| Chris Vigorito | |
| Ms/PhD Student Autonomous Learning Laboratory Department of Computer Science University of Massachusetts Amherst |
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| 140 Governors Dr Amherst, MA, 01003 (413) 545-1596  |
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In the summer of 2004 I received a B.A. in Computer Science and Psychology from Amherst College under the advisorship of Professor Catherine McGeoch (CS) and Professor Sarah Turgeon (Psych). My senior thesis in Computer Science was completed under the supervision of Professor Andrew Barto at the University of Massachusetts Amherst. For my thesis I developed a computational model of the partial reinforcement extinction effect observed in many animal learning studies involving schedules of partial reinforcement. I joined the Department of Computer Science at the University of Massachusetts Amherst in the fall of 2004 and began research as a member of the Autonomous Learning Laboratory under the advisorship of Professor Andrew Barto.
My research interests lie at the intersection of the fields of machine learning, cognitive neuroscience, and developmental robotics. More specifically, I am interested in the design of artificial systems that exhibit life-long, motivated, cumulative learning of increasingly complex perceptual and behavioral representations. Such representations, I believe, must be learned in a developmental setting as they are in humans and animals, and so computational architectures inspired by principles of neural and behavioral development in biological systems interest me greatly.
I am currently engaged in research with my advisor Professor Andrew Barto developing algorithms for intrinsically motivated learning of hierarchies of skills in artificial agents. We use reinforcement learning as our formalism and are exploring various ways of modeling intrinsically motivated behavior in humans and animals; i.e., behavior that is rewarding for its own sake, rather than because it solves a specific problem.
I am also involved in research with Professor John Moore and Robert Polewan of the UMass Department of Neuroscience and Behavior. We have developed a human eyeblink conditioning paradigm (the Cartesian Reflex Project) for studying the effects of different stimuli (e.g., faces vs. geometric shapes) on cognitive processing time in traditional classical conditioning tasks with voluntary unconditioned responses. My primary contribution to this endeavor is the development of the hardware/software interface and protocol design software used in the paradigm.