Abstract not found

A reconceptualization of stigma is presented that changes the emphasis from the devaluation of an individual's identity to the process by which individuals who satisfy certain criteria come to be excluded from various kinds of social interactions. The authors propose that phenomena currently placed under the general rubric of stigma involve a set of distinct psychological systems designed by natural selection to solve specific problems associated with sociality. In particular, the authors suggest that human beings possess cognitive adaptations designed to cause them to avoid poor social exchange partners, join cooperative groups (for purposes of between-group competition and exploitation), and avoid contact with those who are differentially likely to carry communicable pathogens. The evolutionary view contributes to the current conceptualization of stigma by providing an account of the ultimate function of Stigmatization and helping to explain its consensual nature.

Research on judgment and choice has been dominated by functionalist assumptions that depict people as either intuitive scientists animated by epistemic goals or intuitive economists animated by utilitarian ones. This article identifies 3 alternative social functionalist starting points for inquiry: people as pragmatic politicians trying to cope with accountability demands from key constituencies in their lives, principled theologians trying to protect sacred values from secular encroachments, and prudent prosecutors trying to enforce social norms. Each functionalist framework stimulates middle-range theories that specify (a) cognitive–affective–behavioral strategies of coping with adaptive challenges and (b) the implications of these coping strategies for identifying empirical and normative boundary conditions on judgmental tendencies classified as errors or biases within the dominant research programs. Once an esoteric specialty of a small cadre of cognitive psychologists, experimental research on judgment and choice has—to judge just by citation counts—become psychology’s leading intellectual export to the social sciences as well as to a host of applied fields. The influence of this research program has spread (critics might say “metastasized”) into such diverse domains as

One of the defining features of intelligent behaviour is the ordering of individual expressed actions into coherent, apparently rational patterns. Psychology has long assumed that hierarchical and sequential structures internal to the intelligent agent underlie this expression. Recently these assumptions have been challenged by claims that behaviour controlled by such structures is necessarily rigid, brittle, and incapable of reacting quickly and opportunistically to changes in the environment (Hendriks-Jansen 1996, Goldfield 1995, Brooks 1991a). This dissertation is intended to support the hypothesis that sequential and hierarchical structures are necessary to intelligent behaviour, and to refute the above claims of their impracticality. Three forms of supporting evidence are provided: • a demonstration in the form of experimental results in two domains that structured intelligence can lead to robust and reactive behaviour, • a review of recent research results and paradigmatic trends within artificial intelligence, and • a similar examination of related research in natural intelligence.

This proposal was originally a short paper relating representations of intelligence between three fields: psychology, neuroscience and artificial intelligence (AI). I particularly emphasize the role of modularity in these three areas. To my knowledge, this paper was never published — it was written on commission, but several years ago and I have just done yet another web search to find it. Further,

Finding a useful abstract representation is fundamental to solving many difficult problems in software engineering. In order to better understand how representations are actually used in key collaborative software engineering tasks, this empirical study examined all of the spoken representations of soflware behavior in 9 domain analysis sessions. It found that about 70 % of them were metaphorical, representing system behavior as physical movement of objects, as perceptual processes, or in anthropomorphic terms ascribing beliefs and desires to the system. The pattern of use of these representations indicates 1) that they were not merely temporary placeholders, but rather their use persisted even when a specialized and more formal vocabulary had been developed, and 2) the metaphoric descriptions appear to reflect actual use of metaphor, rather than just a choice of vocabulary. The use of metaphor is explained in terms d how well they serve human cognitive abilities and collaborative needs. The predominance of metaphorical representations in synchronous collaborative sessions raises important issues about the possible misleading effects of metaphorical thinking. It also raises questions about the compatibility of the spoken representations with other representations (e.g., diagrams, specification languages) that trigger, and capture the results of, the verbal collaborative work.

Abstract not found

For millennia humans have sought, organized, and used information as they learned and evolved patterns of human information behaviors to resolve their human problems and survive. However, despite the current focus on living in an “information age, ” we have a limited evolutionary understanding of human information behavior. In this article the authors examine the current three interdisciplinary approaches to conceptualizing how humans have sought information including (a) the everyday life information seeking–sense-making approach, (b) the information foraging approach, and (c) the problem–solution perspective on information seeking approach. In addition, due to the lack of clarity regarding the role of information use in information behavior, a fourth information approach is provided based on a theory of information use. The use theory proposed starts from an evolutionary psychology notion that humans are able to adapt to their environment and survive because of our modular cognitive architecture. Finally, the authors begin the process of conceptualizing these diverse approaches, and the various aspects or elements of these approaches, within an integrated model with consideration of information use. An initial integrated model of these different approaches with information use is proposed.

Abstract: L. Cosmides and J. Tooby, leaders in the field of evolutionary psychology, have claimed that an evolutionary perspective toward psychology requires both that psychologists conceive of psychological processes as domain specific and that psychologists view all adaptive behavior as the product of cognition. In fact, we argue, an evolutionary perspective commits psychology to neither of these positions. The real value of evolutionary theory for psychology, we contend, lies in the heuristic role it plays in determining the function of psychological mechanisms and in the depth it contributes to explanations of why psychological processes have the properties they do. Quite unexpectedly, cognitive psychologists find their field intimately connected to a whole new intellectual landscape that had previously seemed remote, unfamiliar, and all but irrelevant. Yet the proliferating connections tying together the cognitive and evolutionary communities promise to transform both fields, with each supplying necessary principles, methods, and a species of rigor that

Brain/mind modularity is a contentious issue in cognitive science. Cognitivists tend to conceive of the mind as a set of distinct specialized modules and they believe that this rich modularity is basically innate. Cognitivist modules are theoretical entities which are postulated in "boxes-and-arrows" models used to explain behavioral data. On the other hand, connectionists tend to think that the mind is a more homogeneous system that basically genetically inherits only a general capacity to learn from experience and that if there are modules they are the result of development and learning rather than being innate. In this chapter we argue for a form of connectionism which is not anti-modularist or anti-innatist. Connectionist modules are anatomically separated and/or functionally specialized parts of a neural network and they may be the result of a process of evolution in a population of neural networks. The new approach, Evolutionary Connectionism, does not only allow us to simulate how genetically inherited information can spontaneously emerge in populations of neural networks, instead of being arbitrarily hardwired in the neural networks by the researcher, but it makes it possible to explore all sorts of interactions between evolution at the population level and learning at the level of the individual that determine the actual phenotype. Evolutionary Connectionism shares the main goal of Evolutionary Psychology, that is, to develop a psychology informed by the importance of evolutionary process in shaping the inherited architecture of human mind, but differs from Evolutionary Psychology for three main reasons: (1) it uses neural networks rather than cognitive models for interpreting human behavior; (2) it adopts computer simulations for testing evolutionary scenarios; (3) it has a less pan-adaptivistic view of