Information processing for learning systems: an action theoretical approach

For learning systems that interact with their environments, the more primitive concept of `variety´ will have to be used, instead of probability. Humans have a fundamental need for variety. This need for variety leads to a different interpretation of human behavior that is often classified as “errors”. Variety is the basis to measure complexity of an activity. Complexity in the relationship between a learning system and human context can be expressed as incongruity. Incongruity is the difference between internal complexity of a learning system and the complexity of the context. Traditional concepts of information processing are models of homoeostasis on a basic level without learning. Activity and the irreversible learning process are driving forces that cause permanently in-homoeostasis in the relationship between a learning system and human context. A concept of information processing is presented that derives an inverted U-shaped function between incongruity and information. This concept leads to some design recommendations for man-machine systems