Alexander Ku

I'm a PhD student in Psychology and Neuroscience at Princeton, where I work with Tom Griffiths and Jon Cohen, and also a Research Scientist at Google DeepMind. Before this, I received my BA and MS in Computer Science from UC Berkeley.

My research focuses on how neural systems balance flexibility and efficiency in adapting to complex, dynamic environments. Central to this balance is the way information about the external world is internally represented. This includes examining the computational costs associated with maintaining and processing different types of representations, the mechanisms by which representations are transformed or consolidated over time, and how these processes are optimized across multiple timescales. The goal is to better understand how representational formats support adaptive cognition and behavior, and to use these insights to guide the development of artificial systems that can adapt to continual change.

Keywords: Continual Learning, Meta-Learning, Cognitive Control, Automaticity

Email / Google Scholar / GitHub / Twitter / CV

Selected Papers

Recent or representative papers:

Using the Tools of Cognitive Science to Understand Large Language Models at Different Levels of Analysis
Alexander Ku, Declan Campbell, Xuechunzi Bai, Jiayi Geng, Ryan Liu, Raja Marjieh, R. Thomas McCoy, Andrew Nam, Ilia Sucholutsky, Veniamin Veselovsky, Liyi Zhang, Jian-Qiao Zhu, Thomas L. Griffiths

Predictability Shapes Adaptation: An Evolutionary Perspective on Modes of Learning in Transformers
Alexander Y. Ku, Thomas L. Griffiths, Stephanie C.Y. Chan

On the Generalization of Language Models from In-Context Learning and Finetuning: A Controlled Study
Andrew K. Lampinen, Arslan Chaudhry, Stephanie C.Y. Chan, Cody Wild, Diane Wan, Alex Ku, Jörg Bornschein, Razvan Pascanu, Murray Shanahan, James L. McClelland