Situational irony concerns what it is about a situation that causes people to describe it as ironic. Although situational irony is as complex and commonplace as verbal and literary irony, it has received nowhere near the same attention from cognitive scientists and other scholars. This paper presents the bicoherence theory of situational irony, based on the theory of conceptual coherence (Kunda & Thagard, 1996; Thagard & Verbeurgt, 1998). On this theory, a situation counts as ironic when it is conceived as (...) having a bicoherent conceptual structure, adequate cognitive salience, and evokes an appropriate configuration of emotions. The theory is applied to a corpus of 250 examples of situational ironies gathered automatically from electronic news sources. A useful taxonomy of situational ironies is produced, new predictions and insights into situational irony are discussed, and extensions of the theory to other forms of irony are examined. (shrink)

This paper proposes a theory of how conscious emotional experience is produced by the brain as the result of many interacting brain areas coordinated in working memory. These brain areas integrate perceptions of bodily states of an organism with cognitive appraisals of its current situation. Emotions are neural processes that represent the overall cognitive and somatic state of the organism. Conscious experience arises when neural representations achieve high activation as part of working memory. This theory explains numerous phenomena concerning emotional (...) consciousness, including differentiation, integration, intensity, valence, and change. Ó 2007 Elsevier Inc. All rights reserved. (shrink)

The prefrontal cortex has long been suspected to play an important role in cognitive control, in the ability to orchestrate thought and action in accordance with internal goals. Its neural basis, however, has remained a mystery. Here, we propose that cognitive control stems from the active maintenance of patterns of activity in the prefrontal cortex that represent goals and the means to achieve them. They provide bias signals to other brain structures whose net effect is to guide the flow of (...) activity along neural pathways that establish the proper mappings between inputs, internal states, and outputs needed to perform a given task. We review neurophysiological, neurobiological, neuroimaging, and computational studies that support this theory and discuss its implications as well as further issues to be addressed. (shrink)

The concept of mechanism is analyzed in terms of entities and activities, organized such that they are productive of regular changes. Examples show how mechanisms work in neurobiology and molecular biology. Thinking in terms of mechanisms provides a new framework for addressing many traditional philosophical issues: causality, laws, explanation, reduction, and scientific change.

Contrary to standard assumptions, reasoning is often an emotional process. Emotions can have good effects, as when a scientist gets excited about a line of research and pursues it successfully despite criticism. But emotions can also distort reasoning, as when a juror ignores evidence of guilt just because the accused seems like a nice guy. In _Hot Thought_, Paul Thagard describes the mental mechanisms -- cognitive, neural, molecular, and social -- that interact to produce different kinds of human thinking, from (...) everyday decision making to legal reasoning, scientific discovery, and religious belief, and he discusses when and how thinking and reasoning should be emotional. Thagard argues that an understanding of emotional thinking needs to integrate the cognitive, neural, molecular, and social levels. Many of the chapters employ computational models of various levels of thinking, including HOTCO models and the more neurologically realistic GAGE model. Thagard uses these models to illuminate thinking in the domains of law, science, and religion, discussing such topics as the role of doubt and reasonable doubt in legal and other contexts, valuable emotional habits for successful scientists, and the emotional content of religious beliefs. Identifying and assessing the impact of emotion, Thagard argues, can suggest ways to improve the process of reasoning. (shrink)

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

This book examines two questions: Do people make use of abstract rules such as logical and statistical rules when making inferences in everyday life? Can such abstract rules be changed by training? Contrary to the spirit of reductionist theories from behaviorism to connectionism, there is ample evidence that people do make use of abstract rules of inference -- including rules of logic, statistics, causal deduction, and cost-benefit analysis. Such rules, moreover, are easily alterable by instruction as it occurs in classrooms (...) and in brief laboratory training sessions. The fact that purely formal training can alter them and that those taught in one content domain can "escape" to a quite different domain for which they are also highly applicable shows that the rules are highly abstract. The major implication for cognitive science is that people are capable of operating with abstract rules even for concrete, mundane tasks; therefore, any realistic model of human inferential capacity must reflect this fact. The major implication for education is that people can be far more broadly influenced by training than is generally supposed. At high levels of formality and abstraction, relatively brief training can alter the nature of problem-solving for an infinite number of content domains. (shrink)

By applying research in artificial intelligence to problems in the philosophy of science, Paul Thagard develops an exciting new approach to the study of scientific reasoning. This approach uses computational ideas to shed light on how scientific theories are discovered, evaluated, and used in explanations. Thagard describes a detailed computational model of problem solving and discovery that provides a conceptually rich yet rigorous alternative to accounts of scientific knowledge based on formal logic, and he uses it to illuminate such topics (...) as the nature of concepts, hypothesis formation, analogy, and theory justification. (shrink)

Connectionism and the Mind provides a clear and balanced introduction to connectionist networks and explores theoretical and philosophical implications. Much of this discussion from the first edition has been updated, and three new chapters have been added on the relation of connectionism to recent work on dynamical systems theory, artificial life, and cognitive neuroscience. Read two of the sample chapters on line: Connectionism and the Dynamical Approach to Cognition: http://www.blackwellpublishing.com/pdf/bechtel.pdf Networks, Robots, and Artificial Life: http://www.blackwellpublishing.com/pdf/bechtel2.pdf.

Cognitive science is the project of understanding the mind by modelling its workings. Its development is one of the most remarkable and fascinating intellectual achievements of the modern era. Mind as Machine is a masterful history of cognitive science, told by one of its most eminent practitioners.

In this path-breaking work, Paul Thagard draws on history and philosophy of science, cognitive psychology, and the field of artificial intelligence to develop a ...

A description of mental mechanisms that explain how emotions influence thought, from everyday decision making to scientific discovery and religious belief, and an analysis of when emotion can contribute to good reasoning.

Keith Holyoak and Paul Thagard provide a unified, comprehensive account of the diverse operations and applications of analogy, including problem solving, ...

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.

In their introduction to this volume, Ram and Leake usefully distinguish between task goals and learning goals. Task goals are desired results or states in an external world, while learning goals are desired mental states that a learner seeks to acquire as part of the accomplishment of task goals. We agree with the fundamental claim that learning is an active and strategic process that takes place in the context of tasks and goals (see also Holland, Holyoak, Nisbett, and Thagard, 1986). (...) But there are important questions about the nature of goals that have rarely been addressed. First, how can a cognitive system deal with incompatible task goals? Someone may want both to get lots of research done and to relax and have fun with his or her friends. Learning how to accomplish both these tasks will take place in the context of goals that cannot be fully realized together. Second, how are goals chosen in the first place and why are some goals judged to be more important than others? People do not simply come equipped with goals and priorities: we sometimes have to learn what is important to us by adjusting the importance of goals in the context of other compatible and incompatible goals. This paper presents a theory and a computational model of how goals can be adopted or rejected in the context of decision making. In contrast to classical decision theory, it views decision making as a process not only of choosing actions but also of evaluating goals. Our theory can therefore be construed as concerned with the goal-directed learning of goals. (shrink)

The vast expressive power of language is made possible by two principles: the arbitrary soundmeaning pairing underlying words, and the discrete combinatorial system underlying grammar. These principles implicate distinct cognitive mechanisms: associative memory and symbolmanipulating rules. The distinction may be seen in the difference between regular inflection (e.g., walk-walked), which is productive and open-ended and hence implicates a rule, and irregular inflection (e.g., come-came, which is idiosyncratic and closed and hence implicates individually memorized words. Nonetheless, two very different theories have (...) attempted to collapse the distinction; generative phonology invokes minor rules to generate irregular as well as regular forms, and connectionism invokes a pattern associator memory to store and retrieve regular as well as irregular forms. I present evidence from three disciplines that supports the traditional word/rule distinction, though with an enriched conception of lexical memory with some of the properties of a pattern-associator. Rules, nonetheless, are distinct from patternassociation, because a rule concatenates a suffix to a symbol for verbs, so it does not require access to memorized verbs or their sound patterns, but applies as the "default," whenever memory access fails. I present a dozen such circumstances, including novel, unusual-sounding, and rootless and headless derived words, in which people inflect the words regularly (explaining quirks like flied out, low-lifes, and Walkmans). A comparison of English to other languages shows that contrary to the connectionist account, default suffixation is not due to numerous regular words reinforcing a pattern in associative memory, but to a memory-independent, symbol-concatenating mental operation. (shrink)