joshua abbott
joshua.abbott@berkeley.edu  

Computational Cognitive Science Lab
Department of Psychology
5429 Tolman Hall
University of California, Berkeley



me  |  research  |  papers  |  presentations

I am currently a fourth-year PhD candidate at UC Berkeley under the advising of Tom Griffiths in the Computational Cognitive Science lab. My research program lies at the intersection of machine learning and computational models of cognition where I'm interested in using mathematical models to formalize "learning", an ability central to both artificial and biological intelligence. From within the interdisciplinary field of cognitive science, ideas and methods from computer science, statistics, and psychology can be merged to explore the many facets of what it means to learn.

As a computational cognitive scientist, I follow a top-down approach to reverse-engineering human learning. This view holds that we can best understand cognition by identifying the problem being solved and comparing how people's behavior differs from an ideal observer. By generating a formal description of the problem being solved, we can develop experiments to test the types of mental representations and inductive biases people use in their everyday lives. As a result, this top-down approach provides us with a better understanding of human cognition and lets us build machine learning models that perform more like people.


Curriculum Vitae | Google Scholar