Generating Inductive Inferences

ArticleinPsychology of Learning and Motivation 53:183-226 · December 2010with 50 Reads
Abstract
Categorical inductive inference is the process by which we project features believed to be true of one class to another related class. Traditional approaches to studying inductive inference have focused on the evaluation of inductive arguments. In this chapter, we introduce a new approach by examining the way people generate inductive inferences. We focus on how relations among premise categories, and the nature of the property being projected, impact the kind of inferences generated. Participants were taught that two animal species shared a novel substance, disease, or gene, and were asked what other species might also have the property, and why. Results show that people attend to salient relations between premise categories, determine their relevance based on the property they are asked to project, and then generate inferences consistent with those relations. Participants drew a broad range of inferences based on taxonomic similarity, contextual similarity, and causal relations. Inference generation was constrained both by salient premise relations and the nature of the projected property. We discuss how these findings expand the list of challenges for the models of induction, question the primacy of taxonomic relations in guiding inductive inference, encourage further investigation into the process by which inductive inferences are generated, and emphasize the knowledge-driven and flexible nature of human inductive reasoning.
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    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 category-based inferences. The relation between the relevance framework and existing models of category-based inductive reasoning is discussed in the light of these findings.
  • Article
    Previous research (e.g. Cognition 64 (1997) 73) suggests that the privileged level for inductive inference in a folk biological conceptual hierarchy does not correspond to the "basic" level (i.e. the level at which concepts are both informative and distinct). To further explore inductive inference within conceptual hierarchies, we examine relations between knowledge of concepts at different hierarchical levels, expectations about conceptual coherence, and inductive inference. In Experiments 1 and 2, 5- and 8-year-olds and adults listed features of living kind (Experiments 1 and 2) and artifact (Experiment 2) concepts at different hierarchical levels (e.g. plant, tree, oak, desert oak), and also rated the strength of generalizations to the same concepts. For living kinds, the level that showed a relative advantage on these two tasks differed; the greatest increase in features listed tended to occur at the life-form level (e.g. tree), whereas the greatest increase in inductive strength tended to occur at the folk-generic level (e.g. oak). Knowledge and induction also showed different developmental trajectories. For artifact concepts, the levels at which the greatest gains in knowledge and induction occurred were more varied, and corresponded more closely across tasks. In Experiment 3, adults reported beliefs about within-category similarity for concepts at different levels of animal, plant and artifact hierarchies, and rated inductive strength as before. For living kind concepts, expectations about category coherence predicted patterns of inductions; knowledge did not. For artifact concepts, both knowledge and expectations predicted patterns of induction. Results suggest that beliefs about conceptual coherence play an important role in guiding inductive inference, that this role may be largely independent of specific knowledge of concepts, and that such beliefs are especially important in reasoning about living kinds.
  • Article
    This research explored children's use of multiple forms of conceptual organization. Experiments 1 and 2 examined script (e.g., breakfast foods), taxonomic (e.g., fruits), and evaluative (e.g., junk foods) categories. The results showed that 4- and 7-year-olds categorized foods into all 3 categories, and 3-year-olds used both taxonomic and script categories. Experiment 3 found that 4- and 7-year-olds can cross-classify items, that is, classify a single food into both taxonomic and script categories. Experiments 4 and 5 showed that 7-year-olds and to some degree 4-year-olds can selectively use categories to make inductive inferences about foods. The results reveal that children do not rely solely on one form of categorization but are flexible in the types of categories they form and use.
  • Article
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    The authors present a similarity-based model of induction and categorization in young children (SINC). The model suggests that (a). linguistic labels contribute to the perceived similarity of compared entities and (b). categorization and induction are a function of similarity computed over perceptual information and linguistic labels. The model also predicts young children's similarity judgment, induction, and categorization performance under different stimuli and task conditions. Predictions of the model were tested and confirmed in 6 experiments, in which 4- to 5-year-olds performed similarity judgment, induction, and categorization tasks using artificial and real labels (Experiments 1-4) and recognition memory tasks (Experiments 5A and 5B). Results corroborate the similarity-based account of young children's induction and categorization, and they support both qualitative and quantitative predictions of the model.
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    According to the diversity principle, diverse evidence is strong evidence. There has been considerable evidence that people respect this principle in inductive reasoning. However, exceptions may be particularly informative. Medin, Coley, Storms, and Hayes (2003) introduced a relevance theory of inductive reasoning and used this theory to predict exceptions, including the nondiversity-by-property-reinforcement effect. A new experiment in which this phenomenon was investigated is reported here. Subjects made inductive strength judgments and similarity judgments for stimuli from Medin et al. (2003). The inductive strength judgments showed the same pattern as that in Medin et al. (2003); however, the similarity judgments suggested that the pattern should be interpreted as a diversity effect, rather than as a nondiversity effect. It is concluded that the evidence regarding the predicted nondiversity-by-property-reinforcement effect does not give distinctive support for relevance theory, although this theory does address other results.
  • Article
    We present a framework for the rational analysis of elemental causal induction-learning about the existence of a relationship between a single cause and effect-based upon causal graphical models. This framework makes precise the distinction between causal structure and causal strength: the difference between asking whether a causal relationship exists and asking how strong that causal relationship might be. We show that two leading rational models of elemental causal induction, DeltaP and causal power, both estimate causal strength, and we introduce a new rational model, causal support, that assesses causal structure. Causal support predicts several key phenomena of causal induction that cannot be accounted for by other rational models, which we explore through a series of experiments. These phenomena include the complex interaction between DeltaP and the base-rate probability of the effect in the absence of the cause, sample size effects, inferences from incomplete contingency tables, and causal learning from rates. Causal support also provides a better account of a number of existing datasets than either DeltaP or causal power.
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    Inductive inference allows humans to make powerful generalizations from sparse data when learning about word meanings, unobserved properties, causal relationships, and many other aspects of the world. Traditional accounts of induction emphasize either the power of statistical learning, or the importance of strong constraints from structured domain knowledge, intuitive theories or schemas. We argue that both components are necessary to explain the nature, use and acquisition of human knowledge, and we introduce a theory-based Bayesian framework for modeling inductive learning and reasoning as statistical inferences over structured knowledge representations.
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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 can help to explain how the rest are 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.
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    Past research suggests that category-based induction flexibly draws on different kinds of knowledge in different contexts, and that different kinds of knowledge may differ in accessibility. The present study investigates the degree to which knowledge accessibility mediates context-sensitive induction by examining the effects of time pressure on inferences about novel properties of animal species. Participants were told about a novel gene or a novel disease that was true of one category of animals, then rated the likelihood that taxonomically, ecologically, and unrelated animals had the same property, under speeded or delayed conditions. Property effects were observed for taxonomically related species independent of time pressure, but were only observed for ecologically related species in the delayed condition. These results suggest that time pressure selectively restricts access to ecological knowledge, and that knowledge access is critical for context-sensitive inductive reasoning.
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    Recent evidence suggests that the conjunction fallacy observed in people's probabilistic reasoning is also to be found in their evaluations of inductive argument strength. We presented 130 participants with materials likely to produce a conjunction fallacy either by virtue of a shared categorical or a causal relationship between the categories in the argument. We also took a measure of participants' cognitive ability. We observed conjunction fallacies overall with both sets of materials but found an association with ability for the categorical materials only. Our results have implications for accounts of individual differences in reasoning, for the relevance theory of induction, and for the recent claim that causal knowledge is important in inductive reasoning.
  • Article
    A connectionist model of argument strength is proposed that applies to categorical arguments involving natural categories and predicates about which subjects have few prior beliefs. An example is robins have sesamoid bones, therefore falcons have sesamoid bones. The model is based on the hypotheses that argument strength (i) increases with the overlap between features of the combined premise categories and features of the conclusion category; and (ii) decreases with the amount of prior knowledge about the conclusion category. The model assumes a two-stage process. First, premises are encoded by connecting the features of premise categories to the predicate. Second, conclusions are tested by examining the degree of activation of the predicate upon presentation of the features of the conclusion category. The model accounts for 13 qualitative phenomena and shows close quantitative fits to several sets of argument-strength ratings. Feature-based induction 3 3 One way we learn about ...
  • Experience and the development of flexible inductive reasoning in biology
    • A Z Vitkin
    • N Y Vasilyeva
    • J D Coley
    Vitkin, A. Z., Vasilyeva, N. Y., & Coley, J. D. (2007). Experience and the development of flexible inductive reasoning in biology. Paper presented at the annual meeting of British Psychological Society 2007 Developmental Section Conference. Plymouth, UK.
  • The role of knowledge in folk biological induction
    • Author
    • T M Muratore
    • J D Coley
    Author's personal copy Muratore, T. M., & Coley, J. D. (2009). The role of knowledge in folk biological induction.
  • Accessibility of taxonomic and script knowl-edge in the domain of food
    • A Vitkin
    • J D Coley
    • K Feigin
    Vitkin, A., Coley, J. D., & Feigin, K. (2005). Accessibility of taxonomic and script knowl-edge in the domain of food. Paper presented at the 46th annual meeting of the Psychonomic Society, Toronto.
  • Experience increases flexible ecological reasoning
    • J D Coley
    • A Z Vitkin
    • N Y Vasilyeva
    • K Amato
    Coley, J. D., Vitkin, A. Z., Vasilyeva, N. Y., & Amato, K. (2007). Experience increases flexible ecological reasoning. In: Paper presented at the 15th Biennial Conference of the Australasian Human Development Association. Sydney, New South Wales, Australia.