Fundamental to spatial knowledge in all species are the representations underlying object recognition, object search, and navigation through space. But what sets humans apart from other species is our ability to express spatial experience through language. This target article explores the language ofobjectsandplaces, asking what geometric properties are preserved in the representations underlying object nouns and spatial prepositions in English. Evidence from these two aspects of language suggests there are significant differences in the geometric richness with which objects and places (...) are encoded. When an object is named (i.e., with count nouns), detailed geometric properties – principally the object's shape (axes, solid and hollow volumes, surfaces, and parts) – are represented. In contrast, when an object plays the role of either “figure” (located object) or “ground” (reference object) in a locational expression, only very coarse geometric object properties are represented, primarily the main axes. In addition, the spatial functions encoded by spatial prepositions tend to be nonmetric and relatively coarse, for example, “containment,” “contact,” “relative distance,” and “relative direction.” These properties are representative of other languages as well. The striking differences in the way language encodes objects versus places lead us to suggest two explanations: First, there is a tendency for languages to level out geometric detail from both object and place representations. Second, a nonlinguistic disparity between the representations of “what” and “where” underlies how language represents objects and places. The language of objects and places converges with and enriches our understanding of corresponding spatial representations. (shrink)

The mathematical theory of communication.

'Is there any other point to which you would wish to draw my attention?' 'To the curious incident of the dog in the night-time.' 'The dog did nothing in the night-time.' 'That was the curious incident, ' remarked Sherlock Holmes. The quotation from A. Conan Doyle with which this book begins, is a delightfully appropriate summation of the authors' point of view garnered from their fifteen years of experiments on the psychology of reasoning. Dr. Wason and Dr. Johnson-Laird are intrigued (...) by the extent to which most individuals can be considered naturally rational thinkers. They present here the surprising results of their comprehensive investigations of how humans draw explicit conclusions from evidence. Given a set of assertions, the authors write, to what extent can the individual appreciate all that follows from them by virtue of logic alone, and remain unseduced by plausible, but fallacious conclusions? We are not concerned with whether these assertions are true or false, nor with whether the individual holds them among his beliefs, nor with whether they are sane or silly. At the core of the Psychology of Reasoning is a vigorous discussion that incorporates various illustrations--some of them humorous, all of them fascinating--of the use of reason under a wide variety of different conditions. Particular emphasis is placed on the difficulties involved in dealing with negatively marked information that must be combined and used with other information for reaching conclusions. Thorough treatment is given as well to the search for plausible contexts that will render anomalous or ambiguous statements sensible. The authors have strived to isolate the components ofinference, the basic steps of any kind of deductive activity, in order to determine the psychological processes involved in them. What has been the outcome of this research? Dr. Wason and Dr. Johnson-Laird conclude, our research has suggested that the individual's logical competence may be either enhanced or limited by performance variables. And, of these, content has turned out to be vitally important for revealing, or obscuring structure. At best, we can all think like logicians; at worst, logicians all think like us. (shrink)

A set of hypotheses is formulated for a connectionist approach to cognitive modeling. These hypotheses are shown to be incompatible with the hypotheses underlying traditional cognitive models. The connectionist models considered are massively parallel numerical computational systems that are a kind of continuous dynamical system. The numerical variables in the system correspond semantically to fine-grained features below the level of the concepts consciously used to describe the task domain. The level of analysis is intermediate between those of symbolic cognitive models (...) and neural models. The explanations of behavior provided are like those traditional in the physical sciences, unlike the explanations provided by symbolic models. (shrink)

We present a model of similarity‐based retrieval that attempts to capture three seemingly contradictory psychological phenomena: (a) structural commonalities are weighed more heavily than surface commonalities in similarity judgments for items in working memory; (b) in retrieval, superficial similarity is more important than structural similarity; and yet (c) purely structural (analogical) remindings e sometimes experienced. Our model, MAC/FAC, explains these phenomena in terms of a two‐stage process. The first stage uses a computationally cheap, non‐structural matcher to filter candidate long‐term memory (...) items. It uses content vectors, a redundant encoding of structured representations whose dot product estimates how well the corresponding structural representations will match. The second stage uses SME (structure‐mapping engine) to compute structural matches on the handful of items found by the first stage. We show the utility of the MAC/FAC model through a series of computational experiments: (a) We demonstrate that MAC/FAC can model patterns of access found in psychological data; (b) we argue via sensitivity analyses that these simulation results rely on the theory; and (c) we compare the performance of MAC/FAC with ARCS, an alternate model of similarity‐based retrieval, and demonstrate that MAC/FAC explains the data better than ARCS. Finally, we discuss limitations and possible extensions of the model, relationships with other recent retrieval models, and place MAC/FAC in the context of other recent work on the nature of similarity. (shrink)

This paper explores the difference between Connectionist proposals for cognitive a r c h i t e c t u r e a n d t h e s o r t s o f m o d e l s t hat have traditionally been assum e d i n c o g n i t i v e s c i e n c e . W e c l a i m t h a t t h (...) e m a j o r d i s t i n c t i o n i s t h a t , w h i l e b o t h Connectionist and Classical architectures postulate representational mental states, the latter but not the former are committed to a symbol-level of representation, or to a ‘language of thought’: i.e., to representational states that have combinatorial syntactic and semantic structure. Several arguments for combinatorial structure in mental representations are then reviewed. These include arguments based on the ‘systematicity’ of mental representation: i.e., on the fact that cognitive capacities always exhibit certain symmetries, so that the ability to entertain a given thought implies the ability to entertain thoughts with semantically related contents. We claim that such arguments make a powerful case that mind/brain architecture is not Connectionist at the cognitive level. We then consider the possibility that Connectionism may provide an account of the neural (or ‘abstract neurological’) structures in which Classical cognitive architecture is implemented. We survey a n u m b e r o f t h e s t a n d a r d a r g u m e n t s t h a t h a v e b e e n o f f e r e d i n f a v o r o f Connectionism, and conclude that they are coherent only on this interpretation. (shrink)

Time underlies many interesting human behaviors. Thus, the question of how to represent time in connectionist models is very important. One approach is to represent time implicitly by its effects on processing rather than explicitly (as in a spatial representation). The current report develops a proposal along these lines first described by Jordan (1986) which involves the use of recurrent links in order to provide networks with a dynamic memory. In this approach, hidden unit patterns are fed back to themselves: (...) the internal representations which develop thus reflect task demands in the context of prior internal states. A set of simulations is reported which range from relatively simple problems (temporal version of XOR) to discovering syntactic/semantic features for words. The networks are able to learn interesting internal representations which incorporate task demands with memory demands: indeed, in this approach the notion of memory is inextricably bound up with task processing. These representations reveal a rich structure, which allows them to be highly context‐dependent, while also expressing generalizations across classes of items. These representations suggest a method for representing lexical categories and the type/token distinction. (shrink)

This book presents an attempt to develop a theory of knowledge and a philosophy of mind using ideas derived from the mathematical theory of communication developed by Claude Shannon. Information is seen as an objective commodity defined by the dependency relations between distinct events. Knowledge is then analyzed as information caused belief. Perception is the delivery of information in analog form for conceptual utilization by cognitive mechanisms. The final chapters attempt to develop a theory of meaning by viewing meaning as (...) a certain kind of information-carrying role. (shrink)

This paper argues for a noncognitiveist computationalism in the philosophy of mind. It further argues that both humans and computers have intentionality, that is, their mental states are semantical -- they are about things in their worlds.

Artificial intelligence research has foundered on the issue of representation. When intelligence is approached in an incremental manner, with strict reliance on interfacing to the real world through perception and action, reliance on representation disappears. In this paper we outline our approach to incrementally building complete intelligent Creatures. The fundamental decomposition of the intelligent system is not into independent information processing units which must interface with each other via representations. Instead, the intelligent system is decomposed into independent and parallel activity (...) producers which all interface directly to the world through perception and action, rather than interface to each other particularly much. The notions of central and peripheral systems evaporateeverything is both central and peripheral. Based on these principles we have built a very successful series of mobile robots which operate without supervision as Creatures in standard office environments. (shrink)

Prior to the twentieth century, theories of knowledge were inherently perceptual. Since then, developments in logic, statis- tics, and programming languages have inspired amodal theories that rest on principles fundamentally different from those underlying perception. In addition, perceptual approaches have become widely viewed as untenable because they are assumed to implement record- ing systems, not conceptual systems. A perceptual theory of knowledge is developed here in the context of current cognitive science and neuroscience. During perceptual experience, association areas in the (...) brain capture bottom-up patterns of activation in sensory-motor areas. Later, in a top-down manner, association areas partially reactivate sensory-motor areas to implement perceptual symbols. The stor- age and reactivation of perceptual symbols operates at the level of perceptual components – not at the level of holistic perceptual expe- riences. Through the use of selective attention, schematic representations of perceptual components are extracted from experience and stored in memory (e.g., individual memories of green, purr, hot). As memories of the same component become organized around a com- mon frame, they implement a simulator that produces limitless simulations of the component (e.g., simulations of purr). Not only do such simulators develop for aspects of sensory experience, they also develop for aspects of proprioception (e.g., lift, run) and introspec- tion (e.g., compare, memory, happy, hungry). Once established, these simulators implement a basic conceptual system that represents types, supports categorization, and produces categorical inferences. These simulators further support productivity, propositions, and ab- stract concepts, thereby implementing a fully functional conceptual system. Productivity results from integrating simulators combinato- rially and recursively to produce complex simulations. Propositions result from binding simulators to perceived individuals to represent type-token relations. Abstract concepts are grounded in complex simulations of combined physical and introspective events. Thus, a per- ceptual theory of knowledge can implement a fully functional conceptual system while avoiding problems associated with amodal sym- bol systems. Implications for cognition, neuroscience, evolution, development, and artificial intelligence are explored. (shrink)

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

In this study on deduction, the authors argue that people reason by imagining the relevant state of affairs, ie building an internal model of it, formulating a tentative conclusion based on this model and then searching for alternative models.

Do children have a theory of mind? If they do, at what age is it acquired? What is the content of the theory, and how does it differ from that of adults? The Child's Theory of Mind integrates the diverse strands of this rapidly expanding field of study. It charts children's knowledge about a fundamental topic - the mind - and characterizes that developing knowledge as a coherent commonsense theory, strongly advancing the understanding of everyday theories as well as the (...) commonsense theory of mind. Wellman presents evidence that children as young as age three do possess a commonsense theory of mind - that they grasp the distinction between mental constructs and physical entities and that they have an understanding of the relationship between individuals' mental states and their overt actions. He delves in detail into questions about the nature of adults' commonsense theories of mind and about the nature of commonsense theories. Wellman then examines the content of the three-year-old's theory of mind, the nature of children's notions of mind before age three, the changes in the theory during subsequent development from ages three to six, and the young child's conception of mind in comparison with those of older children and adults. Henry M. Wellman is a Professor in the Department of Psychology and the Center for Human Growth and Development at the University of Michigan at Ann Arbor. (shrink)

Lance Rips describes a unified theory of natural deductive reasoning and fashions a working model of deduction, with strong experimental support, that is capable of playing a central role in mental life.

Much of the cognitive lies beyond articulate, discursive thought, beyond the reach of current computational notions. In Sketches of Thought, Vinod Goel argues that the cognitive computational conception of the world requires our thought processes to be precise, rigid, discrete, and unambiguous; yet there are dense, ambiguous, and amorphous symbol systems, like sketching, painting, and poetry, found in the arts and much of everyday discourse that have an important, non-trivial place in cognition. Goel maintains that while on occasion our thoughts (...) do conform to the current computational theory of mind, they often are - indeed must be - vague, fluid, ambiguous, and amorphous. He argues that if cognitive science takes the classical computational story seriously, it must deny or ignore these processes, or at least relegate them to the realm of the nonmental. Along the way, Goel makes a number of significant and controversial interim points. He shows that there is a principled distinction between design and nondesign problems, that there are standard stages in the solution of design problems, that these stages correlate with the use of different types of external symbol systems, that these symbol systems are usefully individuated in Nelson Goodman's syntactic and semantic terms, and that different cognitive processes are facilitated by different types of symbol systems. (shrink)

The editor of this volume takes it to mean that a prior experience affects behavior without the individual's appreciation (ability to report) of this...

A model of writing in cognitive development, Understanding the Representational Mind synthesizes the burgeoning literature on the child’s theory of mind to provide an integrated account of children’s understanding of representational and mental processes, which is crucial in their acquisition of our commonsense psychology. Perner describes experimental work on children’s acquisition of a theory of mind and representation, offers a theoretical account of this acquisition, and gives examples of how the increased sophistication in children’s theory of mind improves their understanding (...) of social interaction and how, in the case of autistic children, an impairment results in social ineptitude. He analyzes the concepts of representation and metarepresentation as they appear in current discussion in the philosophy of cognitive science and explains how the unfolding of mental representation enables infants to comprehend change over time, engage in pretence, and use representational systems like pictures and language. Perner goes on to show that around age four children become able to understand the representational nature of pictures and language and to distinguish appearance from reality. Introducing basic distinctions in philosophy of mind for characterizing the mental, Perner discusses differences in how commonsense and cognitive psychology view the mind. Tracing the onset of a commonsense psychology in the social and emotional awareness of early infancy, he reveals how the child begins to take a cognitive, representational view of the mind with repercussions for children’s episodic memory, self control, and their ability to engage in deception. Perner concludes by describing the observed developmental changes as a case of theory change And contrasts his thesis with competing proposals. Josef Perner is Lecturer in Experimental Psychology at Sussex University, Brighton, England. (shrink)

This long-awaited work by prominent Harvard psychologist Stephen Kosslyn integrates a twenty-year research program on the nature of high-level vision and mental ...

Hutchins examines a set of phenomena that have fallen between the established disciplines of psychology and anthropology, bringing to light a new set of relationships between culture and cognition.