Minding Prediction…

Some definitions won’t do any harm, huh?  I’m going to quote Wikipedia for the definitions of the main concepts we are going to work with.

Taking them from Wikipedia is not a guarantee of anything, so we should be able to give better (and more adjusted) definitions after crawling into the state of the art a bit more.

Cognition:

The term cognition is used in different ways by different disciplines. In psychology, it refers to an information processing view of an individual’s psychological functions. Other interpretations of the meaning of cognition link it to the development of concepts; individual minds, groups, organizations, and even larger coalitions of entities, can be modelled as societies which cooperate to form concepts. The autonomous elements of each ‘society‘ would have the opportunity to demonstrate emergent behavior in the face of some crisis or opportunity. Cognition can also be interpreted as “understanding and trying to make sense of the world”.

Cognitive science:

Cognitive science is most simply defined as the scientific study either of mind or of intelligence. ^[1] It is an interdisciplinary study drawing from relevant fields including psychology, philosophy, neuroscience, linguistics,anthropology, computer science, and biology. The term cognitive science was coined by Christopher Longuet-Higginsin his 1973 commentary on the Lighthill report, which concerned the then-current state of Artificial Intelligenceresearch. In the same decade, the journal Cognitive Science^[2] and the Cognitive Science Society began.

Cognitive architecture:

A cognitive architecture is a blueprint for intelligent agents. It proposes (artificial) computational processes that act like certain cognitive systems, most often, like a person, or actsintelligent under some definition. Cognitive architectures form a subset of general agent architectures. The term ‘architecture’ implies an approach that attempts to model not only behavior, but also structural properties of the modelled system. These need not be physical properties: they can be properties of virtual machines implemented in physical machines (e.g. brains or computers).

Cognitive model:

A cognitive model is an approximation to animal cognitive processes (predominantly human) for the purposes of comprehension and prediction. Cognitive models can be developed within or without a cognitive architecture, though the two are not always easily distinguishable.

In contrast to cognitive architectures, cognitive models tend to be focused on a single cognitive phenomenon or process (e.g., list learning), how two or more processes interact (e.g., visual search and decision making), or to make behavioral predictions for a specific task or tool (e.g., how instituting a new software package will affect productivity). Cognitive architectures tend to be focused on the structural properties of the modeled system, and help constrain the development of cognitive models within the architecture. Likewise, model development helps to inform limitations and shortcomings of the architecture. Some of the most popular architectures for cognitive modeling include ACT-R and Soar.