Join us
Openings and how to apply
I am looking for graduate students, postdocs, and Arizona undergraduates interested in how we learn predictive models of the world, and how and when we rely on them to plan.
Below are answers to common questions. For all other questions, write to me at arikahn@arizona.edu.
Who are you recruiting?
- Prospective PhD students. Yes, I am recruiting. See how to apply below.
- Postdocs. Yes. Please write directly, with a CV and some detail on what you would want to work on or are interested in.
- Current Arizona undergraduates. Yes, if you are a current Arizona undergraduate and are interested in the work we do, you can write to me with your background and your interests.
How do I apply to work with you as a PhD student?
You must apply through the Department of Psychology, in the Cognition and Neural Systems (CNS) area, naming me as the faculty member you would like to work with. The window is October 15 to December 1, for the following fall. See the department's advice to applicants.
Do I need to email you before applying?
Neither I nor the program requires it, though you certainly may reach out ahead of time.
In particularly, I am more than happy to answer questions about whether your interests line up with what the lab is actually doing, whether you would be a good fit for the lab, and any questions about the application process. If you do write, please include a CV and some detail on what you would want to work on or are interested in.
Is this lab a good fit for me?
We approach planning and decision-making from a computational perspective.
Most students in this lab have strong coding experience and at least some quantitative background, though the exact nature of that background can vary. In addition, you should have a strong interest in cognitive science and/or neuroscience, as ultimately these methods are being used to understand how the brain implements complex behavior. Existing experience with neuroimaging (e.g. fMRI) is not required, but may be helpful depending on your research interests.
What you could work on
These are the directions the lab is actively moving in. They are not an exhaustive list, and it is possible some are out of date.
How do we build models of the world?
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Learning rules for predictive models
Candidate learning rules behave identically once over-learned — the regime they are usually studied in — but diverge trial by trial. We use reaction times to tell them apart.
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State features underlying behavior
State representations are core to planning, but how they are built from experience is unknown. With collaborators, we ask how rats segment space to support planning in navigation.
How do we choose the right model to guide decision-making?
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Predictive models across development
Adults adapt learning strategies more readily than children. Does the same hold for temporally abstract policies? Early results say children arbitrate much as adults do.
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Arbitration between strategies
What signal drives the switch? State prediction errors and on- versus off-policy inconsistency are both plausible, and we are working to dissociate them.
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Value representation and planning
How do predictive mechanisms interact with value? In ongoing collaborations we study how values of distant locations are updated and queried during reward-guided behavior.
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Disruption in pathological behavior
Adapting planning to environmental statistics is central to healthy behavior. We are asking whether that adaptation differs in patient populations.
More detail on each is on the research page.
Still have a question?
Email is the best way to reach me.
arikahn@arizona.edu