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Critique of Dreyfus’s Theory
Posted By: 6stones In Response To: Matches +1, Phil the preacher -1 (higonefive)
Date: Saturday, 21 July 2012, at 11:44 a.m.
Source: "Expertise and Intuition: A tale of three theories" by Fernand Gobet and Philippe Chassy
Dreyfus’s Theory of Expertise and Intuition
In his book What Computers Can’t Do Dreyfus (1972) developed a wideranging critique of the symbolic approach in artificial intelligence, as exemplified for example by the work of Newell and Simon (1972), Minsky (1977), and McCarthy (1968). One key argument in Dreyfus’s critique was that human cognition is embodied, situated, and experiential. Another key argument was that, in contradiction to classical artificial intelligence and cognitive psychology, humans do not use symbols, but perceive their environment and make decisions using holistic processes. In particular, holistic processing is characteristic of individuals that are experts in a domain. Dreyfus, a philosopher, was more interested in providing a critique of AI based on phenomenology than in offering a detailed scientific theory, and did not develop his view in great detail, nor support it by experimental data. In another influential book, Dreyfus (Dreyfus & Dreyfus, 1988) elaborated this view and described the steps that the aspiring expert has to go through (see also Dreyfus & Dreyfus, 1984; Dreyfus & Dreyfus, 1996, 2005). In the “novice” stage, information is acquired through instruction; domain-specific facts, features, and actions are learnt. Rules are “context-free,” in the sense that their application ignores what else is happening in the environment. The “advanced beginner” stage is attained only after substantial concrete experience with the domain. Situational elements— that is, elements that depend on the context—become meaningful and are used. In the “competence” stage, decision-making procedures are organised hierarchically. While this stage is characterized by an increased level of efficiency, planning is still to a considerable extent conscious and deliberate. In the “proficiency” stage, certain features will be perceived as salient while others will be ignored. Proficient individuals, while able to “intuitively organize and understand” the problem situations, still use analytical thinking to decide what to do next. In the final, “expertise” stage, both understanding of the task and deciding what to do is intuitive and fluid. In routine situations, “experts don’t solve problems and don’t make decisions; they do what normally works” (Dreyfus & Dreyfus, 1988, pp. 30-31). Dreyfus and Dreyfus use mostly anecdotal evidence and references to the reader’s experience to buttress their theory. In the domain of nursing, Benner and her colleagues (Benner, 1984) offer some direct empirical support in favour of the theory, based on group interviews, detailed observations, and intensive personal history interviews. (See Gobet & Chassy (in press) for a discussion of nursing expertise in the light of some of the ideas discussed in the present article.) Dreyfus and Dreyfus have considered three ways in which the brain could produce intuitive behaviour based on experiences. In the first edition of Mind over Machine, they speculated that the brain could be seen as a holographic pattern recognizer. This idea was dismissed in the preface of the second edition of the book. Instead, these authors considered the possibility of using the mechanisms proposed by neural net research. This possibility was in turn dismissed in the preface of the 1992 editions of What Computers Still Can’t Do: “It looks likely that the neglected and then revived connectionist approach is merely getting its deserved chance to fail” (Dreyfus, 1992, p. xxxviii). The final possibility considered was the approach of reinforcement learning (e.g., Tesauro, 1992), but it was concluded that this approach also met with serious practical and theoretical problems (Dreyfus, 1992), although S. E. Dreyfus (2004) provides a more optimistic evaluation. While we have centred on Dreyfus’s approach, we should mention that other authors have emphasized that intuition requires holistic processing. For example, for chess, the domain discussed at length by Dreyfus (1972) and Dreyfus and Dreyfus (1988), one can mention the proposals by De Groot (1986; see also De Groot & Gobet, 1996, for an extended discussion of De Groot's view) and Linhares (2005). At the descriptive level, the theory is in line with cognitive theories proposing that novices start with verbal, analytic knowledge and slowly move to levels where knowledge becomes unconscious (Anderson, 1982; Cleveland, 1907). Although Dreyfus and Dreyfus’s account has face value validity, it also conflicts with a fair amount of empirical data. First, there is evidence that, in many domains, expertise does not imply a decrease of abstract thought and a concomitant increase in concrete thought, as proposed by Dreyfus and Dreyfus. One of the best examples is physics, where experts in fact solve problems at a deep, abstract level, while novices perform at a superficial, concrete level (Chi, Feltovich, & Glaser, 1981; Larkin et al., 1980). Second, the presence of stages in expertise development is poorly documented. In addition to the well known difficulty of empirically establishing the reality of stages (van der Maas & Molenaar, 1992), there is clear evidence that individuals may be experts in one sub-field whilst performing less fluidly in another sub-field of the same domain (Benner, 1984; Gruber & Strube, 1989; Rikers et al., 2002). Although Dreyfus and Dreyfus (1988) acknowledge that the level of expertise of one individual may vary for different problems with the same area, this would suggest that the notion of stage must not be taken literally, but only suggestively. But this seems to undermine one of the main theoretical contributions of the model. Third, a tenet of the theory, and of Dreyfus’s earlier work, is that intuition is necessary for performing at expert level in what Dreyfus (1972) calls “complex formal” and “nonformal” intelligent activities and that, being analytic, heuristicsearch computer programs cannot reach this level of performance (Dreyfus, 1972; Dreyfus & Dreyfus, 1988, e.g., Table 1.1; , 2005). (This is discussed in detail in the conclusion chapter of What Computers Can’t Do (Dreyfus, 1972); see in particular the discussion surrounding Table 1 in the Conclusion chapter.) Recent developments in computer board games, for example in chess where world champion Kasparov was beaten by Deep Blue (Campbell, Hoane, & Hsu, 2002)3 and in Othello where world champion Murakami was beaten by Logistello (Buro, 1999), show that programs using heuristic search—without any holistic understanding of positions—can perform at very high levels (see also Strom & Darden, 1996, for a similar point). Indeed, chess grandmasters are often baffled by how their intuitions can be proven false by commercially-available computer programs. It could actually be the case that computer programs may help develop a much better understanding of chess than humans have been able to achieve (Gobet, 1993). Jansen (1992; , 1992) compared human play with endgame databases and found that even grandmasters perform weakly in simple endings. Consider the endgame King-Queen vs. King-Rook, an endgame that textbooks consider as elementary and to which they devote just a few pages. Jansen found that even worldclass grandmasters made so many errors that it took, on average, four times longer than the optimal line of play to win the game. In many cases, they would have achieved only a draw instead of a win. Fourth, while Dreyfus and Dreyfus (1988) recognize that even individuals at the expert level may need to carry out analytic problem solving, they do not supply details about how the information provided by holistic intuition may be used, for example, to guide look-ahead search in a game such as chess. In addition, the role of conscious problem solving is clearly underestimated in the theory. Based on an informal experiment with a chess international master who “more than held his own” against “a slightly weaker, but master level, player” in spite of having to add dictated numbers, Dreyfus and Dreyfus (1988, p. 33) conclude that players at the expert stage can still produce “fluid and coordinated play” in spite of being “deprived of the time necessary to see problems and construct plans.” Unfortunately, not enough details are provided in Dreyfus and Dreyfus’s book to evaluate this experiment; in particular, it is unclear as to the difference in skill between the two players, whether there were behavioural differences between normal play and play with the interfering task, and, indeed, to what extent the second player was affected by the experimental setting. Well-controlled experiments with large samples (Robbins et al., 1995) have shown that a concurrent task interfering with what Baddeley (1986) calls the central executive substantially impairs the quality of the moves chosen. Robbins et al. used tactical chess positions, and one could argue that Dreyfus and Dreyfus’s point was that their master won his game solely through intuitive strategic play (roughly, position estimation and long-range planning), without using tactical play (roughly, short-term precise calculations based on thinking ahead). This seems unlikely to us, as nearly every game at master level contains moments where tactics become crucial. In addition, recent research (Chabris & Hearst, 2003; Gobet & Simon, 1996b) has clearly established that reducing thinking decreases playing skill, although one should emphasise that the level of play with grandmasters is still fairly high, and thus that some kind of pattern recognition must be involved. Thus, our disagreement is not about the importance of intuitive play at expert level, but about Dreyfus and Dreyfus’s neglect of analytical thinking—in chess, look-ahead search. Finally, evidence from neuroscience does not support the notion of holistic pattern recognition. There is now good evidence that perception proceeds sequentially, engaging specialized modules, as is shown for example by Eimer (2000) for data on face perception and O’Rourke and Holcomb (2002) for data on word perception.
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