this paper. We also thank Barbara Malt for providing the list of basic properties and the family resemblance weightings of properties used in Experiment 4. Correspondence concerning this article should be sent to W.-K. Ahn, Department of Psychology, Vanderbilt University, Wilson Hall, 111 21st Avenue South, Nashville, TN 37203 (e-mail: woo-kyoung.ahn@vander bilt.edu). ---Accepted by previous editorial team. Effect of theory-based feature correlations on typicality judgments WOO-KYOUNG AHN, JESSECAE K. MARSH, and CHRISTIAN C. LUHMANN Vanderbilt University, Nashville, Tennessee and KEVIN LEE Yale University, New Haven, Connecticut In the present study, we examine what types of feature correlations are salient in our conceptual representations

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

People possess theoretical knowledge about categories beyond what they observe. For example, they not only know that birds fly, have wings, and build nests in trees, but also that they build nests in trees because they can fly and can fly because they have wings. When features are arranged in a causal chain (e.g., X→Y→Z), two effects on classification have been established. First, a causal status effect obtains in which the root of the causal chain (X) has greater influence on classification than Y (and, in some studies, Y has greater influence than Z) (Ahn et al., 2000; Rehder & Kim, 2006). Second, a coherence effect obtains in which objects are better category members when their combination of features is consistent with the causal laws (e.g., X and Y both present or both absent) rather than inconsistent (e.g., X present and Y absent or vice versa) (e.g., Rehder, 2003).

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.

Causal graphical models (CGMs) have become popular in numerous domains of psychological research for representing people’s causal knowledge. Unfortunately, however, the CGMs typically used in cognitive models prohibit representations of causal cycles. Building on work in machine learning, we propose an extension of CGMs that allows cycles and apply that representation to one real-world reasoning task, namely, classification. Our model’s predictions were assessed in experiments that tested both probabilistic and deterministic causal relations. The results were qualitatively consistent with the predictions of our model and inconsistent with those of an alternative model. We naturally reason about causally related events that occur in cycles. In economics, we expect that an increase in corporate hiring may increase consumers ’ income and thus their demand for products, leading to a further increase in hiring. In meteorology, we expect that melting tundra due to global warming may release the greenhouse gas methane, leading to yet further warming. In psychology, we expect that clinicians will affect (hopefully help) their clients but also recognize the clients often affect the clinicians. Many psychologists investigate causal reasoning using a formalism known as Bayesian networks or causal graphical models (hereafter, CGMs). CGMs are one hypothesis for how people reason with causal knowledge. There are claims that causal learning amounts to acquiring the structure and/or parameters of a CGM (Cheng, 1997; Gopnik et al.,

Humans routinely answer questions, make decisions, and provide explanations in the face of incomplete knowledge and time constraints. From everyday questions like “What will it cost to take that vacation? ” to policy questions like “How can a carbon taxing scheme affect climate change? ” we often do not have all the knowledge, time and computational resources to come up with a precise, accurate answer. This thesis describes and formalizes Back of the Envelope (BotE) reasoning – the process of generating rough quantitative estimates. We claim that a core collection of seven heuristics: mereology, analogy, ontology, density, domain laws, balances and scale-up achieves broad coverage in BotE reasoning. We provide twofold support for this claim: 1) by evaluation of BotE-Solver, an implementation of our theory, on thirty five problems from the Science Olympics, and 2) by a corpus analysis of all the problems on Force and Pressure, Rotation and Mechanics, Heat, and Astronomy from Clifford Swartz's book (2003), “Back-of-the-envelope Physics.”

Abstract—The overall goal of this research is to study reasoning under uncertainty by combining Bayesian Networks and Case-Based Reasoning through constructing an experimental decision support system for classification of cancer pain. We have experimentally analysed a medical dataset in order to reveal properties of the data with respect to properties of the two reasoning methods. We also preprocessed our medical data with help from a clinical expert, which resulted in four data sets with different characteristics. This culminates in a hybrid system architecture, where CBR handles the exceptions or outliers with respect to the distribution of the data and the target class, while BN handles the more common situations. Through a set of experiments under varying conditions we show that a hybrid BN+CBR system is favorable over each single method.

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