Improving performance through concept formation and conceptual clustering

Research from June 1989 through October 1992 focussed on concept formation, clustering, and supervised learning for purposes of improving the efficiency of problem-solving, planning, and diagnosis. These projects resulted in two dissertations on clustering, explanation-based learning, and means-ends planning, and publications in conferences and workshops, several book chapters, and journals; a complete Bibliography of NASA Ames supported publications is included. The following topics are studied: clustering of explanations and problem-solving experiences; clustering and means-end planning; and diagnosis of space shuttle and space station operating modes

The structure and formation of natural categories

Categorization and concept formation are critical activities of intelligence. These processes and the conceptual structures that support them raise important issues at the interface of cognitive psychology and artificial intelligence. The work presumes that advances in these and other areas are best facilitated by research methodologies that reward interdisciplinary interaction. In particular, a computational model is described of concept formation and categorization that exploits a rational analysis of basic level effects by Gluck and Corter. Their work provides a clean prescription of human category preferences that is adapted to the task of concept learning. Also, their analysis was extended to account for typicality and fan effects, and speculate on how the concept formation strategies might be extended to other facets of intelligence, such as problem solving

The Language of Bias: A Linguistic Approach to Understanding Intergroup Relations

[Excerpt] This chapter explores the role of language in the relationship between diversity and team performance. Specifically, we consider how a linguistic approach to social categorization may be used to study the social psychological mechanisms that underlie diversity effects. Using the results of a study examining the effects of gender, ethnicity and tenure on language abstraction, we consider the potential implications for team processes and effectiveness. In addition, we propose a revised team input-process-output model that highlights the potential effects of language on team processes. We conclude by suggesting directions for future research linking diversity, linguistic categorization and team effectiveness

Understanding analogical reasoning : viewpoints from psychology and related disciplines

Analogy and metaphor have a long history of study in linguistics, education, philosophy and psychology. Consensus over what analogy is or how analogy functions in language and thought, however, has been elusive. This paper, the first in a two part series, examines these various research traditions, attempting to bring out major lines of agreement over the role of analogy in individual human experience. As well as being a general literature review which may be helpful for newcomers to the study of analogy, this paper attempts to extract from these literatures existing theories, models and concepts which may be interesting or useful for computational studies of analogical reasoning

Exerting self-control induces a narrow mindset.

Self-control theories have focused on various aspects of the processes involved in exerting self-control. In the present paper, we intend to add to this literature by demonstrating that exerting self-control leads one to narrow one's attention and cognition, inducing a narrow mindset. We demonstrate this in three studies. Participants who exerted self-control applied a narrower view (Study 1), applied a narrower categorization (Study 2), and used more concrete language (Study 3) than participants who did not exert self-control. Results are discussed in light of the possibility that a narrow mindset enhances performance on the self-control task at hand at the cost of poorer performance on other tasks.Self-control; Theory; Processes; Cognition; Performance; Consumer behavior;

The Bayesian boom: good thing or bad?

A series of high-profile critiques of Bayesian models of cognition have recently sparked controversy. These critiques question the contribution of rational, normative considerations in the study of cognition. The present article takes central claims from these critiques and evaluates them in light of specific models. Closer consideration of actual examples of Bayesian treatments of different cognitive phenomena allows one to defuse these critiques showing that they cannot be sustained across the diversity of applications of the Bayesian framework for cognitive modeling. More generally, there is nothing in the Bayesian framework that would inherently give rise to the deficits that these critiques perceive, suggesting they have been framed at the wrong level of generality. At the same time, the examples are used to demonstrate the different ways in which consideration of rationality uniquely benefits both theory and practice in the study of cognition