Gualtiero Piccinini’s Research

 

NB: Comments on my work are always welcome

Updated: September 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 Computation

· Reformulating Computationalism

· From Cognitive Science to Cognitive Neuroscience

· Intentionality

· Consciousness and Introspection

 

Background of Computationalism

 

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 London; the proceedings were published in Minds and Machines, 2003.)  According to the mathematical objection, machines cannot think because they can only follow a finite set of rules, whereas minds can invent new rules.  This objection was initially raised by Kurt Gödel and was later made famous by John Lucas and Roger Penrose.  I argue that Turing’s views on intelligence were subtler than hitherto recognized.  He did not believe that a Turing machine could reproduce the human mind, but he did believe that a digital computer might, because a digital computer might have the ability of changing its programs in ways that correspond to the invention of new rules by human beings.  Although previously unknown or unappreciated, Turing’s reply to the mathematical objection is the most effective that I know of. 

In Allen Newell (in the New Dictionary of Scientific Biography, Thomson Gale, 2007), 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. 

The Mechanistic Account of Computation

 

I have developed a novel account of computation, the mechanistic account, which integrates conceptual resources from computability theory, computer design, and philosophy of science.  According to the mechanistic account, computing systems are mechanisms that 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 (in Philosophical Studies, 2008), 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 (in Pacific Philosophical Quarterly, 2008), 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 (in Neural Networks, 2008), 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.

Reformulating Computationalism

 

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.

In Computationalism in the Philosophy of Mind, I review some recent literature on computationalism.  Among other things, I connect some of the dots between some of my previous papers.  This is a good place to start reading for people unfamiliar with my work.

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.

 

One obstacle to a sound theory of cognition is the common confusion between computation and information processing.  In Computation vs. Information Processing: How They Are Different and Why It Matters (co-authored with Andrea Scarantino, forthcoming in Studies in History and Philosophy of Science) I argue that computation should be kept distinct from information processing.  The notions of computation and information have different histories, are associated with different bodies of mathematical theory, and have different roles to play in a theory of cognition.  In particular, although the brain is surely an information processor (in more sense than one), it may or may not be a computing system (in the strict sense of the term).

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.)

I have also written Recovering What Is Said with Empty Names (co-authored with Sam Scott).  Empty names, such as ‘Santa Claus’, are names that lack a referent.  The paper offers robust evidence, based on semantic intuitions elicited under appropriate circumstances, to the effect that empty names have meaning.  Such evidence refutes the many semantic theories (Millianism) that deny meaning to empty names.

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, Publicity, and Self-Measurement, 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.

I’ve also reviewed the nice book Describing Inner Experience? Proponent Meets Skeptic, by R. T. Hurlburt and E. Schwitzgebel, in Notre Dame Philosophical Reviews, 2008-04-25.

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.