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

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Inductive learning is impossible without overhypotheses, or constraints on the hypotheses considered by the learner. Some of these overhypotheses must be innate, but we suggest that hierarchical Bayesian models help explain how the rest can be acquired. To illustrate this claim, we develop models that acquire two kinds of overhypotheses — overhypotheses about feature variability (e.g. the shape bias in word learning) and overhypotheses about the grouping of categories into ontological kinds like objects and substances.

ositive side, though, the second person might have some advantage over the first person in learning how to shift gears, because the second person would not have to overcome negative transfer from experience with automatic transmissions. As another example, imagine that you are an explorer visiting a remote island, with the purpose of writing a book about the people that you see there. You bring to this island many forms of prior knowledge that will guide you in learning about these new people. For example, based on your experiences in other places, you would expect to see males and females, younger and older people, shy people and arrogant people. You would also have certain hypotheses at a more abstract level, for example, that the clothes that someone wears may be related to the person's age and gender. (Goodman, 1955, referred to such abstract hypotheses as overhypotheses.) In a way, these biases due to previous knowledge might seem to be undesirable. After all, wouldn't be it be be

this paper is to show that the category inclusion principle has only limited descriptive validity

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...

this article is whether children can incorporate this information into inductive reasoning

A classic feature-set model of similarity, the contrast model of Tversky (1977), is applied to a range of phenomena in category-based induction. These phenomena include basic similarity effects, typicality and asymmetry effects, diversity effects, effects of projectibility of properties, and contextual influences on similarity. These analyses help to extend the contrast model to a new area of research as well as to place constraints on the model itself. Although categorization research has largely focused on people's ability to infer taxonomic category labels, categories facilitate a number of cognitive abilities and functions. One of our most important abilities is inductive inference (Anderson, 1991; Billman & Heit, 1988; Heit, 1992; Osherson, Smith, Wilkie, Lopez, & Shafir, 1990), and category-level information enables a rich set of inferences. For example, you might not be able to infer much about Peter until you are told that Peter is a goldfish, in which case you could...

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.

ovides us with the fundamental representations that we subsequently combine and tweak. In assessing the contribution of developmental research on concepts and categories to our general understanding of human concepts, we will ask four questions. What are concepts? What is the relation between perception and concepts? What are the constraints on concept learning? What are promising future directions for research on concepts? What Are Concepts? A good starting place is Edward Smith's (1989) characterization of a concept as "a mental representation of a class or individual and deals with what is being represented and how that information is typically used during the categorization" (p. 502). It is common to distinguish between a concept and a category (e.g., Hampton & Dubois, 1993). A concept refers to a mentally possessed idea or notion, whereas a category refers to a set of entities that are grouped together. The concept dog is whatever psychological state signifies thoughts of dogs.