AI is an invention that suddenly caught us by surprise and will change society forever. It has offered several opportunities across sectors. AI was first coined in a proposal for a workshop in the summer of 1956 at Dartmouth College. The workshop aimed to find answers to questions such as how to make our language, form abstractions and concepts, and problem solve: How can artificial minds that perform similar to human minds be developed. The meeting was attended by 11 computer scientists, among them Allan Newell and Herb Simon, who won a Nobel Prize for their work two decades later.  

Two weeks after the workshop, in a particular interest group information theory held at MIT, questions regarding language, abstractions and concepts and problem-solving played a central role. These questions were motivated by the main question: How can we better understand the human mind by developing artificial minds. This particular interest group marked the beginning of a cognitive revolution and cognitive science. The cognitive revolution can best be characterized by a movement that emphasizes the interdisciplinary study of the human mind and its processes. It highlights similarities between computational and mental processes, between human minds and Artificial minds. It led to what is now known as cognitive science, an interdisciplinary research program comprised of psychology, computer science, neuroscience, linguistics and related subjects. 

Looking back at the meetings, the birth of AI and cognitive science, it is almost as if AI is computer science motivated by psychology, and cognitive science psychology motivated by computer science.

Relation between AI and cognitive science 

The relationship between AI and cognitive science is not restricted to two workshops. They have striking similarities in aspects of theories, concepts and methods. For example, reinforcement learning in AI is derived from reinforcement learning in psychology. Also, Human neural networks initially inspired Deep Learning and the use of artificial neural networks. Particularly around the 1980s, these artificial neural networks showed a lot of promise, less so at the time in AI and more so in cognitive science. Deep Learning is infact a new name for an approach to AI called Neural Networks. Neural Network was first introduced in 1944 by Warren McCullough and Walter Pitts, two University of Chicago researchers who moved to MIT in 1952. 

The connection between both can also be found in the background of leading researchers. Among the researchers who proposed the Dartmouth workshop were John McCarthy, both a computer scientist and cognitive scientist and Marvin Minsky, both a cognitive and computer scientist. Others who attended the workshop, including Allan Newell, had psychology and computer science research background. Also, David Rumelhart and Jay McClelland, who led research in artificial neural networks in the 1980s, had an experience in psychology.

Explainable AI 

The much more important take-home message for the interdependencies of AI and cognitive science does not lie in the history of AI, neither in the use of similar concepts and methods nor in the background of researchers. Instead, it lies in what is there to learn from AI and cognitive science. For instance, with regards to the importance of Explainable AI, also referred to as XAI. Whereas AI often focuses on accuracy, we may want to pay more attention to why techniques and methods make particular decisions. 

The performance accuracy of AI systems is rapidly increasing with more computing power and more complex algorithms. However, that also comes at a price that is explainability, if we want to build algorithms that follow the FAIR principles of Findability, Accessibility, Interoperability and Reuse of digital assets.  

We have been considerably more skeptical of big data’s promise within the cognitive sciences, largely because we place such a high value on explanation over prediction. In other words, the interdependence of AI and cognitive science does not lie in the past. On the contrary, they lie in the future more than ever. 

_{Source: Psychology Today}