Negotiating Computation & Enaction: Rules of Engagement

In July, PAICSers Adrian Downey and Jonny Lee (with Joe Dewhurst) organised an international conference at Sussex entitled “Computation & Representation in Cognitive Science: Enactivism, Ecological Psychology & Cybernetics”.  It was an excellent meeting, with boundary-pushing talks from:

  • Anthony Chemero (University of Cincinnati)
  • Ron Chrisley (University of Sussex)
  • Sabrina Golonka (Leeds Beckett University)
  • Alistair Isaac (University of Edinburgh)
  • Adam Linson (University of Dundee)
  • Marcin Miłkowski (Polish Academy of Sciences)
  • Nico Orlandi (UC Santa Cruz)
  • Mario Villalobos (Universidad de Tarapaca)

I never wrote an abstract for my talk, so below I include the handout instead.  But it probably only makes sense for those who heard the talk (and even then…).

Negotiating Computation & Enaction: Rules of Engagement

Ron Chrisley, University of Sussex, July 10th 2017

Disputes concerning computation and enaction typically centre on (rejection of) computationalism with respect to cognition:  the claim that cognition is computation.

I: Rules of engagement

I propose the following “rules of engagement” (numbered items, below) as a way to prevent talking past one another, arguing against straw men, etc. and instead make progress on the issues that matter. No doubt they fail to be theory neutral, strictly speaking.  And there are of course other principles that are at least as important to adhere to.  But it’s a start.

The proposals can be seen as specific instances of the general admonition: clarify the computationalist claim at issue (cf end of Grush’s review of Ramsey’s Representation Reconsidered).

But even more so:  Many different versions of the claim that “cognition is computation”, depending on the choices of the following variables:

Computationalism schema: {Some|all} cognition {in humans|others} is {actually|necessarily|possibly} {wholly|partly} {best explained in terms of} computation

So need to clarify A) the relation between them, as well as B) cognition and C) computation themselves

A: Relation?

The instantiation of this that I consider most interesting/likely to be true is given below, but for now, start with this:

  1. Computationalism is best understood as an epistemological claim

That is, I plump for the “is best explained in terms of” rather than the simple “is” version of the schema above, as it is the version with the most direct methodological, experimental, even theoretical import (we’ll see the upshot of this later).

B: Cognition?

Given my background in AI and Philosophy (rather than Psychology or Neuroscience), I am interested in cognition in its most general sense: cognition as it could be, not (just) cognition as it is.  Thus:

  1. Avoid inferences such as: “X is involved in (all cases of) human cognition, therefore X is essential to cognition”

Compare flight and feathers.

An interesting case is when computation is not the best account for some particular kind of cognition, yet only it can account for that and some other kind of cognition.

C: Computation?

  1. We should demand no higher rigour/objectvity for computational concepts than we do for other, accepted scientific concepts
  2. Avoid the reductionism Catch-22 (mainly for representation)

That is, some authors seem to insist both that:

  • A notion of representation must be reduceable to something else (preferably non-intentional) to be naturalistically acceptable
  • Any notion of representation that is reduceable to something else can be replaced by that something else and therefore is surplus to requirements.
  1. Be aware that there are distinct construals (inter-theoretic) and varieties (intra-theoretic) of computation
  • Construals: computation as running some program, operation of a Turing machine, mechanistic account (Milkowski, Piccinini), semantic account, etc.
  • Varieties: digital, analogue, quantum, connectionist, etc.

E.g., if you are arguing against a view in which either everything is computational, or there is only one kind of computation, you are unlikely to persuade a nuanced computationalist.

  1. There are computers, and computers are best explained computationally

Does a given account have the implication that computers are not computational? Or that there are no computers?  Prima facie, these should count as points against that account.

And even so, what is to stop someone from claiming that whatever concepts provide the best account of the things we intuitively (but incorrectly?) called computers, also play a role in the best account of mind?  Cf Transparent computationalism (Chrisley)

On the other hand, do not be so inclusive as to trivialise the notion of computation: pan-computationalism?

  • Actually that’s not the problem with pan-computationalism
  • The real problem is that has difficulty explaining what is specifically computational about computers (beyond universality)

Computationalism (C): the best explanation (of at least some cases) of cognition will involve (among others) computational concepts (that is, concepts that play a key role in our best explanation of (some) computers, qua computers).

  1. So even if computation is only necessary for the explanation of some kinds of cognition, C is still vindicated.

II Examples

Consider two kinds of arguments against computationalism: those that rely on enactivism, and those that do not (but which some enactivists rely on)

These summaries are probably unfair, and likely violate corresponding “rules of engagement” concerning enactivism, etc.

Enactivist arguments

  • Enactivism 1:  Self-maintenance
    • E: The operation of a computer/robot, no matter how behaviourally/functionally similar to a human, would not be sufficient for cognition because not alive/self-maintenant
      • E has same skeptical problems as Zombie/Swampman positions
      • Note:  similarity to human is misleading – it may be that given its materiality, a computer would have to be very behaviourally/functionally different from a human in order to cognise (2)
    • Why believe E? Because:
  1. a) meaning for computers is not intrinsic; and
  2. b) possession of intrinsic meaning is necessary for (explaining?) cognition
  • Why believe b)?
    • Much of our meaning is imposed from outside/others?
    • Even if one accepts b), only follows that computation can’t explain *all* cognition? (7)
    • Even if human cognition has an “intrinsic meaning” core, does that rule out the possibility of cognition that does not? (2)
  • Why believe a)?
    • Reason 1:  Because any meaning in computers is imposed from the outside
      • But why should that preclude that system might have, partially by virtue of its computational properties, (either distinct or coinciding) intrinsic meaning, in addition? (5)
      • Might living systems be examples of such?
    • Reason 2: Because:
  1. c) intrinsic meaning (only) comes from being alive; and
  2. d) computers are not alive
  • Why believe d)? (given behavioural/functional identity with living systems)
    • Because computers are made of non-living material: they don’t, e.g. metabolise
      • By definition?  Could they? (5)
      • But so are cells: the parts of cells don’t metabolise
      • Because computers are not hierarchies of living systems
        • So they have meaning, just not human meaning? (2, 7)
        • What if we hierarchically arrange them?  Why would their computational nature cease to be explanatorily relevant?
      • Why believe c)?
        • Enactivist meaning is imposed by theorist’s valuing of self-maintenance (3)
      • In any event: E is not enough to defeat C — need to show computation is not necessary
  • Enactivism 2a: Basic Minds (Hutto and Myin)
    • Computation involves representation
      • Contentious (e.g., mechanistic account) (5)
    • Representation requires content
    • There is a level of basic mind that does not involve content
    • Therefore computationalism is false
      • At most only shows that there must be a non-computational explanation of the most basic forms of cognition (7)
      • But actually one can have a non-contentful, yet intentional, notion of computation:  robust correspondence (5)
    • Enactivism 2b: Content  (Hutto and Myin)
      • Computation involves representation
        • As above
      • Representation requires content
      • There is a “hard problem of content” (HPC):  no naturalistically acceptable theory of sub-personal content
      • Therefore computationalism is false
        • Impatience: Even if true, lack of a theory is not decisive (3)
        • Some argue the “hard” problem has already been solved, long ago (Milkowski)
      • Enactivism(?) 3:  Fictionalism (after Downey, with apologies)
        • Computation involves representation
        • Although representation is useful for explaining cognition, utility doesn’t imply metaphysical truth
        • Further, considerations like HPC argue against representation, and therefore computation
        • So computationalism is (metaphysically) false
          • Relies on argument Enactivism 2b – see rejoinders above
          • Only tells against computation construed as a metaphysical claim — not a problem for C
          • Yet C, being epistemic/pragmatic, is the one that matters (1)

Non-enactivist arguments against computationalism

(to which enactivists sometimes appeal):

  • Chinese room (Searle)
    • Many objections
    • Against Strong AI (metaphysics), not against C (1)
    • Against sufficiency, not against C (7)
    • Enactivist irony: emphasising the fundamental differences between living/conscious systems and those that are not (such as Macs and PCs) allows one to question the idea that a human (Searle) can perform the same computations as those
      • Proposal: Human has different, intentionality-sensitive counterfactuals that “dead” silicon does not (5)
      • Upshot: Non-living nature of machines is a feature, not a bug — immunizes machine computation to Chinese room critique
  • Diagonalisation (e.g., Penrose)
    • G: Human mathematicians are not computing a (knowably sound) Turing-computable function when ascertaining the truth of certain mathematical propositions
    • Many objections
    • But even if the argument works, it does not impact on C, since X does not need to compute the same functions as Y for X to explain Y
    • That is, C is epistemic, not metaphysical (1)
  • Non-objectivity of computation (Putnam, Searle)
    • Anything can be seen as implementing any Turing machine
    • On some accounts, not all TM instantiations are computers (need intentionality) (5)
    • But fails, even for TMs:  counterfactuals
    • More recently, some (Maudlin, Bishop) have argued that to be explanatory, computational states can only supervene on occurrent physical states, not counterfactual ones.
      • But some occurrent states are individuated by their counterfactual properties
      • Counterfactual properties supervene on occurent state
      • Also: seems to imply computational concepts are not suitable for explaining computers (6)
    • Phenomenology (Dreyfus)
      • E. g., experts don’t engage in search algorithms when, e.g., playing chess – they just see directly the right moves.
      • Makes unfounded assumptions about what it feels like to be this or that kind of physical (computational) system
      • E. g., a (sub-personal) computation that involves millions of steps may realise an experience with no such complex structure, even skilled coping
      • But even if Dreyfus is right, does not refute C (7)
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