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  1. The role of embodied intention in early lexical acquisition.Chen Yu, Dana H. Ballard & Richard N. Aslin - 2005 - Cognitive Science 29 (6):961-1005.
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  • Embodied attention and word learning by toddlers.Chen Yu & Linda B. Smith - 2012 - Cognition 125 (2):244-262.
  • Motion illusions as optimal percepts.Y. Weiss, E. P. Simoncelli & E. H. Adelson - 2002 - Nature Neuroscience 5.
  • Between MDPs and semi-MDPs: A framework for temporal abstraction in reinforcement learning.Richard S. Sutton, Doina Precup & Satinder Singh - 1999 - Artificial Intelligence 112 (1-2):181-211.
  • Change blindness.Daniel J. Simons & Daniel T. Levin - 1997 - Trends in Cognitive Sciences 1 (1):241-82.
  • Effects of prosodically modulated sub-phonetic variation on lexical competition.Anne Pier Salverda, Delphine Dahan, Michael K. Tanenhaus, Katherine Crosswhite, Mikhail Masharov & Joyce McDonough - 2007 - Cognition 105 (2):466-476.
  • Credit Assignment in Multiple Goal Embodied Visuomotor Behavior.Constantin A. Rothkopf & Dana H. Ballard - 2010 - Frontiers in Psychology 1.
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  • Determining optical flow.Berthold K. P. Horn & Brian G. Schunck - 1981 - Artificial Intelligence 17 (1-3):185-203.
  • Deictic codes for the embodiment of cognition.Dana H. Ballard, Mary M. Hayhoe, Polly K. Pook & Rajesh P. N. Rao - 1997 - Behavioral and Brain Sciences 20 (4):723-742.
    To describe phenomena that occur at different time scales, computational models of the brain must incorporate different levels of abstraction. At time scales of approximately 1/3 of a second, orienting movements of the body play a crucial role in cognition and form a useful computational level embodiment level,” the constraints of the physical system determine the nature of cognitive operations. The key synergy is that at time scales of about 1/3 of a second, the natural sequentiality of body movements can (...)
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  • Computational modelling of visual attention.L. Itti & C. Koch - 2001 - Nature Reviews Neuroscience 2 (3):194-203.
  • The what and why of binding: The modeler's perspective.Christoph von der Malsburg - 1999 - Neuron 24:95-104.
    In attempts to formulate a computational understanding of brain function, one of the fundamental concerns is the data structure by which the brain represents information. For many decades, a conceptual framework has dominated the thinking of both brain modelers and neurobiologists. That framework is referred to here as "classical neural networks." It is well supported by experimental data, although it may be incomplete. A characterization of this framework will be offered in the next section. Difficulties in modeling important functional aspects (...)
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  • What you see is what you need.Jochen Triesch, Dana Ballard, Mary Hayhoe & Brian Sullivan - 2003 - Journal of Vision 3 (1):86-94.