The Structure of Long-Term Memory: A Connectivity Model of Semantic Processing

The Structure of Long-Term Memory: A Connectivity Model of Semantic Processing

The Structure of Long-Term Memory: A Connectivity Model of Semantic Processing

The Structure of Long-Term Memory: A Connectivity Model of Semantic Processing

Synopsis

How is information stored and retrieved from long-term memory? It is argued that any systematic attempt to answer this question should be based on a particular set of specific representational assumptions that have led to the development of a new memory theory -- the connectivity model. One of the crucial predictions of this model is that, in sharp contrast to traditional theories, the speed of processing information increases as the amount and complexity of integrated knowledge increases. In this volume, the predictions of the model are examined by analyzing the results of a variety of different experiments and by studying the outcome of the simulation program CONN1, which illustrates the representation of complex semantic structures. In the final chapter, the representational assumptions of the connectivity model are evaluated on the basis of neuroanatomical and physiological evidence -- suggesting that neuroscience provides valuable knowledge which should guide the development of memory theories.

Excerpt

An important but controversial issue in memory research concerns the way in which the complexity of semantic structures influences processing time and memory performance. Traditional memory theories such as HAM, ACT, or ACT* assume that memory load increases and processing time slows down as more semantic components are processed. This assumption amounts to what is known as the paradox of retrieval interference: The more information is stored in memory, the slower it works. Chapters 6 and 7 give an extensive review of this issue. Chapter 8 includes the mathematical basis for a new, nonconnectionist memory model, the connectivity model, which refutes the paradox of retrieval interference. The basic assumption here is that -- in contrast to conventional computers -- the speed of search processes in human memory increases as the complexity of interconnected knowledge increases. This prediction, which contradicts all presently existing memory models, explains a variety of different memory phenomena that are discussed in chap. 9. A simulation program is presented in chap. 10. This program allows for a better understanding of the complex predictions of the connectivity model. Neurophysiological evidence is also in close agreement with the predictions of the connectivity model. This issue is addressed in chap. 11, where it is shown that the well-known properties of postsynaptic signal transmission lead to the conclusion that converging neural activity speeds up processing time, and that the stronger a neural signal is, the faster it can be transmitted. Besides other evidence, this fact is also confirmed through reaction time experiments, which show that reaction times decrease as stimulus intensity increases. Finally, chap. 12 gives . . .

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