Category-based induction (original) (raw)

Category-based induction: An effect of conclusion typicality

2004

Abstract Category-based induction involves the willingness of a thinker to project some newly learned property of one or more classes of objects to another class on the basis of their shared membership in a common superordinate category.

The relevance framework for category-based induction: Evidence from garden-path arguments

2010

Relevance theory suggests that people expend cognitive effort when processing information in proportion to the cognitive effects to be gained from doing so. This theory has been used to explain how people apply their knowledge appropriately when evaluating category-based inductive arguments . In such arguments, people are told that a property is true of premise categories and are asked to evaluate the likelihood that it is also true of conclusion categories. According to the relevance framework, reasoners generate hypotheses about the relevant relation between the categories in the argument. We reasoned that premises inconsistent with early hypotheses about the relevant relation would have greater effects than consistent premises. We designed three premise garden-path arguments where the same 3rd premise was either consistent or inconsistent with likely hypotheses about the relevant relation. In Experiments 1 and 2, we showed that effort expended processing consistent premises (measured via reading times) was significantly less than effort expended on inconsistent premises. In Experiment 2 and 3, we demonstrated a direct relation between cognitive effect and cognitive effort. For garden-path arguments, belief change given inconsistent 3rd premises was significantly correlated with Premise 3 (Experiment 3) and conclusion (Experiments 2 and 3) reading times. For consistent arguments, the correlation between belief change and reading times did not approach significance. These results support the relevance framework for induction but are difficult to accommodate under other approaches.

Expertise and category-based induction

Journal of Experimental Psychology: Learning, Memory, and Cognition, 2000

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 Experiment 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.

Inductive judgments about natural categories

Journal of Verbal Learning and Verbal Behavior

The present study examined the effects of semantic structure on simple inductive judgments about category members. For a particular category (e.g., mammals), subjects were told that one of the species (e.g., horses) had a given property (an unknown disease) and were asked to estimate the proportion of instances in the other species that possessed the property. The results indicated that category structure-in particular, the typicality of the species-influenced subjects' judgments. These results were interpreted by models based on the following assumption: When little is known about the underlying distribution of a property, subjects assume that the distribution mirrors that of better-known properties. For this reason, if subjects learn that an unknown property is possessed by a typical species (i.e., one that shares many of its properties with other category members), they are more likely to generalize than if the same fact had been learned about an atypical species. Gaps in our knowledge of facts force us to rely on inductive methods in determining the truth or probability of certain statements. One, by now traditional, way of studying inductive strategies experimentally is through concept attainment tasks, which have been claimed to provide a direct analogue of inductive reasoning (Hunt, Marin, & Stone, 1966; Trabasso, Rollins, & Shaughnessy, 1971). The basis of the analogy is that in concept formation paradigms, as in inductive reasoning, tentative hypotheses are advanced on the basis of preliminary evidence. These hypotheses are strengthened by confirming evidence or are revised in the light of contradictory evidence. Of course, thus broadly construed, induction is mirrored not only in concept attainment, but also in many other paradigms, for example in problem-solving, decision-Thanks are due to G.

A relevance theory of induction

Psychonomic Bulletin & Review, 2003

A framework theory, organized around the principle of relevance, is proposed for category-based reasoning. According to the relevance principle, people assume that premises are informative with respect to conclusions. This idea leads to the prediction that people will use causal scenarios and property reinforcement strategies in inductive reasoning. These predictions are contrasted with both existing models and normative logic. Judgments of argument strength were gathered in three different countries, and the results showed the importance of both causal scenarios and property reinforcement in categorybased inferences. The relation between the relevance framework and existing models of category-based inductive reasoning is discussed in the light of these findings.

Induction with cross-classified categories

Memory & Cognition, 1999

One of the main functions of categories is to allow inferences about new objects. However, most objects are cross-classified, and it is not known whether and how people combine information from these different categories in making inferences. In six experiments, food categories, which are strongly crossclassified (e.g., a bagel is both a bread and a breakfast food), were studied. For each food, the subjects were told fictitious facts (e.g., 75% of breads are subject to spoilage from Aspergillus molds) about two of the categories to which it belonged and then were asked to make an inference about the food (e.g., how likely is a bagel to be subject to spoilage from Aspergillus molds?). We found no more use of multiple categories in these cases of cross-classification than in ambiguous classification, in which it is uncertain to which category an item belongs. However, some procedural manipulations did markedly increase the use of both categories in inferences, primarily those that focused the subjects' attention on the critical feature in both categories.

Category Induction for Ordinary Facts

2000

Category Induction Category Induction for Ordinary Facts Roman Taraban ( r.taraban@ttu.edu ) Matt Hayes Department of Psychology Texas Tech University Lubbock, TX 79409-2051 Abstract Typically, research on category learning has examined the ac- quisition of correct responses for explicitly identified catego- ries. A connectionist model developed by McClelland (1981) used an interconnected network of factual elements to show that it was possible for a network to correctly infer connec- tions between knowledge representations that were not ex- plicitly coded into the network. Two experiments were con- ducted with adults using facts from the McClelland model. Clustering related facts, presenting the full set of transfer probes, and providing intermittent feedback during learning, did not reliably amplify the induction of implicit categories that was necessary for the transfer of learning tasks. The data in both experiments revealed a wide range of individual dif- ferences suggestive of...