“When taken as a way of modeling cognitive architecture, connectionism really does represent an approach that is quite different from that of the classical cognitive science that it seeks to replace. Classical models of the mind were derived from the structure of Turing and Von Neumann machines. They are not, of course, committed to the details of these machines as exemplified in Turing's original formulation or in typical commercial computers—only to the basic idea that the kind of computing that is relevant to understanding cognition involves operations on symbols.. In contrast, connectionists propose to design systems that can exhibit intelligent behavior without storing, retrieving, or otherwise operating on structured symbolic expressions. The style of processing carried out in such models is thus strikingly unlike what goes on when conventional machines are computing some function.”

— Jerry Fodor

Jerry A. Fodor, and Zenon W. Pylyshyn. "Connectionism and cognitive architecture: A critical analysis." Cognition 28.1-2 (1988): 3-71.

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American philosopher 1935–2017

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“When taken as a way of modeling cognitive architecture, connectionism really does represent an approach that is quite different from that of the classical cognitive science that it seeks to replace. Classical models of the mind were derived from the structure of Turing and Von Neumann machines. They are not, of course, committed to the details of these machines as exemplified in Turing's original formulation or in typical commercial computers—only to the basic idea that the kind of computing that is relevant to understanding cognition involves operations on symbols.. In contrast, connectionists propose to design systems that can exhibit intelligent behavior without storing, retrieving, or otherwise operating on structured symbolic expressions. The style of processing carried out in such models is thus strikingly unlike what goes on when conventional machines are computing some function.”

Zenon Pylyshyn (1937) Canadian philosopher

Jerry A. Fodor, and Zenon W. Pylyshyn. "Connectionism and cognitive architecture: A critical analysis." Cognition 28.1-2 (1988): 3-71.

“In 1936 the notion of a computable function was clarified by Turing, and he showed the existence of universal computers that, with an appropriate program, could compute anything computed by any other computer. […] In some subconscious sense even the sales departments of computer manufacturers are aware of this, and they do not advertise magic instructions that cannot be simulated on competitors machines, but only that their machines are faster, cheaper, have more memory, or are easier to program.”

John McCarthy (1927–2011) American computer scientist and cognitive scientist

" Towards a Mathematical Science of Computation http://www-formal.stanford.edu/jmc/towards.html", Information Processing 1962: Proceedings of IFIP Congress 62, ed. Cicely M. Popplewell (Amsterdam, 1963), pp. 21–28 <br class="br">1960s

“Computer science research is different from these more traditional disciplines. Philosophically it differs from the physical sciences because it seeks not to discover, explain, or exploit the natural world, but instead to study the properties of machines of human creation. In this it as analogous to mathematics, and indeed the "science" part of computer science is, for the most part mathematical in spirit. But an inevitable aspect of computer science is the creation of computer programs: objects that, though intangible, are subject to commercial exchange.”

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“Ashtadhyayi distinguishes between usage in the spoken language and usage that is proper to the language of the sacred texts. The Ashtadhyayi is generative as well as descriptive. With its complex use of metarules, transformations, and recursions, the grammar in Ashtadhyayi has been likened to the Turing machine, an idealized mathematical model that reduces the logical structure of any computing device to its essentials.”

Pāṇini ancient Sanskrit grammarian

Encyclopedia Britannica in: "Panini Indian grammarian".

“The classical support for as-if models in the social sciences stems from the economist Milton Friedman (1953), who, like the psychologist B. F. Skinner, saw little value in modeling cognitive processes. In contrast, our aim is to understand actual decision processes, not only the outcomes. There is a good reason for this. Without modeling the cognitive blade of Simon's scissors, it is utterly impossible to determine in what environments heuristics succeed, that is, their ecological rationality.”

Gerd Gigerenzer Heuristics

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“For the scientist a model is also a way in which the human though processes can be amplified. This method often takes the form of models that can be programmed into computers. At no point, however, the scientist intend to loose control of the situation because off the computer does some of his thinking for him. The scientist controls the basic assumptions and the computer only derives some of the more complicated implications.”

C. West Churchman (1913–2004) American philosopher and systems scientist

Source: 1960s - 1970s, The Systems Approach (1968), p. 61

“For two things both to believe that snow is white, they need not to be physically similar in any specifiable way, but they must both be in a "functional" condition or state specifiable in the most functional language; they must share a Turing machine description according to which they are both in some particular logical state (which is roughly like two different computers having the same program and being in the same "place" in the program). … it is a type functionalism—each mental type is identifiable as a functional type in the language of Turing machine description.”

Daniel Dennett book Brainstorms

Source: Brainstorms (1978), p. xvi

“To an outsider, the most significant innovation in the global warming controversy is the overt reliance that is being placed on models. Back in the days of nuclear winter, computer models were invoked to add weight to a conclusion: “These results are derived with the help of a computer model.” But now, large-scale computer models are seen as generating data in themselves. No longer are models judged by how well they reproduce data from the real world—increasingly, models provide the data. As if they were themselves a reality. And indeed they are, when we are projecting forward.”

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“These computing machines had already been designed, and some built, by {[w|Vannevar Bush}}, Norbert Wiener, and others, and were almost ready-made for the job. These scientists, as well as others such as von Neumann, Shannon and Bigelow, were in a position to see that machines of an electronic kind were ideally suited to carry out the whole of the operations of range-finding and location without any human intervention whatever.
These electronic computing machines were already developed to a very high degree of efficiency for the solution of mathematical equations, and some technical difficulties had led to the suggestion that a process of scanning, similar to that used in television, might be incorporated into the computer. Another innovation was the use of binary notation rather than decimal notation as in the early Harvard Mark I computer.”

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Source: The Brain As A Computer (1962), p.18

“The development of information research has increased considerably the interaction of emerging information science with other disciplines. Librarianship has traditionally had links with education and classification and has drawn ideas from logic and philosophy. But during the last fifty years new insights and methods have been derived from sociology and social psychology, from computer science, from operations research and related quantitative approaches, from communications research, from linguistics, and most recently from the new hybrids: cognitive science and artificial intelligence.”

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