Biomaterial Database

Implicit and explicit representations. 2009

Nicolas P Rougier

INRIA Nancy - Grand Est, 615, Rue du Jardin Botanique, 54 600 Villers-Lès-Nancy, France. Nicolas.Rougier@loria.fr

During the past decades, the symbol grounding problem, as has been identified by Harnard [Harnard, S. (1990). The symbol grounding problem. Physica D: Nonlinear Phenomena, 42, 335-346], became a prominent problem in the cognitive science society. The idea that a symbol is much more than a mere meaningless token that can be processed through some algorithm, sheds new light on higher brain functions such as language and cognition. We present in this article a computational framework that may help in our understanding of the nature of grounded representations. Two models are briefly introduced that aim at emphasizing the difference we make between implicit and explicit representations.

UI	MeSH Term	Description	Entries
D007802	Language	A verbal or nonverbal means of communicating ideas or feelings.	Dialect,Dialects,Languages
D008959	Models, Neurological	Theoretical representations that simulate the behavior or activity of the neurological system, processes or phenomena; includes the use of mathematical equations, computers, and other electronic equipment.	Neurologic Models,Model, Neurological,Neurologic Model,Neurological Model,Neurological Models,Model, Neurologic,Models, Neurologic
D006801	Humans	Members of the species Homo sapiens.	Homo sapiens,Man (Taxonomy),Human,Man, Modern,Modern Man
D000465	Algorithms	A procedure consisting of a sequence of algebraic formulas and/or logical steps to calculate or determine a given task.	Algorithm
D001288	Attention	Focusing on certain aspects of current experience to the exclusion of others. It is the act of heeding or taking notice or concentrating.	Focus of Attention,Selective Attention,Social Attention,Attention Focus,Attention, Selective,Attention, Social,Selective Attentions
D016571	Neural Networks, Computer	A computer architecture, implementable in either hardware or software, modeled after biological neural networks. Like the biological system in which the processing capability is a result of the interconnection strengths between arrays of nonlinear processing nodes, computerized neural networks, often called perceptrons or multilayer connectionist models, consist of neuron-like units. A homogeneous group of units makes up a layer. These networks are good at pattern recognition. They are adaptive, performing tasks by example, and thus are better for decision-making than are linear learning machines or cluster analysis. They do not require explicit programming.	Computational Neural Networks,Connectionist Models,Models, Neural Network,Neural Network Models,Neural Networks (Computer),Perceptrons,Computational Neural Network,Computer Neural Network,Computer Neural Networks,Connectionist Model,Model, Connectionist,Model, Neural Network,Models, Connectionist,Network Model, Neural,Network Models, Neural,Network, Computational Neural,Network, Computer Neural,Network, Neural (Computer),Networks, Computational Neural,Networks, Computer Neural,Networks, Neural (Computer),Neural Network (Computer),Neural Network Model,Neural Network, Computational,Neural Network, Computer,Neural Networks, Computational,Perceptron
D032882	Comprehension	The act or fact of grasping the meaning, nature, or importance of; understanding. (American Heritage Dictionary, 4th ed) Includes understanding by a patient or research subject of information disclosed orally or in writing.	Readability,Understanding