ents. A Bayesian model may be considered an optimal account of induction that, ideally, would make predictions that bear some resemblance to what people actually do in inductive reasoning tasks. Assumptions for Rational Analysis The Bayesian model presented here is meant to be a computational-level account (Marr, 1982), in that it is a description of the task that is performed in evaluating inductive arguments, rather than a detailed process-level account. In this way, the Bayesian account fulfils the first step of Anderson's (1990) scheme for rational analyses, specifying the goals of the system during a particular task. However, this account does not contain other elements of a rational analysis, such as a description of the environment. For inductive reasoning, the environment might be something as large as all properties of all objects, or all beliefs about properties of objects, and it is not clear how a description of the environment would be undertaken. The Bayesian model for in

The authors examined inductive reasoning among experts in a domain. Three types of tree experts (landscapers, taxonomists, and parks maintenance personnel) completed 3 reasoning tasks. In Experi-ment 1, participants inferred which of 2 novel diseases would affect "more other kinds of trees " and provided justifications for their choices. In Experiment 2, the authors used modified instructions and asked which disease would be more likely to affect "all trees. " In Experiment 3, the conclusion category was eliminated altogether, and participants were asked to generate a list of other affected trees. Among these populations, typicality and diversity effects were weak to nonexistent. Instead, experts ' reasoning was influenced by "local " coverage (extension of the property to members of the same folk family) and causal-ecological factors. The authors concluded that domain knowledge leads to the use of a variety of reasoning strategies not captured by current models of category-based induction. Cognitive psychologists are increasingly interested in concep-tual functions beyond categorization (e.g., Barsalou & Hale, 1992; Markman, Yamauchi, & Makin, 1997; Pazzani, 1991; Ross, 1996, 1997; Wisniewski, 1995). Particularly, they have focused on the use of categories in reasoning and have proposed a number of formal models of category-based reasoning (e.g., Heit, 1998; Mc-

Abstract not found

Everyday inductive inferences are often guided by rich background knowledge. Formal models of induction should aim to incorporate this knowledge, and should explain how different kinds of knowledge lead to the distinctive patterns of reasoning found in different inductive contexts. We present a Bayesian framework that attempts to meet both goals and describe four applications of the framework: a taxonomic model, a spatial model, a threshold model, and a causal model. Each model makes probabilistic inferences about the extensions of novel properties, but the priors for the four models are defined over different kinds of structures that capture different relationships between the categories in a domain. Our framework therefore shows how statistical inference can operate over structured background knowledge, and we argue that this interaction between structure and statistics is critical for explaining the power and flexibility of human reasoning.

A feature is central to a concept to the extent that other features depend on it. Four studies tested the hypothesis that people will project a feature from a base concept to a target concept to the extent that they believe the feature is central to the two concepts. This centrality hypothesis implies that feature projection is guided by a principle that aims to maximize the structural commonality between base and target concepts. Participants were told that a category has two or three novel features. One feature was the most central in that more properties depended on it. The extent to which the target shared the feature's dependencies was manipulated by varying the similarity of category pairs. Participants' ratings of the likelihood that each feature would hold in the target category support the centrality hypothesis with both natural kind and artifact categories and with both well-specified and vague dependency structures.

A central question in cognitive research concerns how new properties are generalized to categories. This article introduces a model of how generalizations involve a process of causal inference in which people estimate the likely presence of the new property in individual category exemplars and then the prevalence of the property among all category members. Evidence in favor of this causalbased generalization (CBG) view included effects of an existing feature’s base rate (Experiment 1), the direction of the causal relations (Experiments 2 and 4), the number of those relations (Experiment 3), and the distribution of features among category members (Experiments 4 and 5). The results provided no support for an alternative view that generalizations are promoted by the centrality of the to-be-generalized feature. However, there was evidence that a minority of participants based their judgments on simpler associative reasoning processes. Keywords: Causal-based induction; Generalization; Causal reasoning 1.

Abstract. Mechanisms that underlie the inductive reasoning process in risk contexts are investigated. Experimental results indicate that people rate the same inductive reasoning argument differently according to the direction of risk aversion. In seeking to provide the most valid explanation of this, two kinds of models based on a Support Vector Machine (SVM) that process different knowledge spaces are proposed and compared. These knowledge spaces—a feature-based space and a category-based space—are both constructed from the soft clustering of the same corpus data. The simulation for the category-based model resulted in a slightly more successful replication of experimental findings for two kinds of risk conditions using two different estimated model parameters than the other simulation. Finally, the cognitive explanation by the category-based model based on a SVM for contextual inductive reasoning is discussed.

Available online at www.sciencedirect.com

with negative evidence

Abstract not found