An ongoing goal in the field of categorization has been to determine how objects ’ features provide evidence of membership in one category versus another. Well-known findings include that feature diagnosticity is a function of how often the feature appears in category members versus nonmembers, their perceptual salience, how features are used in support of inferences, and how observable features are related to other observable features. We tested how diagnosticity is affected by causal relations between observable and unobserved features. Consistent with our view of classification as diagnostic reasoning, we found that observable features are more diagnostic to the extent that they are caused by underlying features that define category membership, because the presence of the latter can be (causally) inferred from the former. Implications of these results for current views of conceptual structure and models of categorization are discussed. It is generally accepted that people’s concepts include not only the features and attributes of the entity being represented, but also the ways in which those features are related to one another. For example, we know that hormones can alter a person’s behavior, that chemical structure can affect a substance’s hardness, and that processor

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.

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Two experiments examined the impact of causal relations between features on categorization by adults and 5-6-year-old children. Participants learned about artificial categories containing instances with two causally related features and two non-causal features. They then selected the most likely category member from a series of novel test pairs. Classification patterns and logistic regression were used to diagnose the presence of independent effects of causal coherence, causal status and relational centrality. Adult classification was driven primarily by coherence when causal links were deterministic (Experiment 1), but showed additional influences of causal status and centrality when links were probabilistic (Experiment 2). Children’s classification was based primarily on causal coherence in both cases. These results suggest that the generative model [Rehder, B. (2003). A causalmodel theory of conceptual representation and categorization. Journal of Experimental Psychology: Learning, Memory, and Cognition, 29, 1141-1159] provides a good account of causal categorization in both children and adults. Children’s Causal Categorization 3 It is well established that causal knowledge plays an important role in adult categorization and

Current research suggests a facilitatory role for basic biomedical knowledge in learning and retaining concepts related to medical diagnosis. But learning and performance may be influenced by other knowledge as well. Accordingly, we examined the effects of foundational knowledge beyond basic biomedical science on the learning and retention of medical information. Subjects were asked to study a handout detailing a percussive chest exam and several respiratory disorders. One group was presented with the information in a standard “textbook ” format and the other group was presented with foundational knowledge about how sound travels though solids and liquids. The foundational knowledge group outperformed the control group in a memory task. We suggest that these subjects were able to create causal links between the information to be learned and the foundational knowledge which made the critical information more memorable.

Design history function (i.e., what an artifact was made for) is a central aspect of artifact conceptualization. A generally accepted explanation is that design history is central because it is the root cause for many other artifact properties. In Exp. 1, an inference task allowed us to probe participants ‘ causal models, and then to use them when making predictions for Exp. 2. Design history was, in fact, part of what participants viewed as conceptually relevant. Predictions for Exp. 2 were derived using the currently most comprehensive theory about how causal knowledge affects categorization. Our results show that though participants used design history, functional outcome and physical structure to conceptualize artifacts, the effect of design history was independent from knowledge of physical structure and functional outcome. This result is inconsistent with a causal knowledge explanation of design history‘s conceptual centrality.

Conjunctive causes are causes that all need to be present for an effect to occur. They contrast with independent causes that by themselves can each bring about an effect. We extend existing “causal power ” representations of independent causes to include a representation of conjunctive causes. We then demonstrate how independent vs. conjunctive representations imply sharply different patterns of reasoning (e.g., explaining away effects for independent causes as compared to exoneration effects for conjunctive causes). An experiment testing how people reason with independent and conjunctive causes found that their inferences generally matched the model’s prediction, albeit with some important exceptions. Rather than operating in a vacuum, causes frequently interact with other factors to produce their effects. For example, the conjunction of two or more variables is often necessary for an outcome to occur. A spark may only produce fire if there is fuel to ignite, a virus may only cause disease if one’s immune system is suppressed, the motive to commit murder may result in death only if the means to carry out the crime are available. Sometimes, conjunctive causes take the form of enablers. For example, the presence of oxygen enables fire given spark and fuel. In contrast, disablers interact with existing causes by preventing normal outcomes. Although the eight ball’s path to the side pocket may appear inevitable, it may be interrupted by an earthquake, a falling ceiling tile, or a spilled beer. The last 20 years has seen a growing interest in the role of causal knowledge in numerous areas of cognition. Many studies have investigated how causal relations are learned from observed correlations (Cheng, 1997; Gopnik et al.,

People are amateur social psychologists: they explain other people’s behavior, infer what other people are thinking and feeling, and predict how other people will act. I will refer to this sort of psychologizing as social reasoning in order to highlight the fact that it involves reasoning about people. Social reasoning often requires significant leaps of inductive inference: people infer others ’ mental states, such as their preferences, goals, and beliefs, from relatively sparse information, such as others ’ choices and actions. The capacity to reason about mental states and about how mental states relate to behavior is often referred

studies of concept learning in adults address the learning of novel concepts, but much of learning involves the updating and restructuring of familiar concepts. Research on conceptual change explores this issue directly but differs greatly from the formal approach of the adult learning studies. This paper bridges these two areas to advance our knowledge of the mechanisms underlying concept restructuring. The main idea behind this approach is that concepts are built on causal-explanatory knowledge, and hence, models of causal induction may help to clarify the mechanisms of the restructuring process. A new paradigm is presented to study the learning and revising of causal networks. Experiments 1 and 2 showed that learners’ prior beliefs about the causal relations in a domain affected their hypotheses as they began to infer the correct causes. First, when the prior learning suggested evidence against some of the incorrect causes, this helped learners to focus on the correct causes later in learning. Second, the prior causal beliefs were difficult to give up, and they biased learners away from the correct causes that competed to explain the same effects. Experiment 3 showed that learning by intervention, as opposed to observation, affected the concept restructuring process in different ways, depending on what interventions were chosen and by whom. People choosing their own