Stevan Harnad

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

In innate Categorical Perception (CP) (e.g., colour perception), similarity space is "warped," with regions of increased within-category similarity (compression) and regions of reduced between-category similarity (separation) enh ancing the category boundaries and making categorisation reliable and all-or-none rather than graded. We show that category learning can likewise warp similarity space, resolving uncertainty near category boundaries. Two Hard and two Easy texture learning tasks were compared: As predicted, there were fewer successful Learners with the Hard task, and only the successful Learners of the Hard task exhibited CP. In a second experiment, the Easy task was made Hard by making the corrective feedback during learn ing only 90% reliable; this too generated CP. The results are discussed in relation to supervised, unsupervised and dual-mode models of category learning and representation.The world is full of things that vary in their similarity and interconfusability.O rganisms must somehow resolve this confusion, sorting and acting upon things adaptively. It might be important, for example, to learn which kinds of mushrooms are poisonous and which are safe to eat, minimising the confusion between them (Greco, Cangelosi & Harnad 1997).

There are many entry points into the problem of categorization. Two particularly important ones are the so-called top-down and bottom-up approaches. Top-down approaches such as artificial intelligence begin with the symbolic names and descriptions for some categories already given; computer programs are written to manipulate the symbols. Cognitive modeling involves the further assumption that such symbol-interactions resemble the way our brains do categorization. An explicit expectation of the top-down approach is that it will eventually join with the bottom-up approach, which tries to model how the hardware of the brain works: sensory systems, motor systems and neural activity in general. The assumption is that the symbolic cognitive functions will be implemented in brain function and linked to the sense organs and the organs of movement in roughly the way a program is implemented in a computer, with its links to peripheral devices such as transducers and effectors.

To appreciate what a huge difference there is between the author of a peer reviewed journal article and just about any other kind of author we need only remind ourselves why universities have their "publish or perish" policy: Aside from imparting existing knowledge to students through teaching, the work of a university scholar or scientist is devoted to creating new knowledge for other scholars and scientists to use, apply, and build upon, for the benefit of us all. Creating new knowledge is called "research," and its active use and application are called "research impact." Researchers are encouraged, indeed required, to publish their findings because that is the only way to make their research accessible to and usable by other researchers. It is the only way for research to generate further research. Not publishing it means no access to it by other researchers, and no access means no impact -- in which case the research may as well not have done in the first place.

Why, oh why do we keep conflating this question, which is about the uncertainty of sensory information, with the much more profound and pertinent one, which is about the functional explicability and causal role of feeling? " _Kant: How is it possible for something even to be a thought? What are the conditions for the_ _possibility of experience at all?_ " That's not the right question either. The right question is not even an epistemic one, about "thought" or "knowledge" but an "aesthesiogenic" one: How and why are there any feelings at all?

According to "computationalism" (Newell, 1980; Pylyshyn 1984; Dietrich 1990), mental states are computational states, so if one wishes to build a mind, one is actually looking for the right program to run on a digital computer. A computer program is a semantically interpretable formal symbol system consisting of rules for manipulating symbols on the basis of their shapes, which are arbitrary in relation to what they can be systematically interpreted as meaning. According to computationalism, every physical implementation of the right symbol system will have mental states.

Steels & Belpaeme's (S&B's) simulations contain all the right components, but they are put together wrongly. Color categories are unrepresentative of categories in general and language is not merely naming. Language evolved because it provided a powerful new way to acquire categories (through instruction, rather than just the old way of other species, through trial-and-error experience). It did not evolve so that multiple agents looking at the same objects could let one another know which of the objects they had in mind, co-coining names for them on the fly.

1.1 The predominant approach to cognitive modeling is still what has come to be called "computationalism" (Dietrich 1990, Harnad 1990b), the hypothesis that cognition is computation. The more recent rival approach is "connectionism" (Hanson & Burr 1990, McClelland & Rumelhart 1986), the hypothesis that cognition is a dynamic pattern of connections and activations in a "neural net." Are computationalism and connectionism really deeply different from one another, and if so, should they compete for cognitive hegemony, or should they collaborate? These questions will be addressed here, in the context of an obstacle that is faced by computationalism (as well as by connectionism if it is either computational or seeks cognitive hegemony on its own): The symbol grounding problem (Harnad 1990).

The problem seems apparent even in Glasgow's term ``depict'', which is used by way of contrast with ``describe''. Now ``describe'' refers relatively unproblematically to strings of symbols, such as those in this written sentence, that are systematically interpretable as propositions describing objects, events, or states of affairs. But what does ``depict'' mean? In the case of a picture -- whether a photo or a diagram -- it is clear what depict means. A picture is an object (I will argue below that it is an analog object, relative to what it is a picture of) and it DEPICTS yet another object: the object it is a picture OF. But in the case of an array, whether described formally, with numerical coordinates, or stored in a machine, or ``depicted'' diagrammatically by way of a secondary illustration, it is not at all clear whether the entity in question is indeed a picture, or merely yet another set of symbols that is INTERPRETABLE as referring to a picture, which picture in turn depicts an object! It is clear that we are dealing with many layers of interpretation here already, and so far we are still talking only about external objects (such as pictures, symbols and objects simpliciter). We still have not gotten to MENTAL objects, such as mental ``images''

Peer Review and Copyright each have a double role: Formal refereeing protects (R1) the author from publishing and (R2) the reader from reading papers that are not of sufficient quality. Copyright protects the author from (C1) theft of text and (C2) theft of authorship. It has been suggested that in the electronic medium we can dispense with peer review, "publish" everything, and let browsing and commentary do the quality control. It has also been suggested that special safeguards and laws may be needed to enforce copyright on the Net. I will argue, based on 20 years of editing Behavioral and Brain Sciences, a refereed (paper) journal of peer commentary, 8 years of editing Psycoloquy, a refereed electronic journal of peer commentary, and 1 year of implementing CogPrints, an electronic archive of unrefereed preprints and refereed reprints in the cognitive sciences modeled on the Los Alamos Physics Eprint Archive, that (i) peer commentary is a supplement, not a substitute, for peer review, (ii) the authors of refereed papers, who get and seek no royalties from the sale of their texts, only want protection from theft of authorship on the Net, not from theft of text, which is a victimless crime, and hence (iii) the trade model (subscription, site license or pay- per-view) should be replaced by author page-charges to cover the much reduced cost of implementing peer review, editing and archiving on the Net, in exchange for making the learned serial corpus available for free for all forever.

Brian Rotman argues that (one) “mind” and (one) “god” are only conceivable, literally, because of (alphabetic) literacy, which allowed us to designate each of these ghosts as an incorporeal, speaker-independent “I” (or, in the case of infinity, a notional agent that goes on counting forever). I argue that to have a mind is to have the capacity to feel. No one can be sure which organisms feel, hence have minds, but it seems likely that one-celled organisms and plants do not, whereas animals do. So minds originated before humans and before language --hence, a fortiori, before writing, whether alphabetic or ideographic.

What lies on the two sides of the linguistic divide is fairly clear: On one side, you have organisms buffeted about to varying degrees, depending on their degree of autonomy and plasticity, by the states of affairs in the world they live in. On the other side, you have organisms capable of describing and explaining the states of affairs in the world they live in. Language is what distinguishes one side from the other. How did we get here from there? In principle, one can tell a seamless story about how inborn, involuntary communicative signals and voluntary instrumental praxis could have been shaped gradually, through feedback from their consequences, first into analog pantomime with communicative intent, and then into arbitrary category names combined into all powerful, truth value bearing propositions, freed from the iconic "shape" of their referents and able to tell all.