Mechanisms of Implicit Learning: Connectionist Models of Sequence Processing

Axel Cleeremans

ISBN 10: 0262032058 / ISBN 13: 9780262032056

Published by MIT Press, 1993

About this Item

Ancien livre de bibliothèque. Légères traces d'usure sur la couverture. Sans jaquette. Couverture différente. Edition 1993. Ammareal reverse jusqu'à 15% du prix net de cet article à des organisations caritatives. ENGLISH DESCRIPTION Book Condition: Used, Good. Former library book. Slight signs of wear on the cover. No dust jacket. Different cover. Edition 1993. Ammareal gives back up to 15% of this item's net price to charity organizations.

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Bibliographic Details

• Title: Mechanisms of Implicit Learning: Connectionist Models of Sequence Processing
• Publisher: MIT Press
• Publication Date: 1993
• Language: English
• ISBN 10: 0262032058
• ISBN 13: 9780262032056
• Binding: No jacket
• Condition: Bon

About this title

Synopsis

This book explores unintentional learning from an information-processing perspective.

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.

About the Author

Axel Cleeremans is a Senior Research Assistant at the National Fund for Scientific Research, Belgium.