Woo-kyoung Ahn

Causal status as a determinant of feature centrality

One of the major problems in categorization research is the lack of systematic ways of constraining feature weights. We propose one method of operationalizing feature centrality, a causal status hypothesis which states that a cause feature is judged to be more central than its effect feature in categorization. In Experiment 1, participants learned a novel category with three characteristic features that were causally related into a single causal chain and judged the likelihood that new objects belong to the category. Likelihood ratings for items missing the most fundamental cause were lower than those for items missing the intermediate cause, which in turn were lower than those for items missing the terminal effect. The causal status effect was also obtained in goodness-of-exemplar judgments (Experiment 2) and in free-sorting tasks (Experiment 3), but it was weaker in similarity judgments than in categorization judgments (Experiment 4). Experiment 5 shows that the size of the causal status effect is moderated by plausibility of causal relations, and Experiment 6 shows that effect features can be useful in retrieving information about unknown causes. We discuss the scope of the causal status effect and its implications for categorization research.

Why are different features central for natural kinds and artifacts?: the role of causal status in determining feature centrality.

Ahn and Lassaline [Ahn, W., Lassaline, M.E., 1995. Causal structure in categorization. Proceedings of the Seventeenth Annual Conference of the Cognitive Science Society, Pittsburgh, PA, pp. 521-526] recently proposed a causal status hypothesis which states that features that play a causal role in a relational structure are more central than their effects. This hypothesis can account for previous research demonstrating that compositional features are generally important for natural kinds but functional features are generally important for artifacts. The causal status hypothesis explains this category-feature interaction effect in terms of differences in the causal status of compositional and functional features between natural kinds and artifacts. Experiments 1 and 2 examined real-life categories used in previous studies, and found positive correlations between the causal status of the features and their centrality across natural and artifactual kinds. Experiments 3 and 4 manipulated the causal status of compositional and functional features in artificial categories, and showed that it was causal status rather than the interaction between the type of feature and the type of category per se that accounted for feature centrality. The implications of these results on the distinctions between natural kinds and artifacts are discussed.

A two-stage model of category construction ☆

The current consensus is that most natural categories are not organized around strict definitions (a list of singly necessary and jointly sufficient features) but rather according to a family resemblance (FR) principle: Objects belong to the same category because they are similar to each other and dissimilar to objects in contrast categories. A number of computational models of category construction have been developed to provide an account of how and why people create FR categories (Anderson, 1990; Fisher, 1987). Surprisingly, however, only a few experiments on category construction or free sorting have been run and they suggest that people do not sort examples by the FR principle. We report several new experiments and a two-stage model for category construction. This model is contrasted with a variety of other models with respect to their ability to account for when FR sorting will and will not occur. The experiments serve to identify one basis for FR sorting and to support the two-stage model. The distinctive property of the two-stage model is that it assumes that people impose more structure than the examples support in the first stage and that the second stage adjusts for this difference between preferred and perceived structure. We speculate that people do not simply assimilate probabilistic structures but rather organize them in terms of discrete structures plus noise.

Schema Acquisition From a Single Example

This study compares Similarity-Based Learning (SBL) and Explanation-Based Learning (EBL) approaches to schema acquisition. In SBL approaches, concept formation is based on similarity across multiple examples. Howevr, these approaches seem to be appropriate when the learner cannot apply existing knowledge and when the concepts to be learned are nonexplanatory. EBL approaches assume that a schema can be acquired from even a single example by constructing an explanation of the example using background knowledge, and generalizing the resulting explanation

Why essences are essential in the psychology of concepts

Woo-kyoung Ahn*, Charles Kalish, Susan A. Gelman, Douglas L. Medin, Christian Luhmann, Scott Atran, John D. Coley, Patrick Shafto Department of Psychology, Vanderbilt University, Nashville, TN 37203, USA Department of Educational Psychology, University of Wisconsin, Madison, WI 53706, USA Department of Psychology, University of Michigan, Ann Arbor, MI 48109-1109, USA Department of Psychology, Northwestern University, Evanston, IL 60208-2710, USA Centre National de la Recherche Scientifique, Marseilles, France Department of Psychology, Northeastern University, Boston, MA 02115, USA

Can Clinicians Recognize DSM-IV Personality Disorders From Five-Factor Model Descriptions of Patient Cases?

OBJECTIVE This article examined, using theories from cognitive science, the clinical utility of the Five-Factor Model (FFM) of Personality, an assessment and classification system under consideration for integration into the forthcoming fifth edition of the Diagnostic and Statistical Manual (DSM) of Mental Disorders. Specifically, the authors sought to test whether FFM descriptors are specific enough to allow practicing clinicians to capture core features of personality disorders. METHOD In two studies, a large nationwide sample of clinical psychologists, psychiatrists, and clinical social workers (N=187 and N=191) were presented case profiles based on symptom formats from either the DSM-IV and/or FFM. Ratings for six aspects of clinical utility for DSM-IV and FFM profiles were obtained and participants provided DSM-IV diagnoses. Prototypic cases (only one personality disorder) and comorbid cases were tested in separate studies. RESULTS Participants rated the DSM-IV as more clinically useful than the FFM on five out of six clinical utility questions. Despite demonstrating considerable background knowledge of DSM-IV diagnoses, participants had difficulty identifying correct diagnoses from FFM profiles. CONCLUSION The FFM descriptors may be more ambiguous than the criteria of the DSM-IV and the FFM may therefore be less able to convey important clinical details than the DSM-IV. The findings flag challenges to clinical utility for dimensional-trait systems such as the FFM.

Categorization inside and outside the laboratory : essays in honor of Douglas L. Medin

This volume raises key questions about the nature and universality of naturally occurring concepts in human thinking. The work showcased in this book suggests that categories can differ significantly across cultures with respect to fundamental human concepts such as space, time, and objecthood. The building of bridges across communities that do not frequently interact has been inspired by the pioneering work of cognitive psychologist Douglas L. Medin, to whom this book is dedicated.

Causal Attribution as a Search for Underlying Mechanisms: An Explanation of the Conjunction Fallacy and the Discounting Principle☆

We propose that causal attribution involves searching for underlying mechanism information (i.e., the processes underlying the relationship between the cause and the effect). This processing account can explain both the conjunction effect (i.e., conjunctive explanations being rated more probable than their components) and the discounting effect (i.e., the effect of one cause being discounted when another cause is already known to be true). When two explanations cohere with respect to a single mechanism, they would be judged to be more likely than a single explanation which partly supports that mechanism. When the two explanations imply two separate mechanisms, one would be discounted. In Experiment 1, both effects occurred with mechanism-based explanations but not with covariation-based explanations in which the cause-effect relationship was phrased in terms of statistical covariations without referring to mechanisms. In Experiments 2 and 3, the amount of the discounting and conjunction effects varied depending on the relationships between specific mechanisms in the two given explanations. We discuss why the current results pose difficulties for previous attribution models.

Primacy in causal strength judgments: The effect of initial evidence for generative versus inhibitory relationships

The order in which people receive information has a substantial effect on subsequent judgment and inference. Our focus is on the order of covariation evidence in causal learning. The first experiment shows that the initial presentation of evidence suggesting a generative causal relationship (the joint presence or joint absence of cause and effect) leads to higher judged causal strength than does the initial presentation of evidence suggesting an inhibitory relationship (the presence of cause or effect in the absence of the other). Additional studies show that this primacy effect is unlikely to be due to fatigue or to an insufficient number of learning trials. These results are not readily explained by current contingency-based or associative theories of causal induction.

Causal status effect in children's categorization

The current study examined the causal status effect (weighing cause features more than effect features in categorization) in children. Adults (Study 1) and 7-9-year-old children (Study 2) learned descriptions of novel animals, in which one feature caused two other features. When asked to determine which transfer item was more likely to be an example of the animal they had learned, both adults and children preferred an animal with a cause feature and an effect feature rather than an animal with two effect features. This study is the first direct demonstration of the causal status effect in children.