Mechanisms of implicit learning : (Record no. 73032)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 03934nam a2200565 i 4500 |
| 001 - CONTROL NUMBER | |
| control field | 6267377 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20220712204646.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 151223s1993 maua ob 001 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| ISBN | 0262032058 |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| ISBN | 9780262032056 |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| ISBN | 9780262270472 |
| -- | ebook |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| -- | electronic |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| -- | electronic |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| -- | electronic |
| 082 04 - CLASSIFICATION NUMBER | |
| Call Number | 006.3/3 |
| 100 1# - AUTHOR NAME | |
| Author | Cleeremans, Axel, |
| 245 10 - TITLE STATEMENT | |
| Title | Mechanisms of implicit learning : |
| Sub Title | connectionist models of sequence processing / |
| 300 ## - PHYSICAL DESCRIPTION | |
| Number of Pages | 1 PDF (xii, 227 pages) : |
| 490 1# - SERIES STATEMENT | |
| Series statement | Neural network modeling and connectionism |
| 500 ## - GENERAL NOTE | |
| Remark 1 | "A Bradford book." |
| 505 0# - FORMATTED CONTENTS NOTE | |
| Remark 2 | 1. 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 -- |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc | What 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. |
| 856 42 - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | https://ieeexplore.ieee.org/xpl/bkabstractplus.jsp?bkn=6267377 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | eBooks |
| 264 #1 - | |
| -- | Cambridge, Massachusetts : |
| -- | MIT Press, |
| -- | c1993. |
| 264 #2 - | |
| -- | [Piscataqay, New Jersey] : |
| -- | IEEE Xplore, |
| -- | [1993] |
| 336 ## - | |
| -- | text |
| -- | rdacontent |
| 337 ## - | |
| -- | electronic |
| -- | isbdmedia |
| 338 ## - | |
| -- | online resource |
| -- | rdacarrier |
| 588 ## - | |
| -- | Description based on PDF viewed 12/23/2015. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Connection machines. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Implicit learning. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Neural networks (Computer science) |
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