Language Evolution and Computation Bibliography

The adaptive success of organisms depends on the categorization that is the ability to do the right thing with the right kind of thing. Most species can learn categories by direct experience (induction) and only human beings can acquire categories by word of mouth (instruction). Language began when purposive miming became conventionalized into arbitrary sequences of shared category names describing and defining new categories via propositions. An individual must be able to distinguish the members from the non-members in order to categorize correctly. The feature detector for some categories is inborn. Most categories, however, have to be learned through trial and error during the lifetime of the organism. The artificial-life simulations have showed that simple virtual creatures in virtual worlds, which must learn to do the right thing with the right kind of thing in order to survive and reproduce, are able to categorize through trial-and-error experience. It can be done with the help of neural nets that are able to learn to detect the features, which reliably distinguish one category from another.

Christiansen & Chater (C&C) suggest that language is an organism, like us, and that our brains were not selected for Universal Grammar (UG) capacity; rather, languages were selected for learnability with minimal trial-and-error experience by our brains. This explanation is circular: Where did our brain's selective capacity to learn all and only UG-compliant languages come from?

Scholars studying the origins and evolution of language are also interested in the general issue of the evolution of cognition. Language is not an isolated capability of the individual, but has intrinsic relationships with many other behavioral, cognitive, and social abilities. ...

Many special problems crop up when evolutionary theory turns, quite naturally, to the question of the adaptive value and causal role of consciousness in human and nonhuman organisms. One problem is that -- unless we are to be dualists, treating it as an independent nonphysical force -- consciousness could not have had an independent adaptive function of its own, over and above whatever behavioral and physiological functions it ``supervenes'' on, because evolution is completely blind to the difference between a conscious organism and a functionally equivalent (Turing Indistinguishable) nonconscious ``Zombie'' organism: In other words, the Blind Watchmaker, a functionalist if ever there was one, is no more a mind reader than we are. Hence Turing-Indistinguishability = Darwin-Indistinguishability. It by no means follows from this, however, that human behavior is therefore to be explained only by the push-pull dynamics of Zombie determinism, as dictated by calculations of ``inclusive fitness'' and ``evolutionarily stable strategies.'' We are conscious, and, more important, that consciousness is piggy-backing somehow on the vast complex of unobservable internal activity -- call it ``cognition'' -- that is really responsible for generating all of our behavioral capacities. Hence, except in the palpable presence of the irrational (e.g., our sexual urges) where distal Darwinian factors still have some proximal sway, it is as sensible to seek a Darwinian rather than a cognitive explanation for most of our current behavior as it is to seek a cosmological rather than an engineering explanation of an automobile's behavior. Let evolutionary theory explain what shaped our cognitive capacity (Steklis and Harnad 1976; Harnad 1996, but let cognitive theory explain our resulting behavior.

Neural network models of categorical perception (compression of withincategory similarity and dilation of between-category differences) are applied to the symbol-grounding problem (of how to connect symbols with meanings) by connecting analogue sensorimotor projections to arbitrary symbolic representations via learned category-invariance detectors in a hybrid symbolic/non-symbolic system. Our nets are trained to categorize and name 50 2 50 pixel images (e.g. circles, ellipses, squares and rectangles) projected on to the receptive field of a 7 2 7 retina. They first learn to do prototype matching and then entry-level naming for the four kinds of stimuli, grounding their names directly in the input patterns via hidden-unit representations ('sensorimotor toil'). We show that a higher-level categorization (e.g. 'symmetric' versus 'asymmetric') can be learned in two very different ways: either (1) directly from the input, just as with the entry-level categories (i.e. by toil); or (2) indirectly, from Boolean combinations of the grounded category names in the form of propositions describing the higher-order category ('symbolic theft'). We analyse the architectures and input conditions that allow grounding (in the form of compression/ separation in internal similarity space) to be 'transferred' in this second way from directly grounded entry-level category names to higher-order category names. Such hybrid models have implications for the evolution and learning of language.

There has been much discussion recently about the scope and limits of purely symbolic models of the mind and about the proper role of connectionism in cognitive modeling. This paper describes the ``symbol grounding problem'': How can the semantic interpretation of a formal symbol system be made intrinsic to the system, rather than just parasitic on the meanings in our heads? How can the meanings of the meaningless symbol tokens, manipulated solely on the basis of their (arbitrary) shapes, be grounded in anything but other meaningless symbols? The problem is analogous to trying to learn Chinese from a Chinese/Chinese dictionary alone. A candidate solution is sketched: Symbolic representations must be grounded bottom-up in nonsymbolic representations of two kinds: (1) ``iconic representations'' , which are analogs of the proximal sensory projections of distal objects and events, and (2) ``categorical representations'' , which are learned and innate feature-detectors that pick out the invariant features of object and event categories from their sensory projections. Elementary symbols are the names of these object and event categories, assigned on the basis of their (nonsymbolic) categorical representations. Higher-order (3) ``symbolic representations'' , grounded in these elementary symbols, consist of symbol strings describing category membership relations (e.g., ``An X is a Y that is Z'').

Connectionism is one natural candidate for the mechanism that learns the invariant features underlying categorical representations, thereby connecting names to the proximal projections of the distal objects they stand for. In this way connectionism can be seen as a complementary component in a hybrid nonsymbolic/symbolic model of the mind, rather than a rival to purely symbolic modeling. Such a hybrid model would not have an autonomous symbolic ``module,'' however; the symbolic functions would emerge as an intrinsically ``dedicated'' symbol system as a consequence of the bottom-up grounding of categories' names in their sensory representations. Symbol manipulation would be governed not just by the arbitrary shapes of the symbol tokens, but by the nonarbitrary shapes of the icons and category invariants in which they are grounded.