I am a graduate student in Cognitive Psychology at the University of Colorado in Boulder. I am primarily interested in computer models of learning and representation.
Learning Complex Dynamic Tasks
Humans have an incredible capacity to learn new complex tasks that involve processing massive amounts of information. Often, only a small subset of the available information is relevant to accomplishing our goals. When performing a complex task we learn to attend only to the most useful features. The most relevant features of our environment are the ones that allow us to make accurate predictions of appropriate actions, rewards, and future events. My goal is to understand how people discover these features, and what role feature discovery and selective attention play in learning dynamic tasks.
- Cañas F, & Jones M. (2010). Attention and reinforcement learning: Constructing representations from indirect feedback. Proceedings of the 32nd Annual Meeting of the Cognitive Science Society.
- Jones M, & Cañas F. (2010). Integrating reinforcement learning with models of representation learning. Proceedings of the 32nd Annual Meeting of the Cognitive Science Society.
- Rebula J, Cañas F, Pratt J. (2007). Learning Capture Points for Humanoid Push Recovery. IEEE-RAS International Conference on Humanoid Robots
- Cañas F., & Jones M. (2009). Applying principles of attention learning from categorization to reinforcement learning. Poster presented at the 50th Annual Meeting of the Psychonomic Society.[handout]
- 2011 M.A. University of Colorado, Boulder – Department of Psychology and Neuroscience
- 2007 B.S. Cornell University in Biology with a concentration in Neurobiology and Behavior