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Neural Reuse and the Modularity of Mind: Where to Next for Modularity?

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Abstract

The leading hypothesis concerning the “reuse” or “recycling” of neural circuits builds on the assumption that evolution might prefer the redeployment of established circuits over the development of new ones. What conception of cognitive architecture can survive the evidence for this hypothesis? In particular, what sorts of “modules” are compatible with this evidence? I argue that the only likely candidates will, in effect, be the columns which Vernon Mountcastle originally hypothesized some 60 years ago, and which form part of the well-known columnar hypothesis in neuroscience—systems that cannot handle gross cognitive functions (vision, olfaction, language, etc.) as distinct from strictly exiguous subfunctions (such as aspects of edge detection, depth discrimination, etc.). This is in stark contrast to the modules postulated by much of cognitive psychology, cognitive neuropsychology, and evolutionary psychology. And yet the fate of this revised notion is unclear. The main issue confronting it is the effect of the neural network context on local function. At some point the effects of context are so strong that the degree of specialization required for modularity is not able to be met. Still, despite indications from neuroimaging that peripheral and central systems deploy shared circuitry, some skills clearly do seem to display modularization and autonomy. This article: (1) provides an in-depth analytical and historical review of the fortunes of modular thinking in cognitive science; (2) offers a systematic calibration of brain regions in terms of degrees of functional specificity and robustness; and (3) suggests another way of accounting for the partially encapsulated character of expertise and other highly practiced skills without having to resort to domain-specific modules.

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Fig. 1

(Reproduced with permission from Anderson 2010, p. 247; figure copyright Cambridge University Press)

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Notes

  1. Arguably Chomsky no longer holds to functional specialization in the sense of separate modifiability (see Zerilli 2017), but some passages in recent work suggest otherwise, e.g., intimating that Merge (the fundamental operation postulated within the so-called “Minimalist Program” in linguistics) is a self-contained generative device that dissociates from core motor operations (Berwick and Chomsky 2016, pp. 75–77).

  2. For a similar approach, see Carruthers (2006) and Zerilli (2017).

  3. “[Functional specialization] furnishes a kind of cognitive movable type for the mind, and mechanisms that can support robust laws, generalizations, and predictions (e.g., ‘forward’ inferences from cognitive tasks to brain areas) (Burnston 2016). If, for a given neural area A, there is some univocal description D such that D explains the functional role of A’s activity whenever A functions, it should be possible to formulate a theory tokening A providing ‘functional descriptions that apply over a range of instances of functioning,’ and ‘functional explanations in particular contexts that are relevant to contexts not yet explored’ (Burnston 2016, pp. 529, 531). This would be a ‘very powerful theory in terms of generalizability and projectability’ (Burnston 2016, p. 531)” (Zerilli 2017, p. 239).

  4. This usage of “exaptation” is somewhat misleading, since exaptation usually implies loss of original function (see Godfrey-Smith 2001).

  5. Of course what corresponds to a node in network neuroscience is somewhat arbitrary. For certain purposes neuroscientists may use a neuroanatomically delimited region as a node, whereas for other purposes they may use another one. I certainly would not wish to say that a module is something that depends on the interest of the neuroscientist. Additionally, the notion of a “module” in network neuroscience is different from the notion of a “node.” A module in network theory refers to a community of nodes (Zerilli 2017).

  6. I hasten to add, however, that Anderson’s (2014) “dispositional vector” account of brain regions is an alternative strategy for coming to grips with the same set of issues. Others are clearly alive to the problem. Proponents of the Leabra architecture, for instance, resist modularist terminology precisely because it “forces a binary distinction on what is fundamentally a continuum” (Petrov et al. 2010, p. 287). See also Frost et al. (2015).

  7. The scaling problem arises from the fact that as the number of neurons increases, the number of neurons that must be connected grows quadratically larger. As Gilbert (2013, p. 570) explains, “The clustering of neurons into functional groups, as in the columns of the cortex, allows the brain to minimize the number of neurons required for analyzing different attributes. If all neurons were tuned for every attribute, the resultant combinatorial explosion would require a prohibitive number of neurons.”

  8. I am heavily indebted to Anderson (2014) for the review that follows.

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This research was supported by an Australian Government Research Training Program Scholarship.

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    John Zerilli

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Zerilli, J. Neural Reuse and the Modularity of Mind: Where to Next for Modularity?. Biol Theory 14, 1–20 (2019). https://doi.org/10.1007/s13752-018-0309-7

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