Cognitive science and artificial intelligence: death and rebirth of a collaboration


The first chapter proposes a brief historical overview of some of the main insights developed over 65 years of research in Artificial Intelligence (AI), by introducing the early vision of the discipline (based on a mutual collaboration with Cognitive Psychology) and its “paradigm shift”, which started from the mid-1980s of the last century. Starting from that period on, AI and the interdisciplinary enterprise known as Cognitive Science started to produce several sub-fields, each with its own goals, methods, and criteria for evaluation. The reasons for the current renewed interest of a cognitively inspired approach in AI research are discussed.

When Cognitive Science was AI

Cognitive Science and Artificial Intelligence (AI) are, nowadays, scientific research fields each endowed with a specific autonomy and research agenda. According to the Oxford Dictionary, the term “Artificial Intelligence” is defined as “the theory and development of computer systems able to perform tasks normally requiring human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages”, while “Cognitive Science” is defined as “the study of thought, learning, and mental organization, which draws on aspects of psychology, linguistics, philosophy, and computer modelling”.

Despite the current different focuses and objectives of each, these two disciplines have many common interests and share the idea of studying the “mind”, its emergent properties, and its functioning in natural and artificial systems, respectively.

The history of these two research fields is, in fact, strongly interconnected. Research in AI - the birth of which dates back to the now-legendary “Dartmouth Workshop” (McCarthy et al., 1955) held in the summer of 19561 - has, indeed, been historically inspired by the experimental research in psychology.[1] [2] Notable examples of such intellectual connections are represented by the early AI systems/frameworks developed until the 1980s. Most of them, indeed, were explicitly designed with a “cognitively oriented” inspiration. In the following sections, we briefly present few famous examples of such systems and formalisms (though the list is far from being exhaustive) with the aim of introducing some of the main modelling paradigms and assumptions that have characterized, and still characterize, the research in AI and cognitive modelling. Each of the systems/ formalisms reviewed below can be considered important either because they have achieved some important milestones in terms of performances or because has introduced some relevant ideas that have fostered meaningful developments in the study and the realization of “artificial minds”.

  • [1] The organisers of this even were some “giants” of the history of the Computer Science field fromthe last century: John McCarthy, Marvin Minsky, Nathaniel Rochester, and Claude Shannon.The workshop, during which McCarthy proposed the use of the term “artificial intelligence”to identify the new emerging discipline, ran for several weeks and saw the participation of manyresearchers. The notes taken by Ray Solomonoff (one of the participants at the workshop) areavailable online at
  • [2] It must be noted that, at that time, there wasn’t a “Cognitive Science” field. However, allthe disciplines (philosophy, psychology, computer science, anthropology, linguistics, andneurophysiology) and the cultural elements that would have later be called upon to form theinterdisciplinary field of “Cognitive Science” were already present.
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