Alana Jaskir


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I am a 4th year PhD student in Cognitive Science at Brown Univeristy’s Laboratory of Neural Computation and Cognition and am interested broadly in computational models of human learning and decision-making. I investigate how the brain learns structure and creates useful representations in order to generalize in new contexts. In my research, I aim to integrate multiple levels of analysis, for example by developing reinforcement learning algorithms (i.e. how to learn by trial-and-error) that draw from biological details of learning and memory.

I received my Bacherlor’s in Computer Science and a minor in Cognitive Science from Princeton in 2017. I also completed a Fulbright Student fellowship as an English Teaching Assistant in Rivne, Ukraine.

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Laboratory of Neural Computation and Cognition
Carney Institute for Brain Science
Department of Cognitive, Linguistic, and Psychological Sciences


Jaskir, A. & M.J. Frank. 2022 “On the normative advantages of dopamine and striatal opponency for learning and choice.” bioRxiv.

Selected Peer-Reviewed Conference Posters

Jaskir, A., L. Lehnert, M.J. Frank (2022) “Sleep’s role in analogous transfer for sequential reinforcement learning”. Winter Conference on Brain Research.

Jaskir, A. & M.J. Frank (2019) “Computational advantages of dopaminergic states for decision making.” Computational Cognitive Neuroscience Conference (CCN)

A Jaskir & Y. Niv. “Modeled learning weights predict attention and memory in a multidimensional probabilistic task.” Reinforcement Learning and Decision Making Conference (RLDM) 2017.


(Undergraduate) Jaskir, A. (2017). Learning how to learn: the interaction between attention and learning as a mechanism for dimensionality reduction in the brain. Department of Computer Science, Princeton University.