In the twentieth century, no person epitomized more dramatically the “Renaissance mind” than Herbert A. Simon (1916–2001). In aworking life spanning over 60 years, Simon made seminal contributions to administrative theory, axiomatic foundations of physics, economics, sociology, econometrics, cognitive psychology, logic of scientific discovery, and artificial intelligence. Simon's life of the mind, thus, affords nothing less than a “laboratory” in which to observe and examine at close quarters the phenomenon ofmultidisciplinary creativity. In this paper, we attempt to shed some light (...) on the nature of Simon's creativity and the nature of his particular Renaissance mind. In particular, we have attempted here to articulate thecognitive styleunderlying Simon's multidisciplinary creativity. (shrink)

In interpreting measurements of brain processes it is necessary to make the model used explicit. A concept such as attention cannot be used in the description of brain activities without a model of the relation of mental and neural processes.

The purpose of this paper is to set forth a sense in which programs can and do explain behavior, and to distinguish from this a number of senses in which they do not. Once we are tolerably clear concerning the sort of explanatory strategy being employed, two rather interesting facts emerge; (1) though it is true that programs are "internally represented," this fact has no explanatory interest beyond the mere fact that the program is executed; (2) programs which are couched (...) in information processing terms may have an explanatory interest for a given range of behavior which is independent of physiological explanations of the same range of behavior. (shrink)

In this article the question is raised whether artificial intelligence has any psychological relevance, i.e. contributes to our knowledge of how the mind/brain works. It is argued that the psychological relevance of artificial intelligence of the symbolic kind is questionable as yet, since there is no indication that the brain structurally resembles or operates like a digital computer. However, artificial intelligence of the connectionist kind may have psychological relevance, not because the brain is a neural network, but because connectionist networks (...) exhibit operating characteristics which mimic operant behavior. Finally it is concluded that, since most of the work done so far in AI and Law is of the symbolic kind, it has as yet contributed little to our understanding of the legal mind. (shrink)

This book delivers much more than its title appears to promise; it is not merely a description of current methods for remotely monitoring brain activity. It primarily concentrates on just one such method: positron emission tomography, but it demonstrates beautifully how far that technique can now take us in the quest to discover the mechanisms underlying thought.

This article examines how experimental psychology experienced a revolution as cognitive science replaced behaviorism in the mid-20th century. This transition in the scientific account of human nature involved making normal what had once been normative: borrowing ideas of democratic thinking from political culture and conceptions of good thinking from philosophy of science to describe humans as active, creatively thinking beings, rather than as organisms that simply respond to environmental conditions. Reflexive social and intellectual practices were central to this process as (...) cognitive scientists used anti-positivist philosophy of science simultaneously to justify their own work as valid and also as a model of human thinking. In the process, the normative philosophy of science or ‘good academic thinking’ that cognitive scientists used to reshape the discipline of psychology and characterize themselves became, at the same time, the descriptive model of human nature. (shrink)

Computational models will play an important role in our understanding of human higher‐order cognition. How can a model's contribution to this goal be evaluated? This article argues that three important aspects of a model of higher‐order cognition to evaluate are (a) its ability to reason, solve problems, converse, and learn as well as people do; (b) the breadth of situations in which it can do so; and (c) the parsimony of the mechanisms it posits. This article argues that fits of (...) models to quantitative experimental data, although valuable for other reasons, do not address these criteria. Further, using analogies with other sciences, the history of cognitive science, and examples from modern‐day research programs, this article identifies five activities that have been demonstrated to play an important role in our understanding of human higher‐order cognition. These include modeling within a cognitive architecture, conducting artificial intelligence research, measuring and expanding a model's ability, finding mappings between the structure of different domains, and attempting to explain multiple phenomena within a single model. (shrink)

Introductory Abstract Philosophers of science, in the course of making a sharp distinction between the tasks of the philosopher and those of the scientist, have pointed to the possibility of an empirical science of induction. A comparative psychology of knowledge processes is offered as one aspect of this potential enterprise. From fragments of such a psychology, methodological suggestions are drawn relevant to several chronic problems in the social sciences, including the publication of negative results from novel explorations, the operational diagnosis (...) of dispositions, the status of aggregates of persons as social entities, and the validation of psychological tests. (shrink)

To explain social learning without invoking the cognitively complex concept of imitation, many learning mechanisms have been proposed. Borrowing an idea used routinely in cognitive psychology, we argue that most of these alternatives can be subsumed under a single process, priming, in which input increases the activation of stored internal representations. Imitation itself has generally been seen as a This has diverted much research towards the all-or-none question of whether an animal can imitate, with disappointingly inconclusive results. In the great (...) apes, however, voluntary, learned behaviour is organized hierarchically. This means that imitation can occur at various levels, of which we single out two clearly distinct ones: the a rather detailed and linear specification of sequential acts, and the a broader description of subroutine structure and the hierarchical layout of a behavioural Program level imitation is a high-level, constructive mechanism, adapted for the efficient learning of complex skills and thus not evident in the simple manipulations used to test for imitation in the laboratory. As examples, we describe the food-preparation techniques of wild mountain gorillas and the imitative behaviour of orangutans undergoing to the wild. Representing and manipulating relations between objects seems to be one basic building block in their hierarchical programs. There is evidence that great apes suffer from a stricter capacity limit than humans in the hierarchical depth of planning. We re-interpret some chimpanzee behaviour previously described as and suggest that all great apes may be able to imitate at the program level. Action level imitation is seldom observed in great ape skill learning, and may have a largely social role, even in humans. (shrink)

Posner & Raichle's (1994) exciting, wonderfully illustrated book describes the past successes and future potential of the relatively noninvasive imaging of the nervous systems of living people. The focus has been on cognitive processes but there is no reason why emotional and motivational systems cannot also be tapped. Although the authors do not formally address such contentious issues as consciousness and the private experience of other species, imaging methods may hold promise for helping us to understand these phenomena, as well (...) as to integrate psychological processes into ethological and phylogenetic research in general. (shrink)

Various authors advocate consideration of stakeholder value concerns in organizational decision making. Brenner and Cochran (1990, 1991) propose a stakeholder theory of the firm which contains several propositions and a stakeholder value matrix. In order to begin any stakeholder rnodel validation, an approach is needed to measure stakeholder value and influence weights. We propose a multicriteria decision modeling approach, utilizing the analytic hierarchy process, to estimate stakeholder value matrix weights. This approach is illustrated using a simplified example and suggestions are (...) made regarding the process needed to begin to validate the stakeholder theory of the firm. (shrink)

L’auteur établit le concept nouveau de compromission numérique selon lequel les algorithmes, et les acteurs qui les développent, ne se situent plus dans une logique de réplication de modèles de raisonnement ou de comportement humains, mais dans l’invention de modèles qui changent profondément les environnements physiques et sociaux où ils sont implantés. Dans ce scénario, la compromission numérique affecte le fait social. Uber ou Volskwagen ont créé des algorithmes déloyaux, capables de tromper la vigilance humaine aussi bien que la vigilance (...) machine (des autorités de régulation). L’auteur montre que le chemin emprunté est un chemin de compromission sociétale. (shrink)

: The work of Herbert A. Simon has drawn increasing attention from modern scholars who argue that Simon's work changed during the Cold War. This is due to the fact that Simon seemingly changed the substance of his research in the 1950s. This paper argues that Simon did not change in any significant way, but was lead by his interest in decision making and rationality into areas of economics, political science, sociology, psychology, organization theory, and computer science. He used elements (...) of different disciplines to address his overall interest and there is therefore a considerable continuity in Simon's work. This paper also provides part of a background for the recent increase of interest in Simon's ideas by providing some details of the RAND Corporation and the Ford Foundation's support of scientific research through the post war years in general, and their connections to the behavioral science research at Carnegie Mellon University in particular. (shrink)

When philosophers discuss the possibility of machines making scientific discoveries, they typically focus on discoveries in physics, biology, chemistry and mathematics. Observing the rapid increase of computer-use in science, however, it becomes natural to ask whether there are any scientific domains out of reach for machine discovery. For example, could machines also make discoveries in qualitative social science? Is there something about humans that makes us uniquely suited to studying humans? Is there something about machines that would bar them from (...) such activity? A close look at the methodology of interpretive social science reveals several abilities necessary to make a social scientific discovery, and one capacity necessary to possess any of them is imagination. For machines to make discoveries in social science, therefore, they must possess imagination algorithms. (shrink)

Among the many ideas that go by the name of “enactivism” there is the idea that by “cognition” we should understand what is more commonly taken to be behavior. For clarity, label such forms of enactivism “enactivismb.” This terminology requires some care in evaluating enactivistb claims. There is a genuine risk of enactivist and non-enactivist cognitive scientists talking past one another. So, for example, when enactivistsb write that “cognition does not require representations” they are not necessarily denying what cognitivists claim (...) when they write that “cognition requires representations.” This paper will draw attention to instances of some of these unnecessary confusions. (shrink)

A response to a recent critique by Cem Bozşahin of the theory of syntactic semantics as it applies to Helen Keller, and some applications of the theory to the philosophy of computer science.

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.Higher-level analyses of these connectionist models reveal subtle relations to symbolic models. Parallel connectionist memory and linguistic processes are hypothesized to give rise to processes that are describable at a higher level as sequential rule application. At the lower level, computation has the character of massively parallel satisfaction of soft numerical constraints; at the higher level, this can lead to competence characterizable by hard rules. Performance will typically deviate from this competence since behavior is achieved not by interpreting hard rules but by satisfying soft constraints. The result is a picture in which traditional and connectionist theoretical constructs collaborate intimately to provide an understanding of cognition. (shrink)

This chapter describes the conceptual foundations of cognitive science during its establishment as a science in the 20th century. It is organized around the core ideas of individual agency as its basic explanans and information-processing as its basic explanandum. The latter consists of a package of ideas that provide a mathematico-engineering framework for the philosophical theory of materialism.

In this article, we discuss expertise by considering the activity of reading. Cognitive scientists have traditionally conceptualised reading as a single, well-defined task, namely the decoding of letter sequences into meaningful sequences of speech sounds. This definition captures a core feature of the reading activity at the computational level, but it is an overly narrow model of how reading behaviour occurs in the real world. We propose a more expansive model of expertise. In our view, expertise in general is best (...) conceptualised as a distributed process that takes place within a cultural-cognitive ecosystem. Our model allows for a more inclusive view of expertise in reading. We argue that reading is better understood as a manifold task domain that admits multiple reasonable criteria for evaluating performance. We draw on ethnographic data to show how our model allows for a wider appreciation of what expertise in reading amounts to in educational contexts. (shrink)

Summary The typology of scientific research is of considerable importance for the development of the methodology of science. Apart from the typology accepted by UNESCO, the author introduces a new typology of scientific research, distinguishing the following types of research: diagnostic and generalizing, chronological and systematic, heuristic and justificatory, universalist and specialist, theoretical and practical, explanatory and descriptive and others.

When thought overwhelms the mind, the mind uses the body and the world. Several studies reveal ways that people alone or together use gesture and marks on paper to structure and augment their thought for comprehension, inference, and discovery. The studies show that the mapping of thought to gesture or the page is more direct than the arbitrary mapping to language and suggest that these forms of visual/spatial/action representation are used to “translate” language into mental representations. It is argued that (...) actions in space create patterns in the world that reflect abstractions, that the actions are incorporated into gestures and the patterns into diagrams, a network that integrates gesture, action, the designed world, and abstraction dubbed spraction. (shrink)

When thought overwhelms the mind, the mind uses the body and the world. Several studies reveal ways that people alone or together use gesture and marks on paper to structure and augment their thought for comprehension, inference, and discovery. The studies show that the mapping of thought to gesture or the page is more direct than the arbitrary mapping to language and suggest that these forms of visual/spatial/action representation are used to “translate” language into mental representations. It is argued that (...) actions in space create patterns in the world that reflect abstractions, that the actions are incorporated into gestures and the patterns into diagrams, a network that integrates gesture, action, the designed world, and abstraction dubbed spraction. (shrink)

Posner & Raichle (1994) is a nice addition to the Scientific American Library and the average reader will both enjoy the book and learn a great deal. As an activeresearcher, however, I find the book disappointing in many respects. My two major disappointments are in the illusion of computation that is created throughout the volume and in the inadequate perspective of the presentation on visual attention.