Christina Maher

Where you look next isn’t arbitrary.
In our new paper, we model human eye movements in immersive visual search as reinforcement learning under cognitive constraints. 🧵

Neuroscience PhD applications — Stories of WiN Dr. Nancy Padilla-Coreano chats with Dr. Ben Giasson, the director of the neuroscience PhD program at the University of Florida, to demystify the PhD application process.

It's PhD application season. If you are applying for a PhD in neuroscience or biomedical field or are mentoring someone who is applying, please check out our @storiesofwin.bsky.social episode with advice! www.storiesofwin.org/profiles/202...

A habit and working memory model as an alternative account of human reward-based learning Nature Human Behaviour - In this study, Collins proposes an alternative dual-process (working memory and habit) model of reinforcement learning in humans.

My paper is out!
Computational modeling of error patterns during reward-based learning show evidence that habit learning (value free!) supplements working memory in 7 human data sets.
rdcu.be/eQjLN

The Computational and Cognitive Neural Sciences lab (ballardlab.org) at UC Riverside is recruiting psychology PhD students to join our team! Check out the flyer to learn about the lab and our stellar research community at UCR. Apply by 12/1!
drive.google.com/file/d/19m8i...

Thrilled to be part of this fantastic new project led by @ignaciosaezphd.bsky.social and supported by @bipolardiscoveries.org. Grateful to collaborate with an outstanding team — Dr. Helen Mayberg, Dr. James Murrough and others — and looking forward to the work ahead!

📘🧠💥@fabianrenz.bsky.social , @nicoschuck.bsky.social & I thought about methods to measure brain plasticity and wrote an overview of exciting new methods and developments!

Read our chapter, out now in The Oxford Handbook of Cognitive Enhancement and Brain Plasticity academic.oup.com/edited-volum...

Neurocomputational basis of learning when choices simultaneously affect both oneself and others Nature Communications - When learning to make choices that simultaneously affect the self and others, asymmetric encoding of information guides future social behaviors across mutually beneficial,...

📢 Thrilled to share our paper is out now in @natcomms.nature.com

Shared computations underlie how we acquire actions that are mutually beneficial, instrumentally harmful (benefits self at the expense of others), altruistic (benefit others at the expense of self), or mutually costly

🧵 rdcu.be/eL8mZ

Ever slam on the brakes after seeing a speed trap? Or better yet, slow down ahead in anticipation?

In our new paper w/ @anask07.bsky.social in @cp-iscience.bsky.social, we use #iEEG to study the neural basis of reactive and proactive control in medial and lateral PFC.
tinyurl.com/4bbwbffv

Super cool, congrats!! 🕺

🚀 New Preprint from our team: comparing place cells across species!
Disentangling methods from biology provides a roadmap for cross-species insights into spatial coding 🌍
👉 www.biorxiv.org/content/10.1...

📣 Interested in applying computational methods to advance understanding of mental health? Join us for the New York Computational Psychiatry Workshop (Nov 10-12)!

Apply by Oct 13: form.jotform.com/252448781112...
More info: bit.ly/nycpw2025

Please share & RT!

Inspiring work by @danmirea.bsky.social!

🚨Exciting opportunity for trainees 🚨

Join us at @sinaiccp.bsky.social for the New York Computational Psychiatry Workshop, a 3-day course (Nov 10-12) for learning methods in computational psychiatry 🚀🧠💻

Apply by Oct 13: form.jotform.com/252448781112...

More info: bit.ly/nycpw2025

📢 My lab at the Mount Sinai School of Medicine is considering graduate student applications this fall

We welcome applicants interested in using computational modeling & fMRI to study social connection

🗓️ Deadline: December 1, 2025
🔗 Learn more: sinclaboratory.com/apply

#comppsychiatry #socialneuro

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...

📢 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

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

🚨📊ɴᴇᴡ ᴘᴀᴘᴇʀ!! When we learn a new skill—like tying shoelaces or making pasta—we often start with a series of deliberate steps. But eventually, those steps blend into a smooth, single unit: an action chunk. But why and when do we chunk? 👇
📰https://authors.elsevier.com/a/1lMMD2Hx2-9B8

Happy to announce that my lab @ Yale Psychology (actcompthink.org) will be accepting PhD applications this year (for start in Fall '26)!

Come for the fun experiments on human learning, memory, & skilled behavior, stay for the best 🍕 in the US.

Please reach out if you have any questions!

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...