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  1. Change blindness.Daniel J. Simons & Daniel T. Levin - 1997 - Trends in Cognitive Sciences 1 (1):241-82.
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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.
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  • 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.
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  • Credit Assignment in Multiple Goal Embodied Visuomotor Behavior.Constantin A. Rothkopf & Dana H. Ballard - 2010 - Frontiers in Psychology 1.
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  • The Role of Embodied Intention in Early Lexical Acquisition.Chen Yu, Dana H. Ballard & Richard N. Aslin - 2005 - Cognitive Science 29 (6):961-1005.
    We examine the influence of inferring interlocutors' referential intentions from their body movements at the early stage of lexical acquisition. By testing human participants and comparing their performances in different learning conditions, we find that those embodied intentions facilitate both word discovery and word‐meaning association. In light of empirical findings, the main part of this article presents a computational model that can identify the sound patterns of individual words from continuous speech, using nonlinguistic contextual information, and employ body movements as (...)
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  • 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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  • 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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  • Computational Modelling of Visual Attention.Laurent Itti & Christof Koch - 2001 - Nature Reviews Neuroscience 2 (3):194–203.
    Five important trends have emerged from recent work on computational models of focal visual attention that emphasize the bottom-up, image-based control of attentional deployment. First, the perceptual saliency of stimuli critically depends on the surrounding context. Second, a unique 'saliency map' that topographically encodes for stimulus conspicuity over the visual scene has proved to be an efficient and plausible bottom-up control strategy. Third, inhibition of return, the process by which the currently attended location is prevented from being attended again, is (...)
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  • Determining optical flow.Berthold K. P. Horn & Brian G. Schunck - 1981 - Artificial Intelligence 17 (1-3):185-203.
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  • Embodied attention and word learning by toddlers.Chen Yu & Linda B. Smith - 2012 - Cognition 125 (2):244-262.
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  • A neural substrate of prediction and reward.Wolfram Schultz, Peter Dayan & Read Montague - 1997 - Science 275 (5306):1593–9.
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  • 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.
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  • Motion illusions as optimal percepts.Y. Weiss, E. P. Simoncelli & E. H. Adelson - 2002 - Nature Neuroscience 5.
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