful geometric shapes—perhaps symbolizing cognitive resources or deployment thereof. The style is attractive and modern, if generic. text: The Rational Use of Cognitive Resources Falk Lieder, Frederick Callaway, Thomas L. Griffithts

I'm excited to announce that I had my first (co-authored) book published today! "The Rational Use of Cognitive Resources" with Falk Lieder and Tom Griffiths (@cocoscilab.bsky.social ). You can read it for free! (see thread)

Thanks, and agreed, I think our findings fit squarely with your ideas!

Thanks!

Thanks, Q!

In general, our work suggests that perhaps one reason why we maintain so many seemingly unnecessary details is to construct new preferences as they are needed, allowing us to flexibly adapt to changing goals.

This latter result comes from a new experiment added since our preprint (doi.org/10.1101/2025...), where we also find that people consistently use episodic memory this way when faced with an arbitrary number of features and episodes.


The preprint is up-to-date, in case you run into a paywall!

i) people generally use episodic memories in this way

ii) they mainly do so when future task demands are uncertain

iii) they store “backup” episodes to help if irrelevant details later become relevant

iv) people with more efficient retrieval strategies make better choices

Episodic memory allows us to store individual experiences in high fidelity. Why?

One reason may be to enable flexible behavior: we can reuse whichever details from our memories become relevant for new decisions and goals.

We tested this idea and found that…

Episodic memory facilitates flexible decision-making via access to detailed events - Nature Human Behaviour Nicholas and Mattar found that people use episodic memory to make decisions when it is unclear what will be needed in the future. These findings reveal how the rich representational capacity of episod...

Our experiences have countless details, and it can be hard to know which matter.

How can we behave effectively in the future when, right now, we don't know what we'll need?

Out today in @nathumbehav.nature.com , @marcelomattar.bsky.social and I find that people solve this by using episodic memory.

Last term I tried an experiment: I walked into my Tech and Design Ethics class, admitted that I had *no idea* what to do about ChatGPT - so I would let them figure it out.

As in: their first project was to decide and write the ChatGPT policy for the class.

Here's what happened:

🚨New Paper Alert!🚨 Now out in Emotion!

The Memory Palace Architect: Effect of Valence on Loci-Dependent Recall Performance.

We ask a simple question: does the emotional tone of a memory palace matter for recall?

Turns out: yes—and negative palaces work best.

When does new learning interfere with existing knowledge in people and ANNs? Great to have this out today in @nathumbehav.nature.com

Work with @summerfieldlab.bsky.social, @tsonj.bsky.social, Lukas Braun and Jan Grohn
www.nature.com/articles/s41...

I'm recruiting PhD students for my lab at Northwestern! I'm reviewing applications for the Department of Psychology for the Cognitive Affective Neuroscience and Clinical areas, due 12/1. 🧠

Come join the CATS Lab: nucatslab.com

Learn about our latest research: iamh.northwestern.edu/research/res...

I’m excited to share my recent preprint on a neural network model of free recall that learns multiple memory strategies including the memory palace!

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

Children leverage predictive representations for flexible, value-guided choice By harnessing a mental model of how the world works, learners can make flexible choices in changing environments. However, while children and adolesce…

New paper out in cognition with @arikahn.bsky.social, @nathanieldaw.bsky.social, Cate Hartley, and @katenuss.bsky.social !!

We show that children 👶 use predictive representations (e.g. SR) to guide their choices, providing an account of how they can make flexible choices in a changing world

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...

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:

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...

New preprint from the lab! 🧠
Led by Juliana Trach, w/ Sophia Ou

Using fMRI, we discovered evidence for time-sensitive reward prediction errors (RPEs) in the human cerebellum.

Builds on, and extends, recent work in both rodents and NHPs

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...

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

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...

Thrilled to announce that I'll be starting in January 2026 as an Assistant Professor of Psychology at Hunter College, City University of New York.

The lab will study the thought processes that underlie our decision-making.

Very happy to see this work with Euan Prentis posted! If you’re going to CCN next week, go check out Euan’s poster on this work!

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)

Super excited to share this one!! Meta-learning sparsity and learning rate gives rise to brain-like gradients of complementary learning systems. So complementary learning systems emerge organically through behavior optimization, and it's not just two of them!!

the paper looks cool, excited to dig in!

Discovering cognitive strategies with tiny recurrent neural networks - Nature Modelling biological decision-making with tiny recurrent neural networks enables more accurate predictions of animal choices than classical cognitive models and offers insights into the underlying cog...

Thrilled to see our TinyRNN paper in @nature! We show how tiny RNNs predict choices of individual subjects accurately while staying fully interpretable. This approach can transform how we model cognitive processes in both healthy and disordered decisions. doi.org/10.1038/s415...

Neural evidence that humans reuse strategies to solve new tasks Humans can apply solutions used in past problems to new problems. In this study, the authors reveal the neural correlates of this process, known as generalization, and show that humans apply past poli...

One way to tackle a new task is to reuse solutions from the past. Check out Sam Hall-McMaster's latest finding that strategy reuse is accompanied by neural reactivation of prior solutions @plosbiology.org

Collab w/ M Tomov & @gershbrain.bsky.social

#neuroskyence
journals.plos.org/plosbiology/...