This paper explores a variety of different versions of the thesis that natural language is involved in human thinking. It distinguishes amongst strong and weak forms of this thesis, dismissing some as implausibly strong and others as uninterestingly weak. Strong forms dismissed include the view that language is conceptually necessary for thought (endorsed by many philosophers) and the view that language is _de facto_ the medium of all human conceptual thinking (endorsed by many philosophers and social scientists). Weak forms include (...) the view that language is necessary for the acquisition of many human concepts, and the view that language can serve to scaffold human thought processes. The paper also discusses the thesis that language may be the medium of _conscious_ propositional thinking, but argues that this cannot be its most fundamental cognitive role. The idea is then proposed that natural language is the medium for non-domain-specific thinking, serving to integrate the outputs of a variety of domain-specific conceptual faculties (or central-cognitive ‘quasi-modules’). Recent experimental evidence in support of this idea is reviewed, and the implications of the idea are discussed, especially for our conception of the architecture of human cognition. Finally, some further kinds of evidence which might serve to corroborate or refute the hypothesis are mentioned. The overall goal of the paper is to review a wide variety of accounts of the cognitive function of natural language, integrating a number of different kinds of evidence and theoretical consideration in order to propose and elaborate the most plausible candidate. (shrink)

A suitable project for the new Millenium is to radically reconfigure our image of human rationality. Such a project is already underway, within the Cognitive Sciences, under the umbrellas of work in Situated Cognition, Distributed and De-centralized Cogition, Real-world Robotics and Artificial Life1. Such approaches, however, are often criticized for giving certain aspects of rationality too wide a berth. They focus their attention on on such superficially poor cousins as.

This chapter addresses the main challenge facing massively modular theories of the architecture of the human mind. This is to account for the distinctively flexible, non-domain-specific character of much human thinking. It shows how the appearance of a modular language faculty within an evolving modular architecture might have led to these distinctive features of human thinking with only minor further additions and non-domain-specific adaptations.

Since Darwin, the idea of psychological continuity between humans and other animals has dominated theory and research in investigating the minds of other species. Indeed, the field of comparative psychology was founded on two assumptions. First, it was assumed that introspection could provide humans with reliable knowledge about the causal connection between specific mental states and specific behaviors. Second, it was assumed that in those cases in which other species exhibited behaviors similar to our own, similar psychological causes were at (...) work. In this paper, we show how this argument by analogy is flawed with respect to the case of second‐order mental states. As a test case, we focus on the question of how other species conceive of visual attention, and in particular whether chimpanzees interpret seeing as a mentalistic event involving internal states of perception, attention, and belief. We conclude that chimpanzees do not reason about seeing in this manner, and indeed, there is considerable reason to suppose that they do not harbor representations of mental states in general. We propose a reinterpretation model in which the majority of the rich social behaviors that humans and other primates share in common emerged long before the human lineage evolved the psychological means of interpreting those behaviors in mentalistic terms. Although humans, chimpanzees, and most other species may be said to possess mental states, humans alone may have evolved a cognitive specialization for reasoning about such states. (shrink)

Combining ideas from philosophy, artificial intelligence, and neurobiology, Daniel Dennett leads the reader on a fascinating journey of inquiry, exploring such intriguing possibilities as: Can any of us really know what is going on in someone else's mind? What distinguishes the human mind from the minds of animals, especially those capable of complex behavior? If such animals, for instance, were magically given the power of language, would their communities evolve an intelligence as subtly discriminating as ours? Will robots, once they (...) have been endowed with sensory systems like those that provide us with experience, ever exhibit the particular traits long thought to distinguish the human mind, including the ability to think about thinking? Dennett addresses these questions from an evolutionary perspective. Beginning with the macromolecules of DNA and RNA, the author shows how, step-by-step, animal life moved from the simple ability to respond to frequently recurring environmental conditions to much more powerful ways of beating the odds, ways of using patterns of past experience to predict the future in never-before-encountered situations. Whether talking about robots whose video-camera ”eyes” give us the powerful illusion that ”there is somebody in there” or asking us to consider whether spiders are just tiny robots mindlessly spinning their webs of elegant design, Dennett is a master at finding and posing questions sure to stimulate and even disturb. (shrink)

It has been claimed that recursion is one of the properties that distinguishes human language from any other form of animal communication. Contrary to this claim, a recent study purports to demonstrate center‐embedded recursion in starlings. I show that the performance of the birds in this study can be explained by a counting strategy, without any appreciation of center‐embedding. To demonstrate that birds understand center‐embedding of sequences of the form AnBn (such as A1A2B2B1, or A3A4A5B5B4B3) would require not only that (...) they discriminate such patterns from other patterns, but that they appreciate that elements must be bound from the outside in (thus, in the above examples, A1B1, A2B2, A3B3, A4B4, A5B5 are bound pairs). This has not been shown in nonhuman species, and sentences with this structure are difficult even for humans to parse. There appears to be no evidence to date that nonhuman species understand recursion. (shrink)

Studies of the conceptual abilities of nonhuman primates demonstrate the substantial range of these abilities as well as their limitations. Such abilities range from categorization on the basis of shared physical attributes, associative relations and functions to abstract concepts as reflected in analogical reasoning about relations between relations. The pattern of results from these studies point to a fundamental distinction between monkeys and apes in both their implicit and explicit conceptual capacities. Monkeys, but not apes, might be best regarded as (...) “paleo‐logicans” in the sense that they form common class concepts of identity on the basis of identical predicates (i.e., shared features). The discrimination of presumably more abstract relations commonly involves relatively simple procedural strategies mediated by associative processes likely shared by all mammals. There is no evidence that monkeys can perceive, let alone judge, relations‐between‐relations. This analogical conceptual capacity is found only in chimpanzees and humans. Interestingly, the “analogical ape,” like the child, can make its analogical knowledge explicit only if it is first provided with a symbol system by which propositional representations can be encoded and manipulated. (shrink)

Some regularities enjoy only an attenuated existence in a body of training data. These are regularities whose statistical visibility depends on some systematic recoding of the data. The space of possible recodings is, however, infinitely large – it is the space of applicable Turing machines. As a result, mappings that pivot on such attenuated regularities cannot, in general, be found by brute-force search. The class of problems that present such mappings we call the class of “type-2 problems.” Type-1 problems, by (...) contrast, present tractable problems of search insofar as the relevant regularities can be found by sampling the input data as originally coded. Type-2 problems, we suggest, present neither rare nor pathological cases. They are rife in biologically realistic settings and in domains ranging from simple animat behaviors to language acquisition. Not only are such problems rife – they are standardly solved! This presents a puzzle. How, given the statistical intractability of these type-2 cases, does nature turn the trick? One answer, which we do not pursue, is to suppose that evolution gifts us with exactly the right set of recoding biases so as to reduce specific type-2 problems to type-1 mappings. Such a heavy-duty nativism is no doubt sometimes plausible. But we believe there are other, more general mechanisms also at work. Such mechanisms provide general strategies for managing problems of type-2 complexity. Several such mechanisms are investigated. At the heart of each is a fundamental ploy – namely, the maximal exploitation of states of representation already achieved by prior, simpler learning so as to reduce the amount of subsequent computational search. Such exploitation both characterizes and helps make unitary sense of a diverse range of mechanisms. These include simple incremental learning, modular connectionism, and the developmental hypothesis of “representational redescription”. In addition, the most distinctive features of human cognition – language and culture – may themselves be viewed as adaptations enabling this representation/computation trade-off to be pursued on an even grander scale. (shrink)

The idea that the language we speak influences the way we think has evoked perennial fascination and intense controversy. According to the strong version of this hypothesis, called the Sapir-Whorf hypothesis after the American linguists who propounded it, languages vary in their semantic partitioning of the world, and the structure of one’s language influences how one understands the world. Thus speakers of different languages perceive the world differently. Although the last two decades have been marked by extreme skepticism concerning the (...) possible effects of language on thought, recent theoretical and methodological advances in cognitive science have given the question new life. Research in linguistics and linguistic anthropology has revealed striking differences in cross-linguistic semantic patterns, and cognitive psychology has developed subtle techniques for studying how people represent and remember experience. It is now possible to test predictions about how a given language influences the thinking of its speakers. Language in Mind includes contributions from both skeptics and believers and from a range of fields. It contains work in cognitive psychology, cognitive development, linguistics, anthropology, and animal cognition. The topics discussed include space, number, motion, gender, theory of mind, thematic roles, and the ontological distinction between objects and substances. I Introduction 1 Whither Whorf 3 Dedre Gentner and Susan Goldin-Meadow II Position Statements 15 1 Languages and Representations 17 Eve V. Clark 2 Language and Mind: Let’s Get the Issues Straight! 25 Stephen C. Levinson 3 The Key Is Social Cognition 47 Michael Tomasello III Language as Lens: Does the Language We Acquire Influence How We See the World? 59 4 Sex, Syntax, and Semantics 61 Lera Boroditsky, Lauren A. Schmidt, and Webb Phillips 5 Speaking versus Thinking about Objects and Actions 81 Barbara C. Malt, Steven A. Sloman, and Silvia P. Gennari 6 The Effects of Spatial Language on Spatial Representation: Setting Some Boundaries 113 Edward Munnich and Barbara Landau 7 Language and Thought Online: Cognitive Consequences of Linguistic Relativity 157 Dan I. Slobin IV Language as Tool Kit: Does the Language We Acquire Augment Our Capacity for Higher-Order Representation and Reasoning? 193 8 Why We’re So Smart 195 Dedre Gentner 9 Does Language Help Animals Think? 237 Stan A. Kuczaj, II, and Jennifer L. Hendry 10 What Makes Us Smart? Core Knowledge and Natural Language 277 Elizabeth S. Spelke 11 Conceptual and Linguistic Factors in Inductive Projection: How Do Young Children Recognize Commonalities between Animals and Plants? 313 Kayoko Inagaki and Giyoo Hatano 12 Language for Thought: Coming to Understand False Beliefs 335 Jill G. de Villiers and Peter A. de Villiers V Language as Category Maker: Does the Language We Acquire Influence Where We Make Our Category Distinctions? 385 13 Space under Construction: Language-Specific Spatial Categorization in First Language Acquisition 387 Melissa Bowerman and Soonja Choi 14 Reevaluating Linguistic Relativity: Language-Specific Categories and the Role of Universal Ontological Knowledge in the Construal of Individuation 429 Mutsumi Imai and Reiko Mazuka 15 Interaction of Language Type and Referent Type in the Development of Nonverbal Classification Preferences 465 John A. Lucy and Suzanne Gaskins 16 Thought before Language: Do We Think Ergative? 493 Susan Goldin-Meadow. (shrink)

Routledge is now re-issuing this prestigious series of 204 volumes originally published between 1910 and 1965.

In The Origin of Species (1859) Darwin challenged many of the most deeply-held beliefs of the Western world. Arguing for a material, not divine, origin of species, he showed that new species are achieved by "natural selection." The Origin communicates the enthusiasm of original thinking in an open, descriptive style, and Darwin's emphasis on the value of diversity speaks more strongly now than ever. As well as a stimulating introduction and detailed notes, this edition offers a register of the many (...) writers referred to by Darwin in the text. (shrink)

In this book, Newell makes the case for unified theories by setting forth a candidate.

Since the BBS article in which Premack and Woodruff (1978) asked “Does the chimpanzee have a theory of mind?,” it has been repeatedly claimed that there is observational and experimental evidence that apes have mental state concepts, such as “want” and “know.” Unlike research on the development of theory of mind in childhood, however, no substantial progress has been made through this work with nonhuman primates. A survey of empirical studies of imitation, self-recognition, social relationships, deception, role-taking, and perspective-taking suggests (...) that in every case where nonhuman primate behavior has been interpreted as a sign of theory of mind, it could instead have occurred by chance or as a product of nonmentalistic processes such as associative learning or inferences based on nonmental categories. Arguments to the effect that, in spite of this, the theory of mind hypothesis should be accepted because it is more parsimonious than alternatives or because it is supported by convergent evidence are not compelling. Such arguments are based on unsupportable assumptions about the role of parsimony in science and either ignore the requirement that convergent evidence proceed from independent assumptions, or fail to show that it supports the theory of mind hypothesis over nonmentalist alternatives. Progress in research on theory of mind requires experimental procedures that can distinguish the theory of mind hypothesis from nonmentalist alternatives. A procedure that may have this potential is proposed. It uses conditional discrimination training and transfer tests to determine whether chimpanzees have the concept “see.” Commentators are invited to identify flaws in the procedure and to suggest alternatives. Key Words: apes; associative learning; concepts; convergence; deception; evolution of intelligence; folk psychology; imitation; mental state attribution; monkeys; parsimony; perspective-taking; primates; role-taking; self-recognition; social cognition; social intelligence; theory of mind. (shrink)

From an early age, humans know a surprising amount about basic physical principles, such as gravity, force, mass, and shape. We can see this in the way that young children play, and manipulate objects around them. The same behaviour has long been observed in primates - chimpanzees have been shown to possess a remarkable ability to make and use simple tools. But what does this tell us about their inner mental state - do they therefore share the same understanding to (...) that of a young child? Do they understand the simple, underlying physical principles involved? Though some people would say that they do, this book reports groundbreaking research that questions whether this really is the case. -/- Folk Physics for Apes challenges the assumptions so often made about apes. It offers us a rare glimpse into the workings of another mind, examining how apes perceive and understand the physical world - an understanding that appears to be both similar to, and yet profoundly different from our own. The book will have broad appeal to evolutionary psychologists, developmental psychologists, and those interested in the sub-disciplines of cognitive science (philosophy, anthropology). The book additionally offers for developmental psychologists some valuable new non-verbal techniques for assessing causal understanding in young children. (shrink)

It is widely believed that what distinguishes the social cognition of humans from that of other animals is the belief–desire psychology of four–year–old children and adults (so–called theory of mind). We argue here that this is actually the second ontogenetic step in uniquely human social cognition. The first step is one year old children's understanding of persons as intentional agents, which enables skills of cultural learning and shared intentionality. This initial step is ‘the real thing’ in the sense that it (...) enables young children to participate in cultural activities using shared, perspectival symbols with a conventional/normative/reflective dimension—for example, linguistic communication and pretend play—thus inaugurating children's understanding of things mental. Understanding beliefs and participating in collective intentionality at four years of age—enabling the comprehension of such things as money and marriage—results from several years of engagement with other persons in perspective–shifting and reflective discourse containing propositional attitude constructions. (shrink)

Modularity has been the subject of intense debate in the cognitive sciences for more than 2 decades. In some cases, misunderstandings have impeded conceptual progress. Here the authors identify arguments about modularity that either have been abandoned or were never held by proponents of modular views of the mind. The authors review arguments that purport to undermine modularity, with particular attention on cognitive architecture, development, genetics, and evolution. The authors propose that modularity, cleanly defined, provides a useful framework for directing (...) research and resolving debates about individual cognitive systems and the nature of human evolved cognition. Modularity is a fundamental property of living things at every level of organization; it might prove indispensable for understanding the structure of the mind as well. (shrink)

We propose that children employ specialized cognitive systems that allow them to recover an accurate “causal map” of the world: an abstract, coherent, learned representation of the causal relations among events. This kind of knowledge can be perspicuously understood in terms of the formalism of directed graphical causal models, or “Bayes nets”. Children’s causal learning and inference may involve computations similar to those for learning causal Bayes nets and for predicting with them. Experimental results suggest that 2- to 4-year-old children (...) construct new causal maps and that their learning is consistent with the Bayes net formalism. (shrink)

During the first two years of human life a common neural substrate underlies the hierarchical organization of elements in the development of speech as well as the capacity to combine objects manually, including tool use. Subsequent cortical differentiation, beginning at age two, creates distinct, relatively modularized capacities for linguistic grammar and more complex combination of objects. An evolutionary homologue of the neural substrate for language production and manual action is hypothesized to have provided a foundation for the evolution of language (...) before the divergence of the hominids and the great apes. Support comes from the discovery of a Broca's area homologue and related neural circuits in contemporary primates. In addition, chimpanzees have an identical constraint on hierarchical complexity in both tool use and symbol combination. Their performance matches that of the two-year-old child who has not yet developed the neural circuits for complex grammar and complex manual combination of objects. (shrink)

The present edition provides a detailed and accessible discussion ofhis theories and adds an account of the immediate responses to the book on publication.

As the cognitive revolution was slow to come to the study of animal behavior, the vast majority of what we know about primate cognition has been discovered in the last 30 years. Building on the recognition that the physical and social worlds of humans and their living primate relatives pose many of the same evolutionary challenges, programs of research have established that the most basic cognitive skills and mental representations that humans use to navigate those worlds are already possessed by (...) other primates. There may be differences between humans and other primates, however, in more complex cognitive skills, such as reasoning about relations, causality, time, and other minds. Of special importance, the human primate seems to possess a species-unique set of adaptations for “cultural intelligence,” which are broad reaching in their effects on human cognition. (shrink)

Thinking Without Words provides a challenging new theory of the nature of non-linguistic thought. Jose Luis Bermudez offers a conceptual framework for treating human infants and non-human animals as genuine thinkers. The book is written with an interdisciplinary readership in mind and will appeal to philosophers, psychologists, and students of animal behavior.

Working memory limits are best defined in terms of the complexity of the relations that can be processed in parallel. Complexity is defined as the number of related dimensions or sources of variation. A unary relation has one argument and one source of variation; its argument can be instantiated in only one way at a time. A binary relation has two arguments, two sources of variation, and two instantiations, and so on. Dimensionality is related to the number of chunks, because (...) both attributes on dimensions and chunks are independent units of information of arbitrary size. Studies of working memory limits suggest that there is a soft limit corresponding to the parallel processing of one quaternary relation. More complex concepts are processed by or In segmentation, tasks are broken into components that do not exceed processing capacity and can be processed serially. In conceptual chunking, representations are to reduce their dimensionality and hence their processing load, but at the cost of making some relational information inaccessible. Neural net models of relational representations show that relations with more arguments have a higher computational cost that coincides with experimental findings on higher processing loads in humans. Relational complexity is related to processing load in reasoning and sentence comprehension and can distinguish between the capacities of higher species. The complexity of relations processed by children increases with age. Implications for neural net models and theories of cognition and cognitive development are discussed. (shrink)

Some regularities enjoy only an attenuated existence in a body of training data. These are regularities whose statistical visibility depends on some systematic recoding of the data. The space of possible recodings is, however, infinitely large type-2 problems. they are standardly solved! This presents a puzzle. How, given the statistical intractability of these type-2 cases, does nature turn the trick? One answer, which we do not pursue, is to suppose that evolution gifts us with exactly the right set of recoding (...) biases so as to reduce specific type-2 problems to (tractable) type-1 mappings. Such a heavy-duty nativism is no doubt sometimes plausible. But we believe there are other, more general mechanisms also at work. Such mechanisms provide general (not task-specific) strategies for managing problems of type-2 complexity. Several such mechanisms are investigated. At the heart of each is a fundamental ploy representational redescription language and culture – may themselves be viewed as adaptations enabling this representation/computation trade-off to be pursued on an even grander scale. (shrink)

In Connectionism and the Philosophy of Psychology, Horgan and Tienson articulate and defend a new view of cognition.

José Luis Bermúdez introduces the philosophy of psychology as an interdisciplinary exploration of the nature and mechanisms of cognition. _Philosophy of Psychology_ charts out four influential 'pictures of the mind' and uses them to explore central topics in the philosophical foundations of psychology, including the relation between different levels of studying the mind/brain; the nature and scope of psychological explanation; the architecture of cognition; and the relation between thought and language. Chapters cover all the core concepts, including: models of psychological (...) explanation the nature of commonsense psychology arguments for the autonomy of psychology functionalist approaches to cognition computational models of the mind neural network modeling rationality and mental causation perception, action and cognition the language of thought and the architecture of cognition. _Philosophy of Psychology: A Contemporary Introduction_ is a very clear and well-structured textbook from one of the leaders in the field. (shrink)

This article describes an approach to connectionist language research that relies on the development of grammar formalisms rather than computer models. From formulations of the fundamental theoretical commitments of connectionism and of generative grammar, it is argued that these two paradigms are mutually compatible. Integrating the basic assumptions of the paradigms results in formal theories of grammar that centrally incorporate a certain degree of connectionist computation. Two such grammar formalisms—Harmonic Grammar and Optimality Theory —are briefly introduced to illustrate grammar‐based approaches (...) to connectionist language research. The strengths and weaknesses of grammar‐based research and more traditional model‐based research are argued to be complementary, suggesting a significant role for both strategies in the spectrum of connectionist language research. (shrink)

Keith Holyoak and Paul Thagard provide a unified, comprehensive account of the diverse operations and applications of analogy, including problem solving, ...

A theory of analogy must describe how the meaning of an analogy is derived from the meanings of its parts. In the structure‐mapping theory, the interpretation rules are characterized as implicit rules for mapping knowledge about a base domain into a target domain. Two important features of the theory are (a) the rules depend only on syntactic properties of the knowledge representation, and not on the specific content of the domains; and (b) the theoretical framework allows analogies to be distinguished (...) cleanly from literal similarity statements, applications of abstractions, and other kinds of comparisons.Two mapping principles are described: (a) Relations between objects, rather than attributes of objects, are mapped from base to target; and (b) The particular relations mapped are determined by systematicity, as defined by the existence of higher‐order relations. (shrink)

We examine the question of which aspects of language are uniquely human and uniquely linguistic in light of recent suggestions by Hauser, Chomsky, and Fitch that the only such aspect is syntactic recursion, the rest of language being either specific to humans but not to language (e.g. words and concepts) or not specific to humans (e.g. speech perception). We find the hypothesis problematic. It ignores the many aspects of grammar that are not recursive, such as phonology, morphology, case, agreement, and (...) many properties of words. It is inconsistent with the anatomy and neural control of the human vocal tract. And it is weakened by experiments suggesting that speech perception cannot be reduced to primate audition, that word learning cannot be reduced to fact learning, and that at least one gene involved in speech and language was evolutionarily selected in the human lineage but is not specific to recursion. The recursion-only claim, we suggest, is motivated by Chomsky’s recent approach to syntax, the Minimalist Program, which de-emphasizes the same aspects of language. The approach, however, is sufficiently problematic that it cannot be used to support claims about evolution. We contest related arguments that language is not an adaptation, namely that it is “perfect,” non-redundant, unusable in any partial form, and badly designed for.. (shrink)

In the late 1980s, there were many who heralded the emergence of connectionism as a new paradigm – one which would eventually displace the classically symbolic methods then dominant in AI and Cognitive Science. At present, there remain influential connectionists who continue to defend connectionism as a more realistic paradigm for modeling cognition, at all levels of abstraction, than the classical methods of AI. Not infrequently, one encounters arguments along these lines: given what we know about neurophysiology, it is just (...) not plausible to suppose that our brains are digital computers. Thus, they could not support a classical architecture. I argue here for a middle ground between connectionism and classicism. I assume, for argument's sake, that some form(s) of connectionism can provide reasonably approximate models – at least for lower-level cognitive processes. Given this assumption, I argue on theoretical and empirical grounds that most human mental skills must reside in separate connectionist modules or sub-networks. Ultimately, it is argued that the basic tenets of connectionism, in conjunction with the fact that humans often employ novel combinations of skill modules in rule following and problem solving, lead to the plausible conclusion that, in certain domains, high level cognition requires some form of classical architecture. During the course of argument, it emerges that only an architecture with classical structure could support the novel patterns of information flow and interaction that would exist among the relevant set of modules. Such a classical architecture might very well reside in the abstract levels of a hybrid system whose lower-level modules are purely connectionist. (shrink)

This article describes an approach to connectionist language research that relies on the development of grammar formalisms rather than computer models. From formulations of the fundamental theoretical commitments of connectionism and of generative grammar, it is argued that these two paradigms are mutually compatible. Integrating the basic assumptions of the paradigms results in formal theories of grammar that centrally incorporate a certain degree of connectionist computation. Two such grammar formalisms—Harmonic Grammar and Optimality Theory —are briefly introduced to illustrate grammar‐based approaches (...) to connectionist language research. The strengths and weaknesses of grammar‐based research and more traditional model‐based research are argued to be complementary, suggesting a significant role for both strategies in the spectrum of connectionist language research. (shrink)