Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
ICDL '02 Proceedings of the 2nd International Conference on Development and Learning
2006 Special issue: A probabilistic model of gaze imitation and shared attention
Neural Networks - 2006 Special issue: The brain mechanisms of imitation learning
Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems
Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems
Emergence of Mirror Neurons in a Model of Gaze Following
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
The cog project: building a humanoid robot
Computation for metaphors, analogy, and agents
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The control of overt visual attention relies on an interplay of bottom-up and top-down mechanisms. Purely bottom-up models may provide a reasonable account of the looking behaviors of young infants, but they cannot accurately account for attention orienting of adults in many natural behaviors. But how do humans learn to incorporate top-down mechanisms into their control of attention? The phenomenon of gaze following, i.e. the ability to infer where someone else is looking and to orient to the same location, offers an interesting window into this question. We review findings on the emergence of gaze following in human infants and present a computational model of the underlying learning processes. The model exhibits a gradual incorporation of top-down cues in the infant's attention control. It explains this process in terms of generic reinforcement learning mechanisms. We conclude that reinforcement learning may be a major driving force behind the incorporation of top-down cues into the control of visual attention.