Gualtiero Piccinini’s Research
NB:
Comments on my work are always welcome
Updated: January
2008
My research covers several areas, such as computational theories of
mind, cognitive neuroscience, intentionality, and consciousness. The goal is to understand the mind, so all
the pieces are connected in various ways.
My work may be approximately organized within the following categories.
·
Background
of Computationalism
·
The
Mechanistic Account of Computing Mechanisms
·
Reformulating
Computationalism
·
From
Cognitive Science to Cognitive Neuroscience
·
Intentionality
·
Consciousness
and Introspection
I
have devoted considerable energy to understanding the original motivation of
computational theories of mind. The
motivation is to find a mechanistic explanation of mental capacities. It is important that we identify the relevant
notion of mechanistic explanation. I
think the notion that serves our purposes is roughly the one worked out by Carl Craver and other
philosophers of science. But in order to
show that mechanistic explanation in the relevant sense is what
computationalists are after, we also need to dispel a number of confusions that
have permeated the literature.
In
Alan Turing and the
Mathematical Objection, I reconstruct Turing’s (usually misunderstood)
reply to the mathematical objection.
(Jack Copeland invited me to present this paper at a workshop on
hypercomputation in
In
Allen Newell (forthcoming in New Dictionary of
Scientific Biography, Thomson Gale), I briefly reconstruct the main ideas
and contributions of one of the greatest and most sophisticated champions of
the computational theory of mind.
In
The First
Computational Theory of Mind and Brain: A Close Look at McCulloch and Pitts’s
‘Logical Calculus of Ideas Immanent in Nervous Activity’ (in Synthese, 2004), I argue that contrary
to popular belief, the first computational theory of mind and brain is not due
to Turing but to McCulloch and Pitts. I
investigate the enormous impact their theory had, both positively, in offering
a mechanistic explanation of mental capacities, and negatively, in introducing
the mistaken idea that appealing to computation automatically explains the
intentionality of mental states. The
mixture of computationalism and semantic language that one finds in McCulloch
and Pitts carried over in the philosophical literature, where most authors
believe computational states are individuated by their semantic properties. I call this the semantic view of computation.
I
look at the sources of the semantic view of computation in Functionalism,
Computationalism, and Mental Contents (in Canadian Journal of Philosophy, 2004), where I argue that the
semantic view of computation is untenable and question begging, and should be
replaced by a non-semantic view of computation.
But
a non-semantic view of computation is workable only if we don’t conflate
functional/mechanistic descriptions and computational ones, as is customarily
done in the literature. In Functionalism,
Computationalism, and Mental States (in Studies
in the History and Philosophy of Science, 2004), I identify the origin of
this conflation and attempt to eradicate it.
One of my conclusions is that computational descriptions are only one
kind of functional/mechanistic descriptions among others. In this paper, I also discuss and reject the
popular view that there is a useful sense in which everything performs
computations.
I
am developing a novel account of computing mechanisms, the mechanistic
account, which integrates conceptual resources from computability theory,
computer design, and philosophy of science. According to the mechanistic
account, computing mechanisms have the function of generating output strings of
digits from input strings of digits (and perhaps internal states), in
accordance with a general rule that applies to all inputs and depends on the
inputs for its application. The key word
here is “function”: computing mechanisms
are individuated by their functional (non-semantic) properties, and functional
properties are specified by mechanistic explanations (roughly in Carl Craver’s
sense).
In Computation without
Representation (forthcoming in Philosophical
Studies), I formulate the mechanistic account with respect to computational
states, defend it on the grounds of computer science’s explanatory practices,
and criticize existing arguments for the semantic account of computation.
In Computing Mechanisms (in Philosophy of Science, 2007), I
formulate the mechanistic account more generally in terms of computing
mechanisms and their mechanistic explanation, and argue that it is superior to
other accounts of computing mechanisms because it has a number of desirable
features.
In Computers, I argue that contrary to what
many philosophers have maintained, there is a principled distinction between
computers properly so called and other computing mechanisms, and I use the
mechanistic account to draw such a distinction in terms of their functional
properties. I also exhibit the
fruitfulness of the mechanistic account by analyzing in some detail some of the
theoretically important functional differences between computers, such as
programmability vs. program control, special purpose vs. general purpose,
parallel vs. serial, and analog vs. digital.
There are quite a few surprises for some orthodox views here.
In Some Neural
Networks Compute, Others Don’t (forthcoming in Neural Networks), I look
more closely at connectionist systems and the sense in which they compute. Connectionist systems are often invoked as
the most plausible model of neural and cognitive mechanisms. Yet there is considerable confusion as to
whether connectionism is a computational framework. I show how the mechanistic account naturally
accommodates paradigmatic connectionist systems and sheds light on the way in
which they compute. I also draw a
distinction between connectionist systems that compute in a classical way,
connectionist systems that compute in a non-classical way, and connectionist
systems that don’t compute at all. I
believe brains fall into the last class.
In The Physical Church-Turing
Thesis: Modest or Bold? I try to shed light on the Physical Church-Turing
thesis, not to be confused with the original thesis put forward by Church and
Turing. I argue that existing
formulations of the Physical Church-Turing thesis are too strong and too little
concerned with practical usefulness to be relevant to the notion of computability
that motivates the (original) Church-Turing thesis and the discipline of
computer science. Such formulations
should be replaced by a more modest but more pertinent formulation, according
to which anything that can be physically computed (in a sense specified in
terms of usability) can be computed by some Turing machine. Besides being more pertinent to
computability, this is a more plausible thesis than traditional formulations,
although its truth value remains unknown.
With
the mechanistic account in hand, we can go back to computationalism and begin
to reformulate and resolve some old disputes.
A first step is to appreciate that computational theories of mind and
brain have great elegance and explanatory power. According to the mechanistic account,
computational theories are bona fide mechanistic theories, explaining the
capacities of a system in terms of their components and their functional
organization. Different versions of the
computational theory differ in the kinds of mechanisms they postulate, and
their explanatory power is a function of the capacities that can be exhibited
by the mechanisms that they postulate.
In this respect, for instance, paradigmatic “connectionist” theories are
less explanatorily powerful than paradigmatic “classical” theories.
In The Mind as the Software of the Brain?
Revisiting Functionalism, Computationalism, and Computational Functionalism
(forthcoming in Philosophy and
Phenomenological Research), I clarify the relationship between
functionalism, computationalism, and computational functionalism. I give clear content to the idea that
functionalism and computationalism are logically independent views. Functionalism is the view that the mind is
the functional organization of the brain; computationalism is the view that the
functional organization of the brain is computational; and computational
functionalism is the conjunction of the two.
I explicate all the relevant notions, including functional organization,
in mechanistic terms. Thus, this paper
offers a novel formulation of functionalism, which I call mechanistic
functionalism.
In Computationalism,
the Church-Turing Thesis, and the Church-Turing Fallacy (in Synthese, 2007), I address the main
arguments to the effect that the Church-Turing thesis entails
computationalism. Although these
arguments turn out to be unsound, I do point out some important connections
between computation, the Church-Turing thesis, and theories of mind and brain.
In Computational Modeling vs. Computational
Explanation: Is Everything a Turing Machine, and Does It Matter to the
Philosophy of Mind? (in Australasian
J Phil, 2007) I discuss in detail pancomputationalism—the view that
everything performs computations.
Believe it or not, pancomputationalism has been defended or at least
endorsed in some form by a large number of distinguished philosophers (and
scientists), including Hilary Putnam, David Chalmers, and John Searle. This is largely because until now, no one has
produced an adequate account of the difference between things that compute and
things that don’t. To remedy this, I
offer an account of the distinction between computational explanation
(explaining a phenomenon in terms of the computations performed by the
mechanism that is responsible for the phenomenon) and computational modeling
(describing a phenomenon using computations).
By using this distinction, I dismiss the view that everything performs
computations as either trivial or false.
From Cognitive Science to Cognitive Neuroscience
The conceptual relations between
computational explanations of mind and neighboring topics, such as modeling the
mind, explaining the mind mechanistically, and understanding the brain, are not
always carefully and correctly laid out.
I clarify some of these relations in Computational
Explanation in Neuroscience (in Synthese,
2006). The same essay also introduces
some relevant articles in the same issue of Synthese,
which I guest-edited. The articles are
by Carl Craver, Frances Egan and Robert Matthews, Oron Shagrir, and Rick Grush.
The mechanistic account of
computing mechanisms can be used to formulate computational theories sufficiently
precisely as to make them testable on the grounds of evidence from
neuroscience. When this is done, I
believe there is no (nontrivial) computational theory that survives the
empirical test. I give a brief analysis
of the notion of computational explanation in neuroscience in Computational
Explanation and Mechanistic Explanation of Mind (in Cartographies of the Mind: The Interface between Philosophy and
Cognitive Science, edited by de Caro et al., 2007). A first pass at my argument against
computational theories of mind and brain is in Symbols,
Strings, and Spikes. The conclusion
is that computationalism is only one kind of mechanistic explanation of mind
among other possible ones, and it’s not the one that fits the neuroscientific
evidence. Since computational theories
have been the mainstream over the last decades, we need new ways to think mechanistically
about the mind, and I think we can find them in cognitive neuroscience.
At this point, some
philosophers will be tempted to reply that my attempt to use neuroscience
against computationalism is wrong-headed, because computational theories are at
a different level of description than that of neural mechanisms. According to this reply, computational
theories are psychological descriptions, and psychology is autonomous from
neuroscience. I agree that psychology is
autonomous from neuroscience in many respects, but it doesn’t follow that
computationalism is not refutable by neuroscience. In fact, any nontrivial computational theory
of mind is committed to the existence of appropriate mechanisms that realize
the computations. If those mechanisms
are not found in the brain, then computationalism is refuted, at least for
biological organisms. I have spelled out
this argument in writing but I do not have a readable paper on this.
Intentionality
I’m very interested in
intentionality. One of the limitations
of many current theories of mental content is that they are conjoined with
computational theories of mind, where computation is understood as in the
semantic view of computation. But as
I’ve argued in some of the papers mentioned above, computation per se does
relatively little to advance our understanding of intentionality, and the
computational theory of mind needs to be rejected anyway. I think freeing our thinking about mental
content from the baggage of the computational theory of mind paves the way for
making progress in our theory of content.
We can find useful resources in cognitive neuroscience.
As a preliminary step, I have
written Splitting Concepts (co-authored
with Sam Scott, in Philosophy of Science,
2006, followed by Edouard Machery’s response, How to Split Concepts),
where I argue that the traditional notion of concept might need to be split
into several different notions, each of which explains different
phenomena. (There is not really any
cognitive neuroscience in this paper, though.)
Consciousness and Introspection
I’m also interested in
consciousness. Here too, I think many philosophers
have underestimated the potential for progress that would come from paying
serious attention to neuroscience. For
now, most of what I’ve written in this area pertains to the methodology of
using introspective reports in science and the related topic of whether
scientific methods should be public.
In Epistemic
Divergence and the Publicity of Scientific Methods (in Studies in Hist and Phil of Sci, 2003), I revisit the venerable
methodological principle that scientific methods ought to be public by offering
a plausible formulation of the principle and defending it against a recent
attack by Alvin Goldman. Goldman’s
attack was based in part on the view that the use of introspective reports in science
constitutes a private method.
In Data from
Introspective Reports: Upgrading from Commonsense to Science (in J Consciousness Studies, 2003), I
articulate and defend a middle way between Daniel Dennett’s
“heterophenomenology” about introspective reports (scientists should be neutral
as to their truth value) and Alvin Goldman and David Chalmers’s “first-person
science” (scientists should trust introspective reports). My alternative is that scientists can and
should validate introspective reports by publicly available evidence and
assumptions, in ways that I spell out in my paper. Curiously, both Goldman and Dennett now claim
to agree with me. Goldman cites my
article in his “Epistemology and the Evidential Status of Introspective
Reports: Trust, Warrant, and Evidential Sources,” Journal of Consciousness Studies, 2004, 11.7-8, pp. 1-16. Goldman’s position in that article appears to
be closer to mine than his previously published one, and he has told me in
conversation that our views are now close.
Dennett cites my article in his “Heterophenomenology
Reconsidered,” forthcoming in Phenomenology and the Cognitive Sciences. Dennett says that my paper is an “unwitting
re-invention of heterophenomenology”.
This is a mischaracterization. I
disagree with many of the things Dennett says about heterophenomenology. In addition, unlike Dennett, I explicitly
articulate the means by which introspective reports can be validated as a
source of scientific evidence about mental states. Aside from mislabeling my proposal, though,
Dennett sounds as though he endorses what I say in my paper.
In First-Person Data, I revisit the
topic of the above paper, with two main goals.
First, to respond more forcefully to the growing literature according to
which first-person data are private yet legitimate, including literature by neo-introspectionists
and neo-phenomenologists. I argue that
their view is both methodologically unacceptable and unjustified. Second, to refine my justification for
believing that first-person data are legitimate. I argue that they should be seen as the outcome
of a process of self-measurement, in which part of the subject who is the
data’s source acts as a measurement instrument.
We can then apply to first-person data the same methodological and
epistemological considerations that we apply to data from other measuring
instruments.
The Ontology of
Creature Consciousness: A Challenge for Philosophy (forthcoming in Behavioral and Brain Sciences, 30.1) is
a commentary on the target article “Consciousness
without a Cerebral Cortex: A Challenge for Neuroscience and Medicine,” by
Björn Merker. In his article, Merker defends
a radical theory to the effect that the brainstem can sustain phenomenal
consciousness by itself (without a cerebral cortex). I appeal to Merker’s theory (regardless to
whether it’s correct) to motivate the hypothesis that contrary to the common assumption
by philosophers that creature consciousness has little to tell us about the
ontology of phenomenal consciousness, creature consciousness is (at least
partially) constitutive of phenomenal consciousness. Thus, an adequate theory of consciousness should
begin with an account of creature consciousness.