Is the history of life a series of accidents or a drama scripted by selfish genes? Is there an “essential” human nature, determined at birth or in a distant evolutionary past? What should we conserve—species, ecosystems, or something else? -/- Informed answers to questions like these, critical to our understanding of ourselves and the world around us, require both a knowledge of biology and a philosophical framework within which to make sense of its findings. In this accessible introduction to philosophy (...) of biology, Kim Sterelny and Paul E. Griffiths present both the science and the philosophical context necessary for a critical understanding of the most exciting debates shaping biology today. The authors, both of whom have published extensively in this field, describe the range of competing views—including their own—on these fascinating topics. -/- With its clear explanations of both biological and philosophical concepts, Sex and Death will appeal not only to undergraduates, but also to the many general readers eager to think critically about the science of life. (shrink)

How do minds emerge from developing brains? According to the representational features of cortex are built from the dynamic interaction between neural growth mechanisms and environmentally derived neural activity. Contrary to popular selectionist models that emphasize regressive mechanisms, the neurobiological evidence suggests that this growth is a progressive increase in the representational properties of cortex. The interaction between the environment and neural growth results in a flexible type of learning: minimizes the need for prespecification in accordance with recent neurobiological evidence (...) that the developing cerebral cortex is largely free of domain-specific structure. Instead, the representational properties of cortex are built by the nature of the problem domain confronting it. This uniquely powerful and general learning strategy undermines the central assumption of classical learnability theory, that the learning properties of a system can be deduced from a fixed computational architecture. Neural constructivism suggests that the evolutionary emergence of neocortex in mammals is a progression toward more flexible representational structures, in contrast to the popular view of cortical evolution as an increase in innate, specialized circuits. Human cortical postnatal development is also more extensive and protracted than generally supposed, suggesting that cortex has evolved so as to maximize the capacity of environmental structure to shape its structure and function through constructive learning. (shrink)

Most biologists and some cognitive scientists have independently reached the conclusion that there is no such thing as learning in the traditional “instructive‘ sense. This is, admittedly, a somewhat extreme thesis, but I defend it herein the light of data and theories jointly extracted from biology, especially from evolutionary theory and immunology, and from modern generative grammar. I also point out that the general demise of learning is uncontroversial in the biological sciences, while a similar consensus has not yet been (...) reached in psychology and in linguistics at large. Since many arguments presently offered in defense of learning and in defense of “general intelligence‘ are often based on a distorted picture of human biological evolution, I devote some sections of this paper to a critique of “adaptationism,‘ providing also a sketch of a better evolutionary theory. Moreover, since certain standard arguments presented today as “knock-down‘ in psychology, in linguistics and in artificial intelligence are a perfect replica of those once voiced by biologists in favor of instruction and against selection, I capitalize on these errors of the past to draw some lessons for the present and for the future. (shrink)

The book from which these sections are excerpted is concerned with the prospects for assimilating the study of human intelligence and its products to the natural sciences through the investigation of cognitive structures, understood as systems of rules and representations that can be regarded as These mental structui′es serve as the vehicles for the exercise of various capacities. They develop in the mind on the basis of an innate endowment that permits the growth of rich and highly articulated structures along (...) an intrinsically determined course under the triggering and partially shaping effect of experience, which fixes parameters in an intricate system of predetermined form. It is argued that the mind is modular in character, with a diversity of cognitive structures, each with its specific properties arid principles. Knowledge of language, of the behavior of objects, and much else crucially involves these mental structures, and is thus not characterizable in terms of capacities, dispositions, or practical abilities, nor is it necessarily grounded in experience in the standard sense of this term. (shrink)

The book from which these sections are excerpted is concerned with the prospects for assimilating the study of human intelligence and its products to the natural sciences through the investigation of cognitive structures, understood as systems of rules and representations that can be regarded as “mental organs.” These mental structui′es serve as the vehicles for the exercise of various capacities. They develop in the mind on the basis of an innate endowment that permits the growth of rich and highly articulated (...) structures along an intrinsically determined course under the triggering and partially shaping effect of experience, which fixes parameters in an intricate system of predetermined form. It is argued that the mind is modular in character, with a diversity of cognitive structures, each with its specific properties arid principles. Knowledge of language, of the behavior of objects, and much else crucially involves these mental structures, and is thus not characterizable in terms of capacities, dispositions, or practical abilities, nor is it necessarily grounded in experience in the standard sense of this term.Various types of knowledge and modes of knowledge acquisition are discussed in these terms. Some of the properties of the language faculty are investigated. The basic cognitive relation is “knowing a grammar”; knowledge of language is derivative and, correspondingly, raises further problems. Language as commonly understood is not a unitary phenomenon but involves a number of interacting systems: the “computational” system of grammar, which provides the representations of sound and meaning that permit a rich range of expressive potential, is distinct from a conceptual system with its own properties; knowledge of language must be distinguished from knowledge of how to use a language; and the various systems that enter into the knowledge and use of language must be further analyzed into their specific subcomponents. (shrink)

Chomsky proposes a reformulation of the theory of transformational generative grammar that takes recent developments in the descriptive analysis of particular ...

The aim of this book is to discuss the fundamental ideas which lie behind the statistical theory of learning and generalization. It considers learning as a general problem of function estimation based on empirical data. Omitting proofs and technical details, the author concentrates on discussing the main results of learning theory and their connections to fundamental problems in statistics. This second edition contains three new chapters devoted to further development of the learning theory and SVM techniques. Written in a readable (...) and concise style, the book is intended for statisticians, mathematicians, physicists, and computer scientists. (shrink)

This powerfully iconoclastic book reconsiders the influential nativist position toward the mind. Nativists assert that some concepts, beliefs, or capacities are innate or inborn: "native" to the mind rather than acquired. Fiona Cowie argues that this view is mistaken, demonstrating that nativism is an unstable amalgam of two quite different--and probably inconsistent--theses about the mind. Unlike empiricists, who postulate domain-neutral learning strategies, nativists insist that some learning tasks require special kinds of skills, and that these skills are hard-wired into our (...) brains at birth. This "faculties hypothesis" finds its modern expression in the views of Noam Chomsky. Cowie, marshaling recent empirical evidence from developmental psychology, psycholinguistics, computer science, and linguistics, provides a crisp and timely critique of Chomsky's nativism and defends in its place a moderately nativist approach to language acquisition. Also in contrast to empiricists, who view the mind as simply another natural phenomenon susceptible of scientific explanation, nativists suspect that the mental is inelectably mysterious. Cowie addresses this second strand in nativist thought, taking on the view articulated by Jerry Fodor and other nativists that learning, particularly concept acquisition, is a fundamentally inexplicable process. Cowie challenges this explanatory pessimism, and argues convincingly that concept acquisition is psychologically explicable. What's Within? is a clear and provocative achievement in the study of the human mind. (shrink)

Fiona Cowie’s _What’s Within_ consists of three parts. In the first, she examines the early modern rationalist- empiricist debate over nativism, isolating what she considers the two substantive “strands” (67)¹ that truly separated them: whether there exist domain-specific learning mechanisms, and whether concept acquisition is amenable to naturalistic explanation. She then turns, in the book’s succeeding parts, to where things stand today with these issues. The second part argues that Jerry Fodor’s view of concepts is continuous with traditional nativism in (...) that it precludes a naturalistic story of concept acquisition. Cowie objects, however, to Fodor’s path to this conclusion and thus sees no reason to endorse it. The third part assesses Chomskyan nativism as a contemporary instance of positing domain- specific learning mechanisms. Though she is highly critical of how “poverty of the stimulus” arguments and the like have been used to lend credence to stronger conclusions, she holds that such arguments do indeed support the nativist’s domain-specificity claim. Cowie’s reconsideration of nativism thus limits itself to concepts and language (a few exceptions aside: there are two brief forays into face recognition and a mention of pathogen response). The terrain she does cover, however, is vast; and Cowie’s illuminating discussions will stimulate anyone interested in the area. As I focus on a few large-scale qualms in what follows, let me mention in particular that much of what is of interest in Cowie’s book is to be found in her detailed consideration of specific arguments. (shrink)

It is sometimes argued that if PDP networks can be trained to make correct judgements of grammaticality we have an existence proof that there is enough information in the stimulus to permit learning grammar by inductive means alone. This seems inconsistent superficially with Gold's theorem and at a deeper level with the fact that networks are designed on the basis of assumptions about the domain of the function to be learned. To clarify the issue I consider what we should learn (...) from Gold's theorem, then go on to inquire into what it means to say that knowledge is domain specific. I first try sharpening the intuitive notion of domain specific knowledge by reviewing the alleged difference between processing limitations due to shortage of resources vs shortages of knowledge. After rejecting different formulations of this idea, I suggest that a model is language specific if it transparently refer to entities and facts about language as opposed to entities and facts of more general mathematical domains. This is a useful but not necessary condition. I then suggest that a theory is domain specific if it belongs to a model family which is attuned in a law-like way to domain regularities. This leads to a comparison of PDP and parameter setting models of language learning. I conclude with a novel version of the poverty of stimulus argument. (shrink)

It is sometimes argued that if PDP networks can be trained to make correct judgements of grammaticality we have an existence proof that there is enough information in the stimulus to permit learning grammar by inductive means alone. This seems inconsistent superficially with Gold's theorem and at a deeper level with the fact that networks are designed on the basis of assumptions about the domain of the function to be learned. To clarify the issue I consider what we should learn (...) from Gold's theorem, then go on to inquire into what it means to say that knowledge is domain specific. I first try sharpening the intuitive notion of domain specific knowledge by reviewing the alleged difference between processing limitatons due to shartage of resources vs shortages of knowledge. After rejecting different formulations of this idea, I suggest that a model is language specific if it transparently refer to entities and facts about language as opposed to entities and facts of more general mathematical domains. This is a useful but not necessary condition. I then suggest that a theory is domain specific if it belongs to a model family which is attuned in a law-like way to domain regularities. This leads to a comparison of PDP and parameter setting models of language learning. I conclude with a novel version of the poverty of stimulus argument. (shrink)