Cognitive systems and the supersized mind
Robert D. Rupert
Published online: 26 August 2010
Springer Science+Business Media B.V. 2010
In Supersizing the mind: Embodiment, action, and cognitive extension (Clark 2008),
Andy Clark bolsters his case for the extended mind thesis and casts a critical eye on
some related views for which he has less enthusiasm. To these ends, the book
canvasses a wide range of empirical results concerning the subtle manner in which
the human organism and its environment interact in the production of intelligent
behavior. This fascinating research notwithstanding, Supersizing does little to
assuage my skepticism about the hypotheses of extended cognition and extended
mind. In particular, Supersizing fails to make the case for the extended view as a
revolutionary thesis in the theoretical foundations of cognitive science.
1 Clark’s case for extension
The primary theme of Chapter 1 represents one of the book’s most important
conceptual threads: the idea of information self-structuring. Here is one version of
the thesis, having particularly to do with perceptual information:
The embodied agent is empowered to use active sensing and perceptual
coupling in ways that simplify neural problem solving by making the most of
environmental opportunities and information freely available in the optic
array. (p. 17)
1
This sort of active sensing comes in a variety of forms, but two aspects of it are
central to Clark’s presentation: (1) that the cognitive system learns more efficiently
R. D. Rupert (&)
Department of Philosophy, University of Colorado at Boulder,
Campus Box 232, Boulder, CO 80309-0232, USA
e-mail: robert.rupert@colorado.edu
1
All page references are to Clark (2008) unless otherwise noted.
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Philos Stud (2011) 152:427–436
DOI 10.1007/s11098-010-9600-6
by detecting correlations between its self-generated movement and the resulting
perceptual or kinesthetic signals and (2) that the agent intentionally moves so as to
try to produce data that exhibit such correlations.
I see little connection here to the extended view—the view that human cognition
literally comprises states, property instances, or processes beyond the boundary of
the organism. The correlations in question hold between structures within the
organism; in Clark’s examples, the events that constitute learning all amount to the
recording of correlated patterns of activity within the organism or, in cases of AI,
within a neatly bounded artificial system. Surely external material plays a historical
role in producing those traces (cf. Rupert 1998), but Clark does not take the
extended view to be a thesis about the subject’s history of causal interaction with the
environment (p. xxvii). What, though, is the role of external material as it
contributes to learning via informational self-structuring, if not historical?
Rupert (2004) distinguishes between HEC—the hypothesis of extended cogni-
tion—and HEMC—the hypothesis of embedded cognition. The former is the
extended view as described above. The latter, HEMC, holds that the human
cognitive system is organismically bounded but that it interacts to a surprising
extent with external materials in the course of its cognitive processing. While
reading Supersizing, I repeatedly found myself thinking that Clark had provided
clear examples of HEMC-based, but not HEC-based, cognitive processing. Here is
Clark, quoting Lungarella and Sporns: ‘‘the agent’s control architecture (e.g.
nervous system) attends to and processes streams of sensory stimulation, and
ultimately generates sequences of motor actions which in turn guide the further
production and selection of sensory information’’ (p. 17). The control architecture
issues motor commands and, as a result, indirectly produces sensory stimulation—
and the commands, the stimulation, and the resulting correlations between them are
all internal. Clark goes on to describe research by Fitzpatrick and Arsenio that
involves ‘‘the cross-modal binding of incoming signals’’ (p. 18); but these are
incoming signals in the standard sense: they enter into a robot’s computational
system through peripheral sensory channels (or are produced internally via
proprioception). Over the following pages (pp. 19–21), this theme recurs in a
handful of further examples, always to the same effect. A similar diagnosis applies
to the later discussion of sensorimotor contingencies (p. 23) (as well as the
discussion of sensory surrogates [pp. 35–36]). What is it to learn such contingen-
cies? It is to have the physical materials of one’s body, mostly one’s brain, altered in
certain respects. This is clearly an internalist view, HEMC, not HEC.
Chapter 2 introduces the idea of a ‘negotiable body’: under certain conditions, the
brain incorporates external elements into the body schema, treating these as part of
the subject’s own body. For instance, neurons in macaques trained to retrieve food
using rakes take on new receptive fields, suggesting that trained macaques’ brains
treat the rakes as extensions of the monkeys’ own hands (p. 38). Prior to training,
certain bimodal neurons are distinctively sensitive both to touch on a particular area
of the hand and to visual stimulus of an object approaching that same part of the
hand. After training, these neurons are specially sensitive to visual stimulus of
objects in the vicinity of the rake head, in the way they previously had been to
visually presented objects near the relevant portion of the hand.
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In these cases, the cognitive story seems to me to be wholly nonextended; in fact,
this seems to follow from the very nature of the evidence at issue. Research on
neurons in macaques’ intraparietal sulcus may show that macaques represent their
bodily boundaries differently after being trained to collect food with a rake, but to
the extent that the research shows this, it does so by showing that macaques use
neural resources to represent their bodies in a new way; and neural resources are, of
course, inside the organism. Internal, neural resources represent bodily boundaries,
track ongoing activity of the body, and send motor commands to ‘‘body’’ parts,
whether or not the parts so commanded are components of the organism.
To be fair, Chapter 2 contains intimations of at least two further arguments, one
phenomenological, the other broadly evolutionary. I leave discussion of these
mostly to other venues (see Rupert 2009b, Chapters 7 and 8; Rupert 2009c). One
version of the evolutionary argument focuses on environmental tailoring or suited-
ness and is particularly related to results in cognitive science; so, I say a bit about it
here. This argument appeals to the role of representational resources: ‘‘[T]he effect
of extended problem-solving practice may often be to install a kind of motor-
informational tuning such that repeated calls to epistemic actions become built into
the very heart of many of our daily cognitive routines. Such calls do not then depend
on…representing the fact that such and such information is available by such and
such a motor act’’ (p. 75). The idea seems to be that, if a fact about the world is not
explicitly represented, yet some cognitive process functions properly only when that
fact holds, then the part of the world constituting that fact becomes a literal part of
the cognitive process.
This is curious style of argument, resting as it does on one of the central insights
of the embedded view: that certain heuristics employed by the local computational
(or connectionist, or dynamical) system are valid only when employed in an
environment of a certain sort (McClamrock 1995; Gigerenzer 2000). Moreover, it
seems quite sensible to say that the cognitive system adjusts—either developmen-
tally or evolutionarily—to its environment. This, however, presupposes the
existence of a cognitive system that is becoming so suited. To take the tailoring
process to bring into existence a further cognitive system serves no purpose.
Compare: As one climbs a very high mountain, one’s breathing adjusts to the
changes in atmospheric pressure and density, but this provides no reason to
introduce a new biological unit, the organism-plus-atmospheric-pressure-and-
density. Otherwise indispensable theoretical constructs—the organism, its proper-
ties, and the ways in which they interact with environmental factors—do all of the
necessary explanatory work.
Another theme touched on briefly in Chapter 2 is that of transformation: the
appearance of ‘‘novel properties of the new systemic wholes’’ (p. 33) at work in
extended cognitive processing. Chapter 3 explores this idea to a much greater
extent, with regard to the transformational contribution of external codes (that is,
public languages and other systems of external symbols, such as mathematical
symbols—pp. 50–53). Clark argues that these material symbols transform human
cognition (pp. 50, 57), conferring upon humans a wide range of capacities
distinctive of human intelligence. It is, for example, only by being able to represent
our own thoughts that we humans become able to think about our own thoughts, an
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ability at the root of many of our impressive cognitive achievements (p. 58); and on
Clark’s view, we become able to represent our own thoughts only because an
external code is available.
This observation does not seem to support HEC. The contributions in question
appear to ground only a historical, causal account of the effects of external codes on
cognition. An entirely orthodox view is in the offing, then: elements in the external
code cause the activation of various mental representations, including representa-
tions of external sounds and inscriptions; these internal representations participate in
internal cognitive processing.
Why should Clark object to this relatively mundane, internalist view? After all,
Clark asserts that, in the important case of number words, ‘‘there is (at least) an
internal representation of the numeral, of the word form, and of the phonetics’’
(p. 52). This, however, recognizes the essential representational materials posited by
a typical internalist approach. Clark’s objections to the internalist story seem to be
that internal representations of words are ‘‘shallow, imagistic inner encodings’’
(p. 238; p. 53) and not, individually, ‘‘fully content-providing’’ (p. 52). It is not
clear, however, in what way this conflicts with the internalist standpoint. Consider,
for example, that computational models commonly incorporate pointers (Newell
and Simon 1997/1976), which seem about as shallow as mental representations get;
thus, the shallowness of mental representations of external symbols does not conflict
with orthodox approaches in cognitive science. Neither does the imagistic nature of
representations of public symbols. Computational primitives need not take any
particular form, so long as they’re treated as primitives by the computational
system. Thus, there is no reason a computational primitive cannot possess pictorial
or imagistic properties. So long as the imagistic properties play no role in cognitive
processing, then a computational account of that process remains as viable as ever.
But, what if the particular form—the physical implementation or realizer—of a
given mental representation (individuated in terms of its content) varies from
subject to subject (say, from the speaker of one language to the next)? That is, what
if two subjects form substantially different shallow, imagistic representations of
number words with the same content (both referring, for instance, to 98)? Won’t the
imagistic features of the representations govern the subjects’ responses in at least
some circumstances? Perhaps, but that shows only that computationalism leaves
something out, not that there is anything extended about the story. It is one thing to
say that certain behavioral variables are distinctively affected by a vehicle’s
imagistic properties; it is quite another to hold that the vehicle itself is external. In
the standard language-based case, the vehicle with imagistic properties is still an
internal vehicle.
With regard to something’s being ‘‘fully content-providing,’’ the reader should
ask for clarification. Does Clark think that every genuine Mentalese symbol must
enter into all of the internal relations that might be relevant to any processing
concerning what we might take to be represented by that symbol? That the mind
contains modules, computing in a proprietary code, has been a highly influential
view in orthodox cognitive science (Fodor 1983). It is virtually guaranteed that in
any such architecture there will be at least two distinct symbols (that is, mental
representations over which computations are performed) with the same referent;
430 R. D. Rupert
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moreover, it is virtually guaranteed that neither of these symbols is fully content-
providing, simply because, by the nature of the architecture, one of the symbols
(say, inside the module) enters into computational processes that the other symbol
(in central processing, say, or in a different module) doesn’t. Given this, it is no
departure from orthodox, internalist cognitive science to introduce mental represen-
tations that fail to be fully content-providing.
Clark is impressed also by the way in which external symbols can, when
immediately present, seem to play an active, attention-directing role in cognition
(pp. 48, 57). I’m inclined to think words do play such a role, but that they do it via
the activation of internal representations. Consider a recurring example drawn from
the work of Dana Ballard and his associates (Ballard et al. 1997). Subjects are
shown a pattern of colored blocks—the target—and are given various colored
blocks as resources to use to replicate the target. Ballard et al. showed that subjects
often (but nothing close to exclusively) use a strategy that relies more on looking
back and forth than it does on the committing of lots of information about the target
to internal memory.
We should not, however, misinterpret these results. The experiments do not show
that subjects don’t rely on mental representations of block colors or positions. To the
contrary, one of the commonly used strategies (the P–D strategy—Ballard et al. 1997,
p. 732) relies heavily on internal memory. Moreover, even on the least memory-
intensive strategy—the one that involves the most looking back and forth—the deictic
pointers used by subjects must represent the colors of the external blocks or their
positions, even if only one block and one property at a time. What’s interesting about
visual pointers is the dynamic reassignment of them to the job of representing various
external things, positions, or colors. Each time one is ‘‘reassigned,’’ however, it must
be bound to standing representations of properties, or else it is useless in the copying
task. Comparing two bare pointers to each other or comparing one bare pointer
(aimed, for instance, at the color of a block the subject has just attended to) to the color
of a block in the resource pool does not do the subject any good. The subject must be
able to ‘‘decide’’ whether the pointer and the visual representation of the color of the
block to which she is currently attending (while looking at a candidate block in the
resource pool) are the same, so that she can pick up the correct block. This requires
binding the pointer to an external object but also to an internal representation of its
color. After all, a bare pointer has no content, so the use of it alone would not guide the
subject to pick up a block of one color, rather than a different one, from the resource
pool. Ballard et al. do not deny this; rather, it’s built into their approach (Ballard et al.
(1997, p. 725).
Return now to the case of words. When reading, some words differentially capture
the subject’s attention. Nevertheless, it’s reasonably clear that mental representations
of words commonly contribute to cognitive processing in the absence of the actual
units: during literature exams, students routinely produces names of characters and
descriptions of settings, without having the text at hand. So, there is independent
reason to posit internal mental representations activated in subjects while reading. In
which case, the attention-directing role of external resources begins to look pretty
humdrum: when one looks at a given word, it ‘‘directs one’s attention’’ by causing the
activation of an internal representation of that word.
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