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.
Keywords: artificial intelligence -- association -- logical reasoning -- stimulus / response -- connectionistic model -- conditional logic -- inference