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Dell, Gary S. Adams, David Russell 2015-09-25T20:39:39Z 2015-09-25T20:39:39Z 10000-01-01 1997 1997 While connectionist models have been proposed and met with some success in many areas of psychology, connectionist models of memory have not met the standards created by competing models of memory. Much of the reason for this failure is due to the severe interference connectionist models typically show for information learned sequentially, which is often called catastrophic interference. Solutions to the catastrophic interference problem in turn sacrifice the properties that make these models attractive. This dissertation proposes an alternative connectionist model of recognition memory, which contains a solution to the catastrophic interference problem that preserves the strengths of the connectionist approach. This model not only solves the catastrophic interference problem, but proposes a unique solution to the need for models of recognition to explain both interference and generalization processes in memory. Made available in DSpace on 2015-09-25T20:39:39Z (GMT). No. of bitstreams: 2 license.txt: 4848 bytes, checksum: 96035ab3f5e1c23cc7138a224ce498bd (MD5) 9812517.pdf: 4365648 bytes, checksum: 1dbd095744331af83f1a18b07f67a29c (MD5) Previous issue date: 1997 Embargo set by: Seth Robbins for item 83498 Lift date: Forever Reason: Restricted to the U of I community idenfinitely during batch ingest of legacy ETDs Restricted to the U of I community idenfinitely during batch ingest of legacy ETDs U of I Only 110 p. Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 1997. http://hdl.handle.net/2142/82217 (MiAaPQ)AAI9812517 eng Psychology, Cognitive A Hybrid Connectionist-Instance Model of Recognition Memory text Psychology Psychology University of Illinois at Urbana-Champaign Dissertation Ph.D.