This paper argues against Oaksford and Chater's claim that logicist cognitive science is not possible. It suggests that there arguments against logicist cognitive science are too closely tied to the account of Pylyshyn and of Fodor, and that the correct way of thinking about logicist cognitive science is in a mental models framework.

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)

This paper presents a general computational treatment of how mammals are able to deal with visual objects and environments. The model tries to cover the entire range from behavior and phenomenological experience to detailed neural encodings in crude but computationally plausible reductive steps. The problems addressed include perceptual constancies, eye movements and the stable visual world, object descriptions, perceptual generalizations, and the representation of extrapersonal space.The entire development is based on an action-oriented notion of perception. The observer is assumed to (...) be continuously sampling the ambient light for information of current value. The central problem of vision is taken to be categorizing and locating objects in the environment. The critical step in this process is the linking of visual information to symbolic object descriptions; this is calledindexing, from the analogy of identifying a book from index terms. The system must also identifysituationsand use this knowledge to guide movement and other actions in the environment. The treatment focuses on the different representations of information used in the visual system.The four representational frames capture information in the following forms: retinotopic, head-based, symbolic, and allocentric. The functional roles of the four frames, the communication among them, and their suggested neurophysiological realization constitute the core of the paper. The model is perforce crude, but appears to be consistent with all relevant findings. (shrink)

Much of the progress in the fields constituting cognitive science has been based upon the use of explicit information processing models, almost exclusively patterned after conventional serial computers. An extension of these ideas to massively parallel, connectionist models appears to offer a number of advantages. After a preliminary discussion, this paper introduces a general connectionist model and considers how it might be used in cognitive science. Among the issues addressed are: stability and noise‐sensitivity, distributed decision‐making, time and sequence problems, and (...) the representation of complex concepts. (shrink)

This paper critically examines the claim that parallel distributed processing (PDP) networks are autonomous learning systems. A PDP model of a simple distributed associative memory is considered. It is shown that the 'generic' PDP architecture cannot implement the computations required by this memory system without the aid of external control. In other words, the model is not autonomous. Two specific problems are highlighted: (i) simultaneous learning and recall are not permitted to occur as would be required of an autonomous system; (...) (ii) connections between processing units cannot simultaneously represent current and previous network activation as would be required if learning is to occur. Similar problems exist for more sophisticated networks constructed from the generic PDP architecture. We argue that this is because these models are not adequately constrained by the properties of the functional architecture assumed by PDP modelers. It is also argued that without such constraints, PDP researchers cannot claim to have developed an architecture radically different from that proposed by the Classical approach in cognitive science. (shrink)

This book presents views of the concept of intention and its relationship to communication from three perspectives: philosphy, linguistics, and artificial intelligence. The book is a record of a workshop held in 1987.

Most Artificial Intelligence theories of language either assume a syntactic component which serves as “front end” for the rest of the system, or else reject all attempts at distinguishing modules within the comprehension system. In this paper we will present an alternative which, while keeping modularity, will account for several puzzles for typical “syntax first” theories. The major addition to this theory is a “marker passing” (or “spreading activation”) component, which operates in parallel to the normal syntactic component.

Linguistics. Artificial intelligence. Related fields. Computation.

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

This book represents the first major attempt by any author to provide an integrated account of the evidence for bias in human reasoning across a wide range of disparate psychological literatures. The topics discussed involve both deductive and inductive reasoning as well as statistical judgement and inference. In addition, the author proposes a general theoretical approach to the explanations of bias and considers the practical implications for real world decision making. The theoretical stance of the book is based on a (...) distinction between preconscious heuristic processes which determine the mental representation of 'relevant' features of the problem content, and subsequent analytic reasoning processes which generate inferences and judgements. Phenomena discussed and interpreted within this framework include feature matching biases in propositional reasoning, confirmation bias, biasing and debiasing effects of knowledge on reasoning, and biases in statistical judgement normally attributed to 'availability' and 'representativeness' heuristics. In the final chapter, the practical consequences of bias for real life decision making are considered, together with various issues concerning the problem of 'debiasing'. The major approaches discussed are those involving education and training on the one hand, and the development of intelligent software and interactive decision aids on the other. (shrink)

Computing Methodologies -- Artificial Intelligence.

The book can also be used as an excellent text for graduate-level courses in AI, operations research, or applied probability.

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.

From a leading authority in artificial intelligence, this book delivers a synthesis of the major modern techniques and the most current research in natural language processing. The approach is unique in its coverage of semantic interpretation and discourse alongside the foundational material in syntactic processing.

Originally published in 1982, this was an extensive and up-to-date review of research into the psychology of deductive reasoning, Jonathan Evans presents an alternative theoretical framework to the rationalist approach which had dominated much of the published work in this field at the time. The review falls into three sections. The first is concerned with elementary reasoning tasks, in which response latency is the prime measure of interest. The second and third sections are concerned with syllogistic and propositional reasoning respectively, (...) in which interest has focused on the explanation of frequently observed logical errors. In an extended discussion it is argued that reasoning processes are content specific, and give little indication of the operation of any underlying system of logical competence. Finally, a dual process theory of reasoning, with broad implications and connections with other fields of psychology, is elaborated and assessed in the light of recent evidence. (shrink)

Elements of Episodic Memory is a classic text in the psychology literature. It had a significant influence on research in the area has been much sought after in recent years. Finally, it has now been made available again with this reissue, the text unchanged from the original.

"Its publication should be a major event for cognitive linguistics and should pose a major challenge for cognitive science. In addition, it should have repercussions in a variety of disciplines, ranging from anthropology and psychology to epistemology and the philosophy of science.... Lakoff asks: What do categories of language and thought reveal about the human mind? Offering both general theory and minute details, Lakoff shows that categories reveal a great deal."—David E. Leary, American Scientist.

This study synthesizes current information from the various fields of cognitive science in support of a new and exciting theory of mind. Most psychologists study horizontal processes like memory and information flow; Fodor postulates a vertical and modular psychological organization underlying biologically coherent behaviors. This view of mental architecture is consistent with the historical tradition of faculty psychology while integrating a computational approach to mental processes. One of the most notable aspects of Fodor's work is that it articulates features not (...) only of speculative cognitive architectures but also of current research in artificial intelligence. (shrink)

First published in 1982. Routledge is an imprint of Taylor & Francis, an informa company.

[Edited from Conclusion section:] We have looked at various challenging issues to do with getting connectionism to cope with high-level cognitive activities such a reasoning and natural language understanding. The issues are to do with various facets of generalization that are not commonly noted. We have been concerned in particular with the special forms these issues take in the arena of propositional attitude processing. The main problems we have looked at are: (1) The need to construct explicit representations of generalizations, (...) not just generalize correctly to individual cases; (2) The need to be able to match two or more complex short-term information structures, to enable rapid generalization from recent examples rather than from long-term memories; (3) The need to represent and reason with anomalous combinations of concepts; (4) The need to perform embedded reasoning. This presents special problems for systems using non-concatenative representations (as in mainstream connectionist approaches). We also touched on vague quantification in attitude report complements. Neither this topic nor that of analogies between short-term structures (point 2) has been adequately addressed in the symbolic framework, let alone in connectionism. -/- The opportunities and problems covered are put forward as things worth being optimistic about or pessimistic about, respectively. They are not put forward as decisive arguments for or against connectionism. The hope is that this chapter contributes to a greater understanding of the connectionist/symbolist gap by presenting some unusual issues and by throwing new light on some well known ones. (shrink)

It seems to me that the ingredients of most theories both in Artificial Intelligence and in Psychology have been on the whole too minute, local, and unstructured to account–either practically or phenomenologically–for the effectiveness of common-sense thought. The "chunks" of reasoning, language, memory, and "perception" ought to be larger and more structured; their factual and procedural contents must be more intimately connected in order to explain the apparent power and speed of mental activities.

There is disagreement over the notion of representation in cognitive science. Many investigators equate representations with symbols, that is, with syntactically defined elements in an internal symbol system. In recent years there have been two challenges to this orthodoxy. First, a number of philosophers, including many outside the symbolist orthodoxy, have argued that "representation" should be understood in its classical sense, as denoting a "stands for" relation between representation and represented. Second, there has been a growing challenge to orthodoxy under (...) the banner of connectionism. Although this connectionist challenge has evoked emotionally-charged rebukes from the symbolist camp, connectionists as a group have not articulated a conception of representation to replace the symbolist view. Nonetheless, most agree on the need for a nonsymbolic notion of representation. This paper advances a "stands for" sense of representation as primary by incorporating it into a general approach to cognitive science consonant with the connectionism. The idea is to marry connectionism to a particular version of functionalism, viz., one that builds its notion of "function" on the similarity between functional analysis in biology and in psychology. It builds on my earlier work adapting and revising Marr's tri-level approach to cognition. My proposal frees Marr's analysis from its moorings in the orthodox symbolic view of representation and allies it with a notion of functional analysis akin to that proposed by Cummins, developed further by Haugeland, and invoked by Millikan and Dretske. Unlike these authors, however, I do not wed representation to a general belief-desire analysis of behavior. Rather, I follow what I take to be the lesson of psychological practice, according to which the investigator does not seek to explain behavior in general but seeks to analyze the cognitive capacities that underlie behavior, such as vision, memory, learning, and linguistic capacities. Accordingly, ascriptions of representational content are made not by working back from belief-desire ascriptions, but in the context of forming psychological models to account for specific cognitive capacities. Because conceptions of representation in cognitive science typically are embedded in a general approach to the study of cognition, arguments for the comparative plausibility of a particular conception of representation must scout these larger frameworks. I therefore begin by characterizing the interlocking set of assumptions that gave life to the orthodox symbolist approach, paying special attention to the complementarity between representation and process that naturally arises from those assumptions. I then consider two versions of an alternative approach to representation: Dretske's and my own. Finally, I urge the merits of the "cognitive capacities" over the "belief-desire" approach to the subject-matter of cognitive science. (shrink)