000			03934nam a2200565 i 4500
001			6267377
003			IEEE
005			20220712204646.0
006			m o d
007			cr |n|||||||||
008			151223s1993 maua ob 001 eng d
020			_a0262032058
020			_a9780262032056
020			_a9780262270472 _qebook
020			_z0585020388 _qelectronic
020			_z9780585020389 _qelectronic
020			_z0262270471 _qelectronic
035			_a(CaBNVSL)mat06267377
035			_a(IDAMS)0b000064818b4399
040			_aCaBNVSL _beng _erda _cCaBNVSL _dCaBNVSL
050		4	_aQA76.87 _b.C54 1993eb
082	0	4	_a006.3/3 _220
100	1		_aCleeremans, Axel, _eauthor. _922445
245	1	0	_aMechanisms of implicit learning : _bconnectionist models of sequence processing / _cAxel Cleeremans.
264		1	_aCambridge, Massachusetts : _bMIT Press, _cc1993.
264		2	_a[Piscataqay, New Jersey] : _bIEEE Xplore, _c[1993]
300			_a1 PDF (xii, 227 pages) : _billustrations.
336			_atext _2rdacontent
337			_aelectronic _2isbdmedia
338			_aonline resource _2rdacarrier
490	1		_aNeural network modeling and connectionism
500			_a"A Bradford book."
504			_aIncludes bibliographical references (p. [213]-220) and index.
505	0		_a1. Implicit learning : explorations in basic cognition -- 2. The SRN Model : computational aspects of sequence processing -- 3. Sequence learning as a paradigm for studying implicit learning -- 4. Sequence learning : further explorations -- 5. Encoding remote context --
506	1		_aRestricted to subscribers or individual electronic text purchasers.
520			_aWhat do people learn when they do not know that they are learning? Until recently all of the work in the area of implicit learning focused on empirical questions and methods. In this book, Axel Cleeremans explores unintentional learning from an information-processing perspective. He introduces a theoretical framework that unifies existing data and models on implicit learning, along with a detailed computational model of human performance in sequence-learning situations.The model, based on a simple recurrent network (SRN), is able to predict perfectly the successive elements of sequences generated from finite-state, grammars. Human subjects are shown to exhibit a similar sensitivity to the temporal structure in a series of choice reaction time experiments of increasing complexity; yet their explicit knowledge of the sequence remains limited. Simulation experiments indicate that the SRN model is able to account for these data in great detail.Cleeremans' model is also useful in understanding the effects of a wide range of variables on sequence-learning performance such as attention, the availability of explicit information, or the complexity of the material. Other architectures that process sequential material are considered. These are contrasted with the SRN model, which they sometimes outperform. Considered together, the models show how complex knowledge may emerge through the operation of elementary mechanisms - a key aspect of implicit learning performance.Axel Cleeremans is a Senior Research Assistant at the National Fund for Scientific Research, Belgium.
530			_aAlso available in print.
538			_aMode of access: World Wide Web
588			_aDescription based on PDF viewed 12/23/2015.
650		0	_aConnection machines. _921912
650		0	_aImplicit learning. _922446
650		0	_aNeural networks (Computer science) _93414
655		0	_aElectronic books. _93294
710	2		_aIEEE Xplore (Online Service), _edistributor. _922447
710	2		_aMIT Press, _epublisher. _922448
776	0	8	_iPrint version _z9780262032056
830		0	_aNeural network modeling and connectionism. _922449
856	4	2	_3Abstract with links to resource _uhttps://ieeexplore.ieee.org/xpl/bkabstractplus.jsp?bkn=6267377
942			_cEBK
999			_c73032 _d73032