000 | 03934nam a2200565 i 4500 | ||
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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 |
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020 |
_z0585020388 _qelectronic |
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020 |
_z9780585020389 _qelectronic |
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020 |
_z0262270471 _qelectronic |
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035 | _a(CaBNVSL)mat06267377 | ||
035 | _a(IDAMS)0b000064818b4399 | ||
040 |
_aCaBNVSL _beng _erda _cCaBNVSL _dCaBNVSL |
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050 | 4 |
_aQA76.87 _b.C54 1993eb |
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082 | 0 | 4 |
_a006.3/3 _220 |
100 | 1 |
_aCleeremans, Axel, _eauthor. _922445 |
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245 | 1 | 0 |
_aMechanisms of implicit learning : _bconnectionist models of sequence processing / _cAxel Cleeremans. |
264 | 1 |
_aCambridge, Massachusetts : _bMIT Press, _cc1993. |
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264 | 2 |
_a[Piscataqay, New Jersey] : _bIEEE Xplore, _c[1993] |
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300 |
_a1 PDF (xii, 227 pages) : _billustrations. |
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336 |
_atext _2rdacontent |
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337 |
_aelectronic _2isbdmedia |
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338 |
_aonline resource _2rdacarrier |
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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 |
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650 | 0 |
_aImplicit learning. _922446 |
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650 | 0 |
_aNeural networks (Computer science) _93414 |
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655 | 0 |
_aElectronic books. _93294 |
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710 | 2 |
_aIEEE Xplore (Online Service), _edistributor. _922447 |
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710 | 2 |
_aMIT Press, _epublisher. _922448 |
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776 | 0 | 8 |
_iPrint version _z9780262032056 |
830 | 0 |
_aNeural network modeling and connectionism. _922449 |
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856 | 4 | 2 |
_3Abstract with links to resource _uhttps://ieeexplore.ieee.org/xpl/bkabstractplus.jsp?bkn=6267377 |
942 | _cEBK | ||
999 |
_c73032 _d73032 |