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Appraisal of certain methodologies in cognitive science based on Lakatos’s methodology of scientific research programmes


Attempts to apply the mathematical tools of dynamical systems theory to cognition in a systematic way has been well under way since the early 90s and has been recognised as a “third contender” to computationalist and connectionist approaches (Eliasmith in Philos Psychol 9(4):441–463, 1996). Nevertheless, it was also realised that such an application will not lead to a solid paradigm as straightforwardly as was initially hoped (Eliasmith 1996; van Leeuwen in Minds Mach 15:271–333, 2005). In this paper I explicate a method for assessing such proposals by drawing upon Lakatos’s (in: Lakatos, Musgrave (eds) Criticism and the growth of knowledge, Cambridge University Press, London, pp 91–195, 1970) methodology of scientific research programs (hereafter: “MSRP”). MSRP focuses on the heuristics of a particular field and gauges the model/theory building stratagems by reference to theoretical and empirical progress, on the one hand, and the continuity and the autonomy of the way the field’s heuristic generates its series of models/theories, on the other. The requirement of continuity and autonomy afford distinct senses of ad hoc-ness, which serve as an effective tool to detect various subtleties which may otherwise be missed: the present approach identifies shortcomings missed by Chemero’s (Radical embodied cognitive science, The MIT Press, Cambridge, 2009) radical embodied cognitive science and falsifies Chemero’s claim that the methodological powers of his model-based account is on a par with computationalism. In general, I claim that MSRP is relevant to current methodological issues in cognitive science and can supplement debates regarding “local” assessments of methodologies, such as that between mechanical versus covering-law explanations. MSRP must at least be viewed as a necessary constraint for any methodological considerations in cognitive science.

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  1. 1.

    His theory-based approach uses Gibsonian theory as a realist background theory that provides the missing ingredient in the model-based approach. This is a more substantial offer as compared to his model-based offer but it will not be discussed in this article.

  2. 2.

    In this article I will not pursue the broader issues of Lakatos’s theory within the philosophy of science such as its status with respect to Popper’s or Kuhn’s theories, other than a few scattered remarks.

  3. 3.

    Lakatos does use the “requirement of continuous growth” as one of his titles (Lakatos 1970, p. 173).

  4. 4.

    Four different senses of ad hoc-ness will be discussed in what follows.

  5. 5.

    The sense of “novel fact” that I endorse takes into consideration the way a research programme is constructed. This implies that novelty is not restricted to temporal novelty: a well-known empirical fact might be novel for a research programme if it wasn’t taken into consideration during its construction (Zahar 1976, pp. 216–218). This sense of “novel fact” was also endorsed by Lakatos (Lakatos and Zahar 1978, pp. 184–189). A famous example is the prediction of the precession of Mercury’s orbit via general relativity.

  6. 6.

    Note that empirical progress implies theoretical progress. The distinction for stating both for progress is for emphasis rather than logical.

  7. 7.

    For an example of ad hoc2 theorizing Lakatos gives Planck’s radiation formula (Lakatos 1970, p. 175, fn. 3).

  8. 8.

    Note that this is a methodological irrefutability in the sense that it is decision based and not syntactical irrefutability. The latter are about propositions that have the logical form of “for all-there exits” (Lakatos 1970, p. 183). Such metaphysical statements are a part of the heuristic and hence more than welcome in MSRP.

  9. 9.

    Other examples of research programmes in Lakatos’s sense are relativity, quantum mechanics, Marxism, and Freudianism (Lakatos 1978, pp. 4–5).

  10. 10.

    This is why Lakatos finds Popper’s ban on content-decreasing theory generation as too weak (Lakatos 1970, p. 182).

  11. 11.

    To anticipate, in the next section I argue that the pure dynamical approach has an ad hoc3 heuristic so that according to MSRP it is not a research programme.

  12. 12.

    Note that this includes the capacity to throw up “unsolved but solvable problems” (Musgrave, p. 482).

  13. 13.

    The way this proceeded was very similar to Lakatos’s rendering of Newton’s programme (see his quote in Sect. 2.2).

  14. 14.

    The latter requires extensive historical case studies of particular experiments and their interpretations regarding predicting novel facts and corroborations.

  15. 15.

    van Leeuwen gives Giunti’s (Giunti 1991) “cookbook” for producing such models which is more detailed and technical than the Chemero’s one I delineate here but the main steps are almost identical (van Leeuwen, pp. 299–300).

  16. 16.

    Note that although Chemero uses Clark’s arguments regarding the types of heuristics of 19th century physicists, he does not use the full MSRP context in which Clark develops them.

  17. 17.

    The equation was dϕ/dt = − A sin(ϕ) − 2B sin(), where A and B are called the “parameters” of the equation. The ratio B/A is called the “control parameter” and depends on the frequency of the wagging (Chemero, p. 88).

  18. 18.

    Although the later Chemero is not this explicit, HKB’s research strategy, as delineated by him there (Chemero, p. 88), belongs to the strategy of pure dynamical approach (Chemero, p. 82). One important issue regarding HKB itself for the purposes of this paper is that the HKB’s original paper spends a considerable period of time deriving the potential equation from equations of the individual hands and their coupling (HKB, pp. 351–353). These equations are non-linear, dissipative oscillator equations and such a derivation implies a connection with the rest of physics, all the way to far from equilibrium thermodynamics. This in turn means that a hope for continuity exists, at least in principle. But Chemero gives only a passing remark about this aspect of HKB (Chemero, p. 86) and later, after giving the extensions of HKB that are based on the modifications of the potential function, says that “the vague suggestion concerning what coordinated structures are bore fruit in more concrete suggestions concerning how such structures were to be modeled” (Chemero, p. 96). I take it that this shows how different methodological viewpoints result in focusing on different stages of a particular research. For me the unification possibility offered by deriving the potential function is much more encompassing than the one obtained by simply beginning with the potential function (which can be obtained by pure data fitting) and investigating its possible modifications and extensions. Note also that this latter aspect is very similar to what Boltzmann calls the “mathematical phenomenology” whose methodology is even weaker than the “general phenomenology” (Boltzmann, p. 250).

  19. 19.

    That is to V = − acosφ − bcos2φ they added another cosine, so that the new V’ = − acosφ − bcos2φ − ccos3φ.

  20. 20.

    Including Turing machines (Asarin and Maler 1994). Note also that dynamical systems have more power than Turing machines (Bournez and Cosnard 1996).

  21. 21.

    Note that MSRP’s neutrality also holds for charges accusing the dynamical view as being metaphorical only (Eliasmith 2003) and also for cases defending its metaphorical aspect (Thelen and Smith 1994).

  22. 22.

    Does this mean that the best heuristics are intrinsically tied to postulating underlying entities (as we have seen with atomism and representationalism)? Can we have other ways which are just as strong? I do not think Lakatos’s framework places any necessary restriction on this. For example, Einstein’s new physics can be seen to have a strong heuristic and the heuristic that makes it so are invariance properties of whatever rule one has together with obtaining a classical law as the speed of light goes to infinity (Zahar, pp. 237–262).

  23. 23.

    For further precursors see Agassi (1964) and Watkins (1958); Lakatos claims to go much further in giving metaphysics a more determining role (Lakatos 1970, p. 184).

  24. 24.

    The interactivist model of representation (Bickhard 2009) is part of a kind of naturalism which has a process based metaphysics and an evolutionary epistemology (Bickhard in preparation) where representation emerges as anticipatory processes within intrinsically normative processes. For the appraisal of the interactivist research programme and the claim that methodologically it is on a par with computationalism see Erdin (forthcoming b).

  25. 25.

    For Chemero’s Gibsonian based offer and its critique using MSRP, see Erdin (forthcoming a).


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I would like to thank Lucas Thorpe, Mark Bickhard, and Kenneth Westphal for comments and advice on drafts of this article. I would also like to thank the referees for their constructive criticisms. This work is supported by the BAP project 13840(18B02D1).

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Correspondence to Haydar Oğuz Erdin.

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Erdin, H.O. Appraisal of certain methodologies in cognitive science based on Lakatos’s methodology of scientific research programmes. Synthese 199, 89–112 (2021).

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  • Representation
  • Anti-representation
  • Dynamical systems
  • Cognitive Science
  • Models
  • Radical embodied cognitive science
  • Computationalism
  • The methodology of scientific research programmes
  • History of physics
  • Atomism
  • Phenomenological thermodynamics
  • Heuristic
  • Guide to discovery
  • Fact dependency
  • Unification
  • Autonomy
  • Continuity in science
  • The requirement of autonomous continuous growth