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1.3: A Unified Cognitive Science

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    21203
  • When psychology originated, the promise of a new, unified science was fuelled by the view that a coherent object of enquiry (conscious experience) could be studied using a cohesive paradigm (the experimental method). Wundt defined psychological inquiry as “the investigation of conscious processes in the modes of connexion peculiar to them” (Wundt & Titchner, 1904, p. 2). His belief was that using the experimental method would “accomplish a reform in psychological investigation comparable with the revolution brought about in the natural sciences.” As experimental psychology evolved the content areas that it studied became markedly differentiated, leading to a proliferation of methodologies. The fragmentation of psychology was a natural consequence.

    Cognitive science arose as a discipline in the mid-twentieth century (Boden, 2006; Gardner, 1984; Miller, 2003), and at the outset seemed more unified than psychology. In spite of the diversity of talks presented at the “Special Interest Group in Information Theory” at MIT in 1956, cognitive psychologist George Miller,

    left the symposium with a conviction, more intuitive than rational, that experimental psychology, theoretical linguistics, and the computer simulation of cognitive processes were all pieces from a larger whole and that the future would see a progressive elaboration and coordination of their shared concerns. (Miller, 2003, p. 143)

    The cohesiveness of cognitive science was, perhaps, a natural consequence of its intellectual antecedents. A key inspiration to cognitive science was the digital computer; we see in Chapter 2 that the invention of the computer was the result of the unification of ideas from the diverse fields of philosophy, mathematics, and electrical engineering.

    Similarly, the immediate parent of cognitive science was the field known as cybernetics (Ashby, 1956; de Latil, 1956; Wiener, 1948). Cybernetics aimed to study adaptive behaviour of intelligent agents by employing the notions of feedback and information theory. Its pioneers were polymaths. Not only did cyberneticist William Grey Walter pioneer the use of EEG in neurology (Cooper, 1977), he also invented the world’s first autonomous robots (Bladin, 2006; Hayward, 2001; Holland, 2003a; Sharkey & Sharkey, 2009). Cybernetics creator Norbert Wiener organized the Macy Conferences (Conway & Siegelman, 2005), which were gatherings of mathematicians, computer scientists, psychologists, psychiatrists, anthropologists, and neuroscientists, who together aimed to determine the general workings of the human mind. The Macy Conferences were the forerunners of the interdisciplinary symposia that inspired cognitive scientists such as George Miller.

    What possible glue could unite the diversity of individuals involved first in cybernetics, and later in cognitive science? One answer is that cognitive scientists are united in sharing a key foundational assumption that cognition is information processing (Dawson, 1998). As a result, a critical feature of cognition involves representation or symbolism (Craik, 1943). The early cognitive scientists,

    realized that the integration of parts of several disciplines was possible and desirable, because each of these disciplines had research problems that could be addressed by designing ‘symbolisms.’ Cognitive science is the result of striving towards this integration. (Dawson, 1998, p. 5)

    Assuming that cognition is information processing provides a unifying principle, but also demands methodological pluralism. Cognitive science accounts for human cognition by invoking an information processing explanation. However, information processors themselves require explanatory accounts framed at very different levels of analysis (Marr, 1982; Pylyshyn, 1984). Each level of analysis involves asking qualitatively different kinds of questions, and also involves using dramatically different methodologies to answer them.

    Marr (1982) proposed that information processors require explanations at the computational, algorithmic, and implementational levels. At the computational level, formal proofs are used to determine what information processing problem is being solved. At the algorithmic level, experimental observations and computer simulations are used to determine the particular information processing steps that are being used to solve the information processing problem. At the implementational level, biological or physical methods are used to determine the mechanistic principles that actually instantiate the information processing steps. In addition, a complete explanation of an information processor requires establishing links between these different levels of analysis.

    An approach like Marr’s is a mandatory consequence of assuming that cognition is information processing (Dawson, 1998). It also makes cognitive science particularly alluring. This is because cognitive scientists are aware not only that a variety of methodologies are required to explain information processing, but also that researchers from a diversity of areas can be united by the goal of seeking such an explanation.

    As a result, definitions of cognitive science usually emphasize co-operation across disciplines (Simon, 1980). Cognitive science is “a recognition of a fundamental set of common concerns shared by the disciplines of psychology, computer science, linguistics, economics, epistemology, and the social sciences generally” (Simon, 1980, p. 33). Interviews with eminent cognitive scientists reinforce this theme of interdisciplinary harmony and unity (Baumgartner & Payr, 1995). Indeed, it would appear that cognitive scientists deem it essential to acquire methodologies from more than one discipline.

    For instance, philosopher Patricia Churchland learned about neuroscience at the University of Manitoba Medical School by “doing experiments and dissections and observing human patients with brain damage in neurology rounds” (Baumgartner & Payr, 1995, p. 22). Philosopher Daniel Dennett improved his computer literacy by participating in a year-long working group that included two philosophers and four AI researchers. AI researcher Terry Winograd studied linguistics in London before he went to MIT to study computer science. Psychologist David Rumelhart observed that cognitive science has “a collection of methods that have been developed, some uniquely in cognitive science, but some in related disciplines. . . . It is clear that we have to learn to appreciate one another’s approaches and understand where our own are weak” (Baumgartner & Payr, 1995, p. 196).

    At the same time, as it has matured since its birth in the late 1950s, concerns about cognitive science’s unity have also arisen. Philosopher John Searle stated, “I am not sure whether there is such a thing as cognitive science” (Baumgartner & Payr, 1995, p. 203). Philosopher John Haugeland claimed that “philosophy belongs in cognitive science only because the ‘cognitive sciences’ have not got their act together yet” (p. 103). AI pioneer Herbert Simon described cognitive science as a label “for the fact that there is a lot of conversation across disciplines” (p. 234). For Simon, “cognitive science is the place where they meet. It does not matter whether it is a discipline. It is not really a discipline—yet.”

    In modern cognitive science there exist intense disagreements about what the assumption “cognition is information processing” really means. From one perspective, modern cognitive science is fragmenting into different schools of thought—classical, connectionist, embodied—that have dramatically different views about what the term information processing means. Classical cognitive science interprets this term as meaning rule-governed symbol manipulations of the same type performed by a digital computer. The putative fragmentation of cognitive science begins when this assumption is challenged. John Searle declared, “I think that cognitive science suffers from its obsession with the computer metaphor” (Baumgartner & Payr, 1995, p. 204). Philosopher Paul Churchland declared, “we need to get away from the idea that we are going to achieve Artificial Intelligence by writing clever programs” (p. 37).

    Different interpretations of information processing produce variations of cognitive science that give the strong sense of being mutually incompatible. One purpose of this book is to explore the notion of information processing at the foundation of each of these varieties. A second is to examine whether these notions can be unified.

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