Abstract
In some domains (meteorology, live-stock judging, chess, etc.) experts perform better than novices, and in other domains (clinical psychiatry, long-term political forecasting, financial advising, etc.) experts do not generally perform better than novices. According to empirical studies of expert performance, this is because the former but not the latter domains make available to training practitioners a direct form of learning feedback. Several philosophers resource this empirical literature to cast doubt on the quality of philosophical expertise. They claim that philosophy is like the dubious domains in that it does not make available the good, direct kind of learning feedback, and thus there are empirical grounds for doubting the epistemic quality of philosophical expertise. I examine the empirical studies that are purportedly bad news for professional philosophers. On the basis of that examination, I provide three reasons why the empirical study of non-philosophical expertise does not undermine the status of philosophical expertise. First, the non-philosophical task-types from which the critics generalize are unrepresentative of relevant philosophical task-types. Second, empirical critiques of non-philosophical experts are often made relative to the performance of linear models—a comparison that is inapt in a philosophical context. Third, the critics fail to discuss findings from the empirical study of non-philosophical expertise that have more favorable implications for the epistemic status of philosophical expertise. In addition to discussing implications for philosophical expertise, this article makes progress in the philosophical analysis of the science of expertise and expert development.
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Notes
See Shanteau (1992) for elaboration.
For example, on the question of whether expert philosophers have developed “epistemically virtuous concepts or rules” (Weinberg et al. p. 341)—concepts or rules that they allow are candidates for underwriting epistemically virtuous intuitions—WGBA press the worry that philosophers “do not receive anything like the kind of substantial feedback required for such virtuous tuning” (ibid., p. 341). As to what counts as substantial feedback, WGBA refer back to the empirical literature on expertise: “It is important to keep the relevant contrast domains firmly in mind here, for they are what will provide the meterstick by which we can evaluate just what works as the right kind of feedback, and in what needed amounts, in order to produce effective training of real expertise. The fields in which competent experts routinely develop are those like meteorology, livestock judging, and chess” (ibid., p. 341).
See Weinberg (2007) for a discussion of this explanatory burden for defenders of philosophical intuition.
Consider, for example, Devitt’s assertion that a defense of philosophical expertise “requires only that the philosophers’ intuitions be better, in general…, even if just as influenced by non-truth-tracking factors as the folk’s” (Devitt 2012, p. 22). Williamson (2011, pp. 218–219) and Sosa (2007) make similar claims. Even if this view is correct, it would not assuage the concern for philosophical expertise raised by DC. DC resources empirical data to establish a specific causal mechanism—direct or environmental feedback—for the development of enhanced expert performance, and then it claims that this mechanism is not present in the domain of philosophy. Any defense of philosophical expertise must confront this empirical challenge—it must show either that the causal mechanism is not needed for the development of virtuous philosophical expertise in the way that the critics contend or that the domain of philosophy does somehow make available direct feedback. This article develops the former type of response.
This focus is a natural fit for defenders of philosophical expertise who already acknowledge distorting influences on experts’ intuitive judgements (e.g., Williamson, Devitt, and Sosa; see fn. 6). There is also substantial overlap between this focus and how the critics frame the target of DC. Clarke’s formulation of DC, provided in the quote above, is explicit that the target is whether expert philosophers’ intuitions are accurate (Clarke 2013, p. 192). Ryberg (2013, p. 4) is also clear about the importance of looking beyond distorting effects for the purpose of evaluating the reliability of philosophical expertise. In fact, Ryberg’s version of DC—particularly its focus on what Ryberg terms “the quality assumption”—derives much of its force from the idea that trained philosophers do not know when their intuitions or theories meet this second standard. In Ryberg’s terms: “While the philosopher may have engaged in many cases of intuition-based reasoning, it seems much less plausible to hold that she has prior experiences of having made intuitive judgements which led to correct moral answers” (Ryberg 2013, p. 8). While WGBA often focus on the stability of intuitions, they invoke this second standard when discussing whether expert philosophers have developed “epistemically virtuous concepts or rules” (p. 341), philosophical theories that are “successful” (p. 342), and philosophical theories that are “key” (p. 342). Also relevant here is discussion from Alexander and Weinberg (2014) regarding different senses of the term “reliability” as used in debates over the epistemic status of philosophical intuition.
I further discuss the non-predictive aims of philosophical analyses of knowledge in Sect. 3.3.
As was the case for predictive tasks, I do not mean to claim that philosophical theories and associated intuitions can never have intervention-based aims. For instance, there are philosophical models that recommend specific interventions toward contemporary social institutions (e.g., the institutions that control biomedical research—see Reiss and Kitcher 2009). But such intervention-based philosophical theories appear mostly restricted to particular philosophical sub-domains, for example the sub-domains of applied ethics and applied logics. While these domains do not appear to be the target of DC, perhaps they are the domains in reference to which DC is most cogent.
Readers might recognize these same laboratory experiments from Fodor (1975), where they figured centrally in his argument for an innate language of thought. More generally, Fodor resourced these experiments to argue that “learning” new semantic rules did not actually increase the richness of one’s current conceptual system (see, e.g., Fodor 1980, p. 148). Whether we agree with Fodor about this, his view here serves as a warning that modeling philosophical activity in terms of sorting behavior could have radical or implausible implications.
Perhaps there are some philosophical training tasks like this, for example having students sort cases relative to the use/mention distinction? Then again, there are astrological training tasks like this too, where students sort the ecliptic according to the twelve categories of the zodiac. The comparison helps make clear that the epistemic quality worth having pertains to the status of the categories themselves rather than one’s ability to sort according to a rule.
I thank a thoughtful anonymous reviewer for this journal for indicating the importance of this concern.
Which is not to say that the success of philosophical models is not constrained by what we know about the environment—philosophical models need to be empirically adequate. As Paul explains the point, “Science still acts as a constraint upon metaphysics—the metaphysician should want her theory of the whole world to be consistent with accepted scientific theories of the world—but it should not preemptively define the role or concepts of metaphysics. That would give us an understanding of reality that is exactly the wrong way around” (pp. 6–7).
Which does not mean that the information is thereby magical or obscure. For example, Thagard’s ECHO program (Thagard 1989), which detects various parameters of what Thagard terms “explanatory coherence,” is an example of an attempt to make this general type of information computationally tractable. And as I explore in Sect. 3.3, there is some empirical support for the claim that experts improve performance by training against a non-direct or non-environmental type of informational feedback. (For those who might balk at the use of the term “information” here, it is worth flagging that philosophical accounts of natural information do not restrict information-carrying relations to causal relations—see, e.g., Dretske 1991).
Nor would it work simply to retreat to the common ground that predictive domains and philosophy share the same goal of tracking the truth. For one, this obscures the difference between the first-order and higher-order truths that informs the difference in subject matter suggested above. Second, it misses the distinction between amassing truths, on the one hand, and a successful theoretical organization of truths, on the other. A successful theoretical organization—one that separates truths that count as significant—is relative to the questions that guide the theoretical investigation (see especially Anderson 1995). As I have argued above, the questions that guide philosophical investigations are often different in kind than those that guide non-philosophical investigations.
See, e.g., Clarke: “Every professional philosopher started out as a non-philosopher, so defenders of this view are implicitly claiming that reliably accurate intuitions are acquired by a group of people who most probably started out with unreliable intuitions. We are owed an explanation of how this transformation happens” (Clarke 2013, pp. 191–192).
Related concerns about circularity (or at least explanatory breadth) are raised against researchers who widely apply probabilistic causal modeling. According to this concern, even if probabilistic causal modeling can explain how computational systems use probabilistic techniques to update hypotheses in light of new evidence, it fails to explain how computational systems generate these hypotheses in the first place (see, e.g., Goldman 2006; Christie and Gentner 2010). Chalmers et al. (1992) discuss an even more general form of this concern in terms of the “hand-coded” representations often used by artificial intelligence researchers.
At least some intuitions appear to play a role in delivering judgements about explanatory responsibilities in this sense, thus involving (depending on how reflective the intuitions are) the cognitive unconscious to this end. For example, do theories of mental content have the explanatory responsibility of accounting for, in terms of contentful mental states, the prima facie intelligent behavior of a conscious creature that was just created in a swamp lightning storm? Those who “have the intuition” that so-called Swamp-persons are a problem for the theory of teleosemantics appear to judge that theories of mental content do have this explanatory responsibility (a responsibility that teleosemantics would fail). Those who do not “share the intuition” appear to reject that theories of mental content possess this explanatory responsibility. The difference in intuition here is not about what would happen, and it is generally agreed that teleosemantics must classify Swamp-persons as not having contentful mental states (this classification resulting from the simulative component of the thought experiment, as I mention below). The difference is in whether this classification should count as a theoretical vice—about whether Swamp-persons are a proper explanandum for philosophical theories of mental content. See Neander (1996) and Millikan (1996) for a related discussion along these lines.
Empirical support for this claim can be found in Novick (1988), Clement (1982, 1986), Chi et al. (1981), Catrambone and Holyoak (1989), Lowenstein et al. (1999), Blanchette and Dunbar (2001), and Shafto and Coley (2003). See Gentner (1983) for a codification of the representational differences between attributes, relations, higher-order relations, and systematicity among relations.
More generally, Gentner and colleagues characterize children’s cognitive development in terms of their increasing ability to conceptualize domains through relational categories rather than object categories (i.e., “the relational shift”). In fact, Gentner (2003) argues that the capacity to develop relational knowledge through relational abstraction (coupled with a symbol system that can express and help develop that relational knowledge) is what distinguishes human cognition.
Consider two math students who each attempt to solve a conditional probability problem about the sale of crude oil. One student is reminded of a previous problem that is structurally similar (it is about the probability of soil erosion under certain conditions), and transfers information (perhaps unconsciously) from a knowledge schema encoding that problem to the target problem. The other student is reminded of, and transfers knowledge from (perhaps unconsciously), a previous problem that is attributionally but not structurally similar (the problem involved the quadratic equation and the density of crude oil). The former student has employed the more virtuous problem-solving strategy.
For example, young children do not uniformly encode the relation of unclehood and instead conflate this relation with a particular uncle or men of a certain age. In contrast, someone with a uniform relational encoding of unclehood possesses a portable mental predicate that can be matched across superficially different object participants. Possession of this predicate would enable one to grasp that a certain newborn baby, as well as pipe-smoking Uncle Bill, are both uncles.
The abstractions are the outputs of a domain-general cognitive mechanism, computationally modeled by Gentner and colleagues’ “Structure-Mapping Engine,” that begins with a few processing constraints and is driven by invitations to compare.
Philosophical problems might be understood through relational concepts and terms (e.g., “etiological function”, “representation”, “projectable predicate”, “counterfactual dependence”, “modus tollens”, “reliable mechanism”) or primarily through surface features, idiosyncratic object concepts, and context-based examples (e.g., a four chambered heart, the belief that tomatoes are red, green emeralds). As Gentner explains, “habitual use of a stable system of relational language can increase the probability of relational reminding. In instructional situations, it can foster appropriate principle-based reminding and transfer, and mitigate the perennial bugaboos of retrieval: inert knowledge and surface-based retrieval. The growth of technical vocabulary in experts reflects the utility of possessing a uniform relational vocabulary” (Gentner 2003, p. 209).
See Klein (1998, pp 18–21; Ch. 5).
Hogarth (2001, pp. 225–226).
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Acknowledgements
I thank two anonymous reviewers from this journal for their helpful comments and suggestions. I also thank audience members at the 2018 Central Divisional Meeting of the American Philosophical Association for feedback on research related to this article. Support for work on this article was generously provided by Bowling Green State University and the Office of the Provost in the form of Faculty Improvement Leave for Spring 2019.
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Bach, T. Why the Empirical Study of Non-philosophical Expertise Does not Undermine the Status of Philosophical Expertise. Erkenn 86, 999–1023 (2021). https://doi.org/10.1007/s10670-019-00141-2
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DOI: https://doi.org/10.1007/s10670-019-00141-2