Computational cognition

Computational cognition (sometimes referred to as computational cognition science) is the study of the computational basis of learning and inference by mathematical modeling, computer simulation, and behavioral experiments. In psychology, it is an approach which develops computational models based on experimental results. It seeks to understand the basis behind the human method of processing of information.

There are two main purposes for the productions of artificial intelligence: to produce intelligent behaviors regardless of the quality of the results, and to model after intelligent behaviors found in nature. In the beginning of its existence, there was no need for artificial intelligence to emulate the same behavior as human cognition. Until 1960s, economist Herbert Simon and Allen Newell attempted to formalize human problem-solving skills by using the results of psychological studies to develop programs that implement the same problem-solving techniques as people would. Their works laid the foundation for symbolic AI and computational cognition, and even some advancements for cognitive science and cognitive psychology.

The field of symbolic AI is based on the physical symbol systems hypothesis by Simon and Newell, which states that expressing aspects of cognitive intelligence can be achieved through the manipulation of symbols. However, John McCarthy focused more on the initial purpose of artificial intelligence, which is to breakdown the essence of logical and abstract reasoning regardless of whether or not human employs the same mechanism.

Over the next decades, the progress made in artificial intelligence started to be focused more on developing logic-based and knowledge-based programs, veering away from the original purpose of symbolic AI. Researchers started to believe that artificial intelligence may never be able to imitate some intricate processes of human cognition like perception or learning. They began to take a “sub-symbolic” approach to create intelligence without specifically representing that knowledge. This movement led to the emerging discipline of computational modeling, connectionism, and computational intelligence.

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