Mechanisms of Implicit Learning – Connectionist Models of Sequence Processing (Neural Network Modeling and Connectionism) - Hardcover

Cleeremans, Axel; Elman, Jeffrey

Hardcover

ISBN 10: 0262032058 ISBN 13: 9780262032056

Publisher: MIT Press, 1993

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 this title

Synopsis

What do people learn when they do not know that they are learning? Until recently, 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 situtations. 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's 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 sometime outperform. Considered together, the models show how complex knowledge may emerge through the operation of elementary mechanisms - a key aspect of implicit learning performance.

From the Back Cover

In this book, 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.

Edition details

• Publisher: MIT Press
• Publication date: 1993
• ISBN 10: 0262032058
• ISBN 13: 9780262032056
• Binding: Hardcover
• Language: English
• Number of pages: 244