Abstract not found

Early studies of intuitive judgment and decision making conducted with the late Amos Tversky are reviewed in the context of two related concepts: an analysis of accessibility, the ease with which thoughts come to mind; a distinction between effortless intuition and deliberate reasoning. Intuitive thoughts, like percepts, are highly accessible. Determinants and consequences of accessibility help explain the central results of prospect theory, framing effects, the heuristic process of attribute substitution, and the characteristic biases that result from the substitution of nonextensional for extensional attributes. Variations in the accessibility of rules explain the occasional corrections of intuitive judgments. The study of biases is compatible with a view of intuitive thinking and decision making as generally skilled and successful.

Information leakage traditionally has been defined to occur when uncertainty about secret data is reduced. This uncertainty-based approach is inadequate for measuring information flow when an attacker is making assumptions about secret inputs and these assumptions might be incorrect; such attacker beliefs are an unavoidable aspect of any satisfactory definition of leakage. To reason about information flow based on beliefs, a model is developed that describes how attacker beliefs change due to the attacker’s observation of the execution of a probabilistic (or deterministic) program. The model leads to a new metric for quantitative information flow that measures accuracy rather than uncertainty of beliefs. 1.

structure. Perceptual classification learning experiments were conducted in which presentation frequencies of individual exemplars were manipulated. The exemplars had varying degrees of similarity to members of the target and contrast categories. Classification accuracy and typicality ratings increased for exemplars presented with high frequency and for members of the target category that were similar to the high-frequency exemplars. Typicality decreased for members of the contrast category that were similar to the high-frequency exemplars. A frequency-sensitive similarity-to-exemplars model provided a good quantitative account of the classification learning and typicality data. The interactive relations among similarity, frequency, and categorization are considered in the General Discussion. Among the most well-established findings in the categorization literature is that categories have "graded structures"

Rationality is a complex behavioral theory that can be parsed into statements about preferences, perceptions, and process. This paper looks at the evidence on rationality that is provided by behavioral experiments, and argues that most cognitive anomalies operate through errors in perception that arise from the way information is stored, retrieved, and processed, or through errors in process that lead to formulation of choice problems as cognitive tasks that are inconsistent at least with rationality narrowly defined. The paper discusses how these cognitive anomalies influence economic behavior and measurement, and their implications for economic analysis.

Hyperproof is a computer program created by Barwise & Etchemendy for teaching logic using multimodal graphical and sentential representations. Elsewhere, we have proposed a theory of the cognitive impact of assigning information to different modalities. The theory predicts that the Hyperproof's devices for graphical abstraction will play a pivotal role in determining learning outcomes. Here, the claims are tested by a controlled comparison of the effects of teaching undergraduate classes using Hyperproof and a traditional syntactic teaching method. Results indicate that there is significant transfer from the logic courses to a range of verbal reasoning problems. There are also significant interactions between theoretically motivated pre-course aptitude measures and teaching method, and these interactions influence post-course reasoning performance in transfer domains. As well as being theoretically significant, the results provide support for the important practical conclusion that ind...

this article we outline the foundations of such a theory, working in the general framework of Bayesian inference. Much of our proposal for extending Shepard's theory to the cases of multiple examples and arbitrary stimulus structures has already been introduced in other papers (Griffiths & Tenenbaum, 2000; Tenenbaum, 1997, 1999a, 1999b; Tenenbaum & Xu, 2000). Our goal here is to make explicit the link to Shepard's work and to use our framework to make connections between his work and other models of learning (Feldman, 1997; Gluck & Shanks, 1994; Haussler, Kearns & Schapire, 1994; Kruschke, 1992; Mitchell, 1997), generalization (Nosofsky, 1986; Heit, 1998), and similarity (Chater & Hahn, 1997; Medin, Goldstone & Gentner, 1993; Tversky, 1977). In particular, we will have a lot to say about how our generalization of Shepard's theory relates to Tversky's (1977) well-known set-theoretic models of similarity. Tversky's set-theoretic approach and Shepard's metric space approach are often considered the two classic -- and classically opposed -- theories of similarity and generalization. By demonstrating close parallels between Tversky's approach and our Bayesian generalization of Shepard's approach, we hope to go some way towards unifying these two theoretical approaches and advancing the explanatory power of each. The plan of our article is as follows. In Section 2, we recast Shepard's analysis of generalization in a more general Bayesian framework, preserving the basic principles of his approach in a form that allows us to apply the theory to situations with multiple examples and arbitrary (non-spatially represented) stimulus structures. Sections 3 and 4 describe those extensions, and Section 5 concludes by discussing some implications of our theory for the internalization of...

This review proposes that implicit learning processes are the cognitive substrate of social intuition. This hypothesis is supported by (a) the conceptual correspondence between implicit learning and social intuition (nonverbal communication) and (b) a review of relevant neuropsychological (Huntington's and Parkinson's disease), neuroimaging, neurophysiological, and neuroanatomical data. It is concluded that the caudate and putamen, in the basal ganglia, are central components of both intuition and implicit learning, supporting the proposed relationship. Parallel, but distinct, processes of judgment and action are demonstrated at each of the social, cognitive, and neural levels of analysis. Additionally, explicit attempts to learn a sequence can interfere with implicit learning. The possible relevance of the computations of the basal ganglia to emotional appraisal, automatic evaluation, script processing, and decision making are discussed. These "feelings " have an efficiency of operation which it is impossi-ble for thought to match. Even our most highly intellectualized operations depend upon them as a "fringe " by which to guide our inferential movements. They give us our sense of rightness and wrongness, of what to select and emphasize and follow up, and what

Five studies explored cognitive, affective, and behavioral responses to proscribed forms of social cognition. Experiments 1 and 2 revealed that people responded to taboo trade-offs that monetized sacred values with moral outrage and cleansing. Experiments 3 and 4 revealed that racial egalitarians were least likely to use, and angriest at those who did use, race-tainted base rates and that egalitarians who inadvertently used such base rates tried to reaffirm their fair-mindedness. Experiment 5 revealed that Christian fundamentalists were most likely to reject heretical counterfactuals that applied everyday causal schemata to Biblical narratives and to engage in moral cleansing after merely contemplating such possibilities. Although the results fit the sacred-value-protection model (SVPM) better than rival formulations, the SVPM must draw on cross-cultural taxonomies of relational schemata to specify normative boundaries on thought. Research on social cognition ultimately rests on functionalist assumptions about what people are trying to accomplish when they judge events or make choices. The most influential of these assumptions have been the intuitive scientist and the intuitive economist. The former tradition depicts people whose central objective

Human concept learning presents a version of the classic problem of induction, which is made particularly difficult by the combination of two requirements: the need to learn from a rich (i.e. nested and overlapping) vocabulary of possible concepts and the need to be able to generalize concepts reasonably from only a few positive examples. I begin this thesis by considering a simple number concept game as a concrete illustration of this ability. On this task, human learners can with reasonable confidence lock in on one out of a billion billion billion logically possible concepts, after seeing only four positive examples of the concept, and can generalize informatively after seeing just a single example. Neither of the two classic approaches to inductive inference -- hypothesis testing in a constrained space of possible rules and computing similarity to the observed examples -- can provide a complete picture of how people generalize concepts in even this simple setting. This thesis prop...