Stevan Harnad

It is hypothesized that words originated as the names of perceptual categories and that two forms of representation underlying perceptual categorization -- iconic and categorical representations -- served to ground a third, symbolic, form of representation. The third form of representation made it possible to name and describe our environment, chiefly in terms of categories, their memberships, and their invariant features. Symbolic representations can be shared because they are intertranslatable. Both categorization and translation are approximate rather than exact, but the approximation can be made as close as we wish. This is the central property of that universal mechanism for sharing descriptions that we call natural language.

My purpose is to explain, first, that there is an alternative to Harnad's version of the symbol grounding problem, which is known as the problem of primitives; second, that there is an alternative to his solution (which is externalist) in the form of a dispositional conception (which is internalist); and, third, that, while the TTT, properly understood, may provide partial and fallible evidence for the presence of similar mental powers, it cannot supply conclusive proof, because more than observable symbolic manipuation and robotic behavior is involved here, as he admits (Harnad 1991). Carrying the problem further appears to require inference to the best explanation.

Churchland underestimates the power and purpose of the Turing Test, dismissing it as the trivial game to which the Loebner Prize (offered for the computer program that can fool judges into thinking it's human) has reduced it, whereas it is really an exacting empirical criterion: It requires that the candidate model for the mind have our full behavioral capacities -- so fully that it is indistinguishable from any of us, to any of us (not just for one Contest night, but for a lifetime). Scaling up to such a model is (or ought to be) the programme of that branch of reverse bioengineering called cognitive science. It's harmless enough to do the hermeneutics after the research has been successfully completed, but self-deluding and question-begging to do it before.

In our century a Frege/Brentano wedge has gradually been driven into the mind/body problem so deeply that it appears to have split it into two: The problem of "qualia" and the problem of "intentionality." Both problems use similar intuition pumps: For qualia, we imagine a robot that is indistinguishable from us in every objective respect, but it lacks subjective experiences; it is mindless. For intentionality, we again imagine a robot that is indistinguishable from us in every objective respect but its "thoughts" lack "aboutness"; they are meaningless. I will try to show that there is a way to re-unify the mind/body problem by grounding the "language of thought" (symbols) in our perceptual categorization capacity. The model is bottom-up and hybrid symbolic/nonsymbolic

When certain formal symbol systems (e.g., computer programs) are implemented as dynamic physical symbol systems (e.g., when they are run on a computer) their activity can be interpreted at higher levels (e.g., binary code can be interpreted as LISP, LISP code can be interpreted as English, and English can be interpreted as a meaningful conversation). These higher levels of interpretability are called "virtual" systems. If such a virtual system is interpretable as if it had a mind, is such a "virtual mind" real? This is the question addressed in this "virtual" symposium, originally conducted electronically among four cognitive scientists: Donald Perlis, a computer scientist, argues that according to the computationalist thesis, virtual minds are real and hence Searle's Chinese Room Argument fails, because if Searle memorized and executed a program that could pass the Turing Test in Chinese he would have a second, virtual, Chinese-understanding mind of which he was unaware (as in multiple personality). Stevan Harnad, a psychologist, argues that Searle's Argument is valid, virtual minds are just hermeneutic overinterpretations, and symbols must be grounded in the real world of objects, not just the virtual world of interpretations. Computer scientist Patrick Hayes argues that Searle's Argument fails, but because Searle does not really implement the program: A real implementation must not be homuncular but mindless and mechanical, like a computer. Only then can it give rise to a mind at the virtual level. Philosopher Ned Block suggests that there is no reason a mindful implementation would not be a real one

Explaining the mind by building machines with minds runs into the other-minds problem: How can we tell whether any body other than our own has a mind when the only way to know is by being the other body? In practice we all use some form of Turing Test: If it can do everything a body with a mind can do such that we can't tell them apart, we have no basis for doubting it has a mind. But what is "everything" a body with a mind can do? Turing's original "pen-pal" version (the TT) only tested linguistic capacity, but Searle has shown that a mindless symbol-manipulator could pass the TT undetected. The Total Turing Test (TTT) calls for all of our linguistic and robotic capacities; immune to Searle's argument, it suggests how to ground a symbol manipulating system in the capacity to pick out the objects its symbols refer to. No Turing Test, however, can guarantee that a body has a mind. Worse, nothing in the explanation of its successful performance requires a model to have a mind at all. Minds are hence very different from the unobservables of physics (e.g., superstrings); and Turing Testing, though essential for machine-modeling the mind, can really only yield an explanation of the body.

Turing's celebrated 1950 paper proposes a very general methodological criterion for modelling mental function: total functional equivalence and indistinguishability. His criterion gives rise to a hierarchy of Turing Tests, from subtotal ("toy") fragments of our functions (t1), to total symbolic (pen-pal) function (T2 -- the standard Turing Test), to total external sensorimotor (robotic) function (T3), to total internal microfunction (T4), to total indistinguishability in every empirically discernible respect (T5). This is a "reverse-engineering" hierarchy of (decreasing) empirical underdetermination of the theory by the data. Level t1 is clearly too underdetermined, T2 is vulnerable to a counterexample (Searle's Chinese Room Argument), and T4 and T5 are arbitrarily overdetermined. Hence T3 is the appropriate target level for cognitive science. When it is reached, however, there will still remain more unanswerable questions than when Physics reaches its Grand Unified Theory of Everything (GUTE), because of the mind/body problem and the other-minds problem, both of which are inherent in this empirical domain, even though Turing hardly mentions them.

Turing set the agenda for (what would eventually be called) the cognitive sciences. He said, essentially, that cognition is as cognition does (or, more accurately, as cognition is capable of doing): Explain the causal basis of cognitive capacity and you’ve explained cognition. Test your explanation by designing a machine that can do everything a normal human cognizer can do – and do it so veridically that human cognizers cannot tell its performance apart from a real human cognizer’s – and you really cannot ask for anything more. Or can you? Neither Turing modelling nor any other kind of computational r dynamical modelling will explain how or why cognizers feel

The ethical case for Open Access (OA) (free online access) to research findings is especially salient when it is public health that is being compromised by needless access restrictions. But the ethical imperative for OA is far more general: It applies to all scientific and scholarly research findings published in peer-reviewed journals. And peer-to-peer access is far more important than direct public access. Most research is funded so as to be conducted and published, by researchers, in order to be taken up, used, and built upon in further research and applications, again by researchers (pure and applied, including practitioners), for the benefit of the public that funded it – not in order to generate revenue for the peer-reviewed journal publishing industry (nor even because there is a burning public desire to read much of it). Hence OA needs to be mandated, by researchers' institutions and funders, for all research

Jerry Fodor argues that Darwin was wrong about "natural selection" because (1) it is only a tautology rather than a scientific law that can support counterfactuals ("If X had happened, Y would have happened") and because (2) only minds can select. Hence Darwin's analogy with "artificial selection" by animal breeders was misleading and evolutionary explanation is nothing but post-hoc historical narrative. I argue that Darwin was right on all counts. Until Darwin's "tautology," it had been believed that either (a) God had created all organisms as they are, or (b) organisms had always been as they are. Darwin revealed instead that (c) organisms have heritable traits that evolved across time through random variation, with survival and reproduction in (changing) environments determining (mindlessly) which variants were successfully transmitted to the next generation. This not only provided the (true) alternative (c), but also the methodology for investigating which traits had been adaptive, how and why; it also led to the discovery of the genetic mechanism of the encoding, variation and evolution of heritable traits. Fodor also draws erroneous conclusions from the analogy between Darwinian evolution and Skinnerian reinforcement learning. Fodor’s skepticism about both evolution and learning may be motivated by an overgeneralization of Chomsky’s “poverty of the stimulus argument” -- from the origin of Universal Grammar (UG) to the origin of the “concepts” underlying word meaning, which, Fodor thinks, must be “endogenous,” rather than evolved or learned.

Turing's celebrated 1950 paper proposes a very general methodological criterion for modelling mental function: total functional equivalence and indistinguishability. His criterion gives rise to a hierarchy of Turing Tests, from subtotal ("toy") fragments of our functions (t1), to total symbolic (pen-pal) function (T2 -- the standard Turing Test), to total external sensorimotor (robotic) function (T3), to total internal microfunction (T4), to total indistinguishability in every empirically discernible respect (T5). This is a "reverse-engineering" hierarchy of (decreasing) empirical underdetermination of the theory by the data. Level t1 is clearly too underdetermined, T2 is vulnerable to a counterexample (Searle's Chinese Room Argument), and T4 and T5 are arbitrarily overdetermined. Hence T3 is the appropriate target level for cognitive science. When it is reached, however, there will still remain more unanswerable questions than when Physics reaches its Grand Unified Theory of Everything (GUTE), because of the mind/body problem and the other-minds problem, both of which are inherent in this empirical domain, even though Turing hardly mentions them.

What language allows us to do is to "steal" categories quickly and effortlessly through hearsay instead of having to earn them the hard way, through risky and time-consuming sensorimotor "toil" (trial-and-error learning, guided by corrective feedback from the consequences of miscategorisation). To make such linguistic "theft" possible, however, some, at least, of the denoting symbols of language must first be grounded in categories that have been earned through sensorimotor toil (or else in categories that have already been "prepared" for us through Darwinian theft by the genes of our ancestors); it cannot be linguistic theft all the way down. The symbols that denote categories must be grounded in the capacity to sort, label and interact with the proximal sensorimotor projections of their distal category-members in a way that coheres systematically with their semantic interpretations, both for individual symbols, and for symbols strung together to express truth-value-bearing propositions

Searle's celebrated Chinese Room Argument has shaken the foundations of Artificial Intelligence. Many refutations have been attempted, but none seem convincing. This paper is an attempt to sort out explicitly the assumptions and the logical, methodological and empirical points of disagreement. Searle is shown to have underestimated some features of computer modeling, but the heart of the issue turns out to be an empirical question about the scope and limits of the purely symbolic model of the mind. Nonsymbolic modeling turns out to be immune to the Chinese Room Argument. The issues discussed include the Total Turing Test, modularity, neural modeling, robotics, causality and the symbol-grounding problem

In his chapter titled "Consciousness, Charles Taylor suggests that the traditional mind/body, mental/physical dichotomy is an undesirable legacy of the seventeenth century. Its faults are that it gives rise to a dualism that must then be resolved in various unsatisfactory ways. The most prevalent of these ways is currently "functionalism," which explains cognition in terms of functional states and processes like those of a computer and "marginalizes" (i.e., minimizes or denies completely the causal role of) consciousness. The alternative, "interactionism," gives due weight to consciousness but at the cost of adding an independent domain to the physical one, namely, the mental, and possibly tampering indeterminately with physics thereby.

Computationalism. According to computationalism, to explain how the mind works, cognitive science needs to find out what the right computations are -- the same ones that the brain performs in order to generate the mind and its capacities. Once we know that, then every system that performs those computations will have those mental states: Every computer that runs the mind's program will have a mind, because computation is hardware independent : Any hardware that is running the right program has the right computational states.

After people learn to sort objects into categories they see them differently. Members of the same category look more alike and members of different categories look more different. This phenomenon of within-category compression and between-category separation in similarity space is called categorical perception (CP). It is exhibited by human subjects, animals and neural net models. In backpropagation nets trained first to auto-associate 12 stimuli varying along a onedimensional continuum and then to sort them into 3 categories, CP arises as a natural side-effect because of four factors: (1) Maximal interstimulus separation in hidden-unit space during autoassociation learning, (2) movement toward linear separability during categorization learning, (3) inverse-distance repulsive force exerted by the between-category boundary, and (4) the modulating effects of input iconicity, especially in interpolating CP to untrained regions of the continuum. Once similarity space has been "warped" in this way, the compressed and separated "chunks" have symbolic labels which could then be combined into symbol strings that constitute propositions about objects. The meanings of such symbolic representations would be "grounded" in the system's capacity to pick out from their sensory projections the object categories that the propositions were about.

Research is done (mostly at universities) and funded (publicly and privately) in order to advance scientific and scholarly knowledge as well as to produce public benefits (technological and biomedical applications as well as educational and cultural ones). Research and researchers are accordingly funded not only to conduct their research, but to make their findings public, by publishing them. Their employment, salaries, careers and research funding depend on publishing their findings. This is what is often called "publish or perish.&quot

I want to report a thoroughly (perhaps surreally) modern experience I had recently. First a little context. I've always been a zealous scholarly letter writer (to the point of once being cited in print as "personal communication, pp. 14 - 20"). These days few share my epistolary penchant, which is dismissed as a doomed anachronism. Scholars don't have the time. Inquiry is racing forward much too rapidly for such genteel dawdling -- forward toward, among other things, due credit in print for one's every minute effort. So I too had resigned myself to the slower turnaround but surer rewards of conventional scholarly publication. Until I came upon electronic mail: almost as rapid and direct and spontaneous as a telephone call, but with the added discipline and permanence of the written medium. I quickly became addicted, "logging on" to check my e mail at all hours of the day and night and accumulating files of intellectual exchanges with similarly inclined e epistoleans, files that rapidly approached book length