McClelland, J. L. (1981). Retrieving general and specific information from stored knowledge of specifics. In Proceedings of the Third Annual Conference of the Cognitive Science Society (pp. 170-2). Hillsdale, NJ: Erlbaum. Home/Search Document Not in Database Summary Related Articles Check |
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Spoken Word Recognition: A Combined Computational and.. - Gaskell (1994) (Correct)
....ability of a network to handle a large number of possibly conflicting constraints, coming up with the best overall solution to the problem. This kind of model has been very useful in visual perception (McClelland Rumelhart, 1981) speech perception (McClelland Elman, 1986) and memory research (McClelland, 1981) since it is able to replicate subjects ability to handle noisy or incomplete information. A good example of a soft constraint model is the Jets and Sharks model described in McClelland Rumelhart (1988) This is a localist interactive activation model, hard wired to model aspects of ....
McClelland, J. L. (1981). Retrieving general and specific information from stored knowledge of specifics. In Proceedings of the Third Annual Conference of the Cognitive Science Society (pp. 170-2). Hillsdale, NJ: Erlbaum.
Principles for Implicit Learning - Cleeremans (1996) (3 citations) (Correct)
....it to fail to reach awareness, or in such a way that the primed elements remain below some awareness threshold, for instance, but this again appears to be an arbitrary rather than natural feature of the resulting model. In contrast, even early models such as McClelland Jets and Sharks example (McClelland, 1981) illustrate how several properties related to priming, such as content addressability or spontaneous generalization, emerge naturally out of the model. As McClelland, Rumelhart and Hinton (1986) state: These properties must be explicitly implemented as complicated computational extensions of ....
McClelland, J.L. (1981). Retrieving general and specific information from stored knowledge af specifics.
A Brief History of Connectionism - Medler (1998) (5 citations) (Correct)
....Connections between units may or may not be massively parallel in the sense that every unit is connected to every other unit. Moreover, connections may be feed forward (i.e. signals being passed in one direction only [92] 93] or interactive (i.e. bidirectional passing of signals [66]) Finally, the weights associated with the connections may be hardwired , learned, or both. The weights represent the strength of connection (either excitatory or inhibitory) between two units. These three tenets allow a large spectrum of models (e.g. Selfridge s Pandemonium [100] Rumelhart ....
....of the main architectures is described, related network architectures will also be reviewed. These reviews will provide somewhat less detail as they are meant to provide only a cursory examination of comparable architectures. The first architecture to be described in detail is James McClelland s [66] Interactive Activation and Competition (IAC) model of information retrieval from stored knowledge. Although early versions of the IAC architecture did not learn hence, it could rightly be considered within the class of Old Connectionism as defined earlier the model displays many ....
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J. L. McClelland. Retrieving general and specific information from stored knowledge of specifics. In Proceedings of the Third Annual Meeting of the Cognitive Science Society, pages 170--172, 1981.
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