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Induction can be supported by many kinds of knowledge. To be effective, reasoning should be context sensitive; different kinds of knowledge should be selectively deployed in different situations. For example, in the domain of biology, when reasoning about the distribution of novel internal properties over species, taxonomic knowledge should be recruited since we know that taxonomic membership is not only related to perceptual similarity but is also a good predictor of shared unobservable anatomical features such as four-chambered hearts. However, when reasoning about the distribution of environmental toxins, ecological knowledge should be recruited since such a toxin would plausibly spread through an ecosystem. In this chapter, we address the factors that influence the recruitment of different kinds of knowledge in different contexts. We propose that different kinds of knowledge are differentially available across contexts. Using this concept of availability, we will address an array of experimental results, arguing for availability as a way to unite and explain a broad range of phenomena in category-based induction.
In a classic paper, Tversky and Kahneman (1973) discuss availability as a heuristic “by which people evaluate the frequency of classes or the likelihood of events” (p. 207). This involves estimating frequency or probability “by the ease with which instances or associations are brought to mind” (p. 208). As such, availability on this view is essentially a metacognitive heuristic by which information is judged more likely or plausible based on an estimate of the effort involved in retrieving the information; indeed, Tversky and Kahneman argue that “to assess availability it is not necessary to perform the actual operations of retrieval or construction. It suffices to assess the ease with which these operations could be performed” (p. 208).
Philosophers since Hume have struggled with the logical problem of induction, but children solve an even more difficult task – the practical problem of induction. Children somehow manage to learn concepts, categories, and word meanings, and all on the basis of a set of examples that seems hopelessly inadequate. The practical problem of induction does not disappear with adolescence: adults face it every day whenever they make any attempt to predict an uncertain outcome. Inductive inference is a fundamental part of everyday life, and for cognitive scientists, a fundamental phenomenon of human learning and reasoning in need of computational explanation.
There are at least two important kinds of questions that we can ask about human inductive capacities. First, what is the knowledge on which a given instance of induction is based? Second, how does that knowledge support generalization beyond the specific data observed: how do we judge the strength of an inductive argument from a given set of premises to new cases, or infer which new entities fall under a concept given a set of examples? We provide a computational approach to answering these questions. Experimental psychologists have studied both the process of induction and the nature of prior knowledge representations in depth, but previous computational models of induction have tended to emphasize process to the exclusion of knowledge representation. The approach we describe here attempts to redress this imbalance by showing how domain-specific prior knowledge can be formalized as a crucial ingredient in a domain-general framework for rational statistical inference.
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