Recall and reasoning - an information theoretical model of cognitive processes
Cognitive Psychology studies humans' capabilities to memorize and recall knowledge and images, among others. Connectionistic, propositional and conceptual models are a means to survey these phenomenons. This paper proposes an information theoretical network for simulating stimulus and response in categorical structures. A stimulus triggers an information flow throughout the whole network and generates for all ideas representing vertices an impact in the information theoretical unit [bit], thus measuring the recall intensity and producing a response. The method is shown to yield results of high performance even for complex taxonomies and connectionistic models. Reasoning is the logical counterpart of recall. Once an idea is associated with a Stimulus, logical dependencies between both must be established, if required. Information theoretical networks allow to switch between a recall mode and a reasoning mode, also permitting logical reasoning within the same framework. Both capabilities are demonstrated by suitable examples. (C) 2005 Elsevier Inc. All rights reserved.
recall connectionistic model association logical reasoning artificial intelligence stimulus/response information network semantic network conditional logic inference