Design Considerations for a Computational Architecture of Human Cognition

Abstract

How does the brain produce cognitive behavior and is it possible to abstract cognition using computers? This question has intrigued us for a very long time. Cognition is related to processes such as thinking, reasoning, memorizing, problem-solving, analyzing, and applying. Most attempts to understand brain function from a cognitive perspective are primarily derived by describing it as the computation of behavioral responses from internal representation of stimuli and stored representations of information from past experience. The origins of this description can be traced back to two key developments in the early twentieth century. Alan Turing's pioneering work [1] in machine theory defined computation as formally equivalent to the manipulation of symbols in a temporary buffer. Similarly, the pioneering work on telephone communication by Shannon and Weaver [2] resulted in a formal definition of information where the informational content of a signal was inversely related to the probability of that signal arising from randomness.These developments launched computer science into prominence, and as computers grew in functional complexity, the analogy between computers and the brain began to be widely recognized. The basic premise for this analogy was that computers and the brain received information from the external environment and both acted upon this information in complex ways. It soon appeared that digital computers joined human brains as the only examples of systems capable of complex reasoning. This analogy between computers and the brain (also known as the computer metaphor) provided a candidate mechanism to explain cognition as akin to a digital computer program that can manipulate internal representation according to a set of rules. The computer metaphor also appeared to clearly identify with memories and