ICMC2011 Proceedings
نویسندگان
چکیده
We describe a computational model of observational learning inspired by the overlapping cortical activations during execution and observation in primates. For this reason we implement cognitive processes that are known to participate in motor control and investigate how they can be employed during observation. Liquid State Machines are extensively used for the implementation of the model’s neural processes, due to their ability to replicate interdisciplinary biological functions. The developed simulated agent can learn to associate given objects in a scene with novel motor behaviors only by observation, by employing the same neural substrate that is used during motor execution. Keywords—observational learning, motor control, liquid state machines
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