A relevance theory of induction

Medin, Douglas L.; Coley, John D.; Storms, Gerrit; Hayes, Brett K.

doi:10.3758/bf03196515

Cited by 195 publications

(300 citation statements)

References 29 publications

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“…These limitations include the models' inability to incorporate the influence of different predicates (Heit & Rubinstein, 1994), background knowledge of an examiner (Proffitt et al, 2000), and complex reasoning skills that an examiner applies (Heit, 2000;Medin et al, 2003). The present study extends these findings by documenting that categorical information by itself could modify participants' inductive judgments beyond general similarity information.…”

Section: Implications and Extensionsmentioning

confidence: 52%

“…In a typical task, participants are given premises, such as All grizzly bears have Disease X and All polar bears have Disease X, and participants judge the strength of a conclusion such as All bears have Disease X (Heit, 2000;Heit & Rubinstein, 1994;Medin, Coley, Storms, & Hayes, 2003;Osherson et al, 1990;Osherson et al, 1991;Rips, 1975;Sloman, 1993).…”

Section: Definition and Two Types Of Categorical Induction Tasksmentioning

confidence: 99%

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Labeling Bias and Categorical Induction: Generative Aspects of Category Information.

Yamauchi¹

2005

Journal of Experimental Psychology: Learning, Memory, and Cogni

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When a person is characterized categorically with a label (e.g., Linda is a feminist), people tend to think that the attributes associated with that person are central and long lasting (S. Gelman & G. D. Heyman, 1999). This bias, which is related to category-based induction and stereotyping, has been thought to arise because a category label (e.g., feminist) activates the dominant properties associated with the representation of the category. This explanation implies that categorical information influences inferential processes mainly by conjuring up main attributes or instances represented in the category. However, the present experiments reveal that this attribute-based explanation of induction does not provide a complete picture of inferential processes. The results from 3 experiments suggest that category information can affect inferences of attributes that are not directly related to the category, suggesting that categories not only activate likely attributes but also help integrate unlikely or even unrelated attributes.One of the core purposes of categories is to integrate information and apply it to future predictions (Anderson, 1990;Rosch, 1978;E. E. Smith, 1994). For example, one forms the category "liberal" by classifying people who have similar political opinions and then uses the category to make an inference, such as predicting the policy that a member of the category advocates. In this regard, categories help bind information and serve as a medium for inferences.How do categories integrate diverse information and license inductive inferences? Main psychological accounts of inductive judgments suggest that two factors-(a) matching attributes between entities and (b) the coverage of premises over a conclusion (hereafter I call these two factors attribute-based similarity)-are the guiding forces of inferences (see Heit, 2000, for a review; Kunda & Thagard, 1996;Osherson, Smith, Wilkie, Lopez, & Shafir, 1990;Osherson, Stern, Wilke, Stob, & Smith, 1991;Rips, 1975;Sloman, 1993;E. R. Smith & Zarate, 1992;Tversky, 1977). According to this view, for example, the conclusion Lions have Disease X, given a premise, Zebras have Disease X, is credible proportional to the extent to which the two entities-zebras and lions-have features in common and the magnitude of an entity in a premise (i.e., zebras) exceeds that of a conclusion (lions; i.e., coverage).This similarity-based account of categorical induction has inspired many areas of cognitive psychology and social cognition and has yielded a rich array of empirical findings that illuminate the relationship between feature-based similarity and category membership

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Section: Implications and Extensionsmentioning

confidence: 52%

Section: Definition and Two Types Of Categorical Induction Tasksmentioning

confidence: 99%

Labeling Bias and Categorical Induction: Generative Aspects of Category Information.

Yamauchi¹

2005

Journal of Experimental Psychology: Learning, Memory, and Cogni

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“…Gelman & Markman, 1986;Heit & Rubinstein, 1994;Shafto & Coley, 2003;Medin et al, 2005 Like all of our models, the causal model relies on a structure and a stochastic process. The structure captures knowledge about predator-prey relationships among a group of species.…”

Section: Causal Reasoningmentioning

confidence: 99%

Structured statistical models of inductive reasoning.

Kemp¹,

Tenenbaum²

2009

Psychological Review

226

282

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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. 2 Structured statistical models of inductive reasoningHumans are adept at making inferences that take them beyond the limits of their direct experience. Even young children can learn the meaning of a novel word from a single labeled example (Heibeck & Markman, 1987), predict the trajectory of a moving object when it passes behind an occluder (Spelke, 1990), and choose a gait that allows them to walk over terrain they have never before encountered. Inferences like these may differ in many respects, but common to them all is the need to "go beyond the information given" (Bruner, 1973).Two different ways of going beyond the available information can be distinguished.Deductive inferences draw out conclusions that may have been previously unstated but were implicit in the data provided. Inductive inferences go beyond the available data in a more fundamental way, and arrive at conclusions that are likely but not certain given the available evidence. Both kinds of inferences are of psychological interest, but inductive inferences appear to play a more central role in everyday cognition. We have already seen examples related to language, vision, and motor control, and many other inductive problems have been described in the literature (Holland, Holyoak, Nisbett, & Thagard, 1986;Anderson, 1990). This paper describes a formal approach to inductive inference that should apply to many different problems, but we will focus on the problem of property induction (Sloman & Lagnado, 2005). In particular, we consider cases where one or more more categories in a domain are observed to have a novel property, and the inductive task is to predict how the property is distributed over the remaining categories in the domain. For instance, given that bears have sesamoid bones, which species is more likely to share this property: moose or salmon (Rips, 1975; Osherson, Smith, Wilkie, Lopez, & Shafir, 1990)? Moose may seem like the better choice since they are more similar biologically to bears, but different properties can lead to different patterns of inference. For example, given that a c...

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“…"has a liver with two chambers that act as one") people draw upon taxonomic knowledge about animals to guide inferences, but when reasoning about behavioral properties (e.g. "usually travels in a back-and-forth, or zig-zag, trajectory") people draw upon ecological knowledge (see also Medin, Coley, Storms, & Hayes, 2003;Shafto & Coley, 2003). Barsalou (1983) demonstrated that people derive cross-cutting categories, such as 'things to take out of the house in case of a fire', in the service of goals, and that these goal-based categories are used in planning (Barsalou, 1991).…”

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confidence: 99%

A probabilistic model of cross-categorization

et al. 2011

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Most natural domains can be represented in multiple ways: we can categorize foods in terms of their nutritional content or social role, animals in terms of their taxonomic groupings or their ecological niches, and musical instruments in terms of their taxonomic categories or social uses. Previous approaches to modeling human categorization have largely ignored the problem of cross-categorization, focusing on learning just a single system of categories that explains all of the features. Cross-categorization presents a difficult problem: how can we infer categories without first knowing which features the categories are meant to explain? We present a novel model that suggests that human cross-categorization is a result of joint inference about multiple systems of categories and the features that they explain. We also formalize two commonly proposed alternative explanations for cross-categorization behavior: a features-first and an objects-first approach. The features-first approach suggests that cross-categorization is a consequence of attentional processes, where features are selected by an attentional mechanism first and categories are derived second. The objects-first approach suggests that crosscategorization is a consequence of repeated, sequential attempts to explain features, where categories are derived first, then features that are poorly explained are recategorized. We present two sets of simulations and experiments testing the models' predictions about human categorization. We find that an approach based on joint inference provides the best fit to human categorization behavior, and we suggest that a full account of human category learning will need to incorporate something akin to these capabilities.People explain different aspects of everyday objects in different ways. For example, steak is high in iron because it is a meat; however, it is often served with wine because it is a dinner food. The different ways of thinking about steak underscore different ways of thinking about the domain of foods: as a system of taxonomic categories including meats and vegetables, or as a system of

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A relevance theory of induction

Cited by 195 publications

References 29 publications

Labeling Bias and Categorical Induction: Generative Aspects of Category Information.

Labeling Bias and Categorical Induction: Generative Aspects of Category Information.

Structured statistical models of inductive reasoning.

A probabilistic model of cross-categorization

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