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.
“This is a major book for two reasons. First, the model succeeds quite well in capturing the data patterns of several experiments in which subjects engage in a sequential reaction time task. It will be viewed as the standard model of the task and, hence, will likely be a source of a great deal of future research. The second reason is that it is an excellent example of using a connectionist or neural network model as theory in cognitive science. The model makes use of a popular class of networks pioneered by Jordan and Elman, but unlike most of the work with these networks, Cleereman’s work fits the networks’ behavior to a great deal of experimental data.”
—Gary Dell, University of Illinois
“There have been no well-developed theories of implicit learning and no computational models of the phenomenon. Cleeremans’s book changes that. He presents a detailed computational model of the implicit learning of sequential stimuli that will be important to the field because it has a great deal of predictive power. In addition, the book provides a valuable survey. The review sections are comprehensive and detailed. Cleeremans’s writing style is a delight.”
—Jeffrey Elman, University of California, San Diego
“For some time now, implicit learning has been a phenomenon in search of a theory. Cleeremans takes the first important step in providing a formal model based on connectionist theory. His penetrating analysis now becomes the standard against which others will be judged.”
—Arthur S. Reber, Professor of Psychology, Brooklyn College and the Graduate Center of the City University of New York