The Experts below are selected from a list of 4644 Experts worldwide ranked by ideXlab platform
Michael J Zvolensky - One of the best experts on this subject based on the ideXlab platform.
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Obsessive-compulsive symptoms and problematic alcohol use: The Explanatory Role of drinking motives.
Addictive behaviors, 2020Co-Authors: Jafar Bakhshaie, Eric A. Storch, Michael J ZvolenskyAbstract:Abstract The purpose of the present investigation was to examine the unique Explanatory Role of alcohol use motives above the effects of each other, using a multiple mediation model, for the relationship between obsessive-compulsive symptomatology and problematic alcohol use among a young ethno-racially diverse sample of college students with current (past-month) alcohol use. Participants were 454 college students (81.72% female, Mage=22.46, SD=4.71). Results indicated that coping motives significantly explained the relationship between obsessive-compulsive symptoms, and alcohol consumption (past year), risky drinking, and recent alcohol use (past month) as behavioral indices of problematic drinking after controlling for the variance accounted for by recent cannabis use, smoking, and anxiety and depressive symptoms (with small to medium effect sizes). These findings are discussed in terms of the development of specialized treatments to specifically target coping oriented alcohol use motives in the context of obsessive-compulsive symptoms among young adults with clinically significant OCD symptoms and comorbid problematic alcohol use.
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Obsessive-Compulsive Symptoms and Cannabis Misuse: The Explanatory Role of Cannabis Use Motives.
Journal of dual diagnosis, 2020Co-Authors: Jafar Bakhshaie, Eric A. Storch, Nhan Tran, Michael J ZvolenskyAbstract:The purpose of the present investigation was to examine the unique Explanatory Role of cannabis use motives above the effects of each other, for the relationship between obsessive-compulsive sympto...
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The Explanatory Role of anxiety sensitivity in the association between effortful control and child anxiety and depressive symptoms
Cognitive behaviour therapy, 2020Co-Authors: Elizabeth M. Raines, Michael J Zvolensky, Andres G. Viana, Erika S. Trent, Haley E. Conroy, Emma C. Woodward, Eric A. StorchAbstract:The present study examined the underlying Role of anxiety sensitivity in the association between effortful control and anxiety and depressive symptoms in a sample of clinically anxious children. It...
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Emotion dysregulation and body mass index: The Explanatory Role of emotional eating among adult smokers.
Eating behaviors, 2019Co-Authors: Jenna Jones, Brooke Y Kauffman, David Rosenfield, Jasper A. J. Smits, Michael J ZvolenskyAbstract:Abstract There is limited understanding of the relationship between emotion dysregulation and weight gain among smokers, although available data suggest there are potential relationships that may be of clinical importance. The present study explored a potential mechanism in the relationship between emotion dysregulation and body mass index (BMI). Specifically, the current study examined the indirect effects of emotional eating on the association between emotion dysregulation and BMI among smokers. Participants included 136 (52.2% female; Mage = 42.25, SD = 11.24) adults who were treatment-seeking smokers. Primary analysis included one regression-based model, wherein emotion dysregulation served as the predictor, emotional eating as the intermediary variable, and BMI as the criterion variable. Covariates were age and gender. Results indicated that emotional dysregulation was significantly associated with BMI through emotional eating (a*b = 0.02, SE = 0.01, CI95% = 0.002, 0.042). The current findings provide initial empirical evidence that greater reported levels of emotion dysregulation may be associated with greater reported levels of emotional eating, which in turn, may be related to higher BMI.
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Pain-related anxiety and smoking processes: The Explanatory Role of dysphoria.
Addictive behaviors, 2018Co-Authors: Tanya Smit, Kirsten J Langdon, Lorra Garey, Natalia Peraza, Joseph W. Ditre, Andrew H. Rogers, Kara Manning, Michael J ZvolenskyAbstract:Abstract Scientific evidence suggests that pain-related anxiety may contribute to the maintenance of tobacco addiction among smokers with varying levels of pain. Yet, no work has investigated the relation between pain-related anxiety and cognitive-based smoking processes within an indirect effect model. Dysphoria may explain the relation between pain-related anxiety and cigarette smoking, as it is a construct that relates to both pain and smoking outcomes. Thus, the current cross-sectional study examined the indirect effect of pain-related anxiety and three clinically significant smoking processes: perceived barriers to cessation, negative affect reduction motives, and negative mood abstinence expectancies via dysphoria. Participants included 101 (Mage = 32.74 years, SD = 13.60; 35.6% female) adult tobacco cigarette smokers with low cigarette dependence. Results indicated that pain-related anxiety had an indirect effect on all dependent variables through dysphoria. The current findings provide evidence that dysphoria may serve to maintain maladaptive smoking processes in smokers who experience pain-related anxiety. This study furthers research on pain-smoking relations by providing initial evidence for a conceptual model in which smokers with elevated pain-related anxiety endorse greater dysphoric symptoms and use smoking to reduce or escape symptoms of their pain-related anxiety and dysphoria, thus contributing to the maintenance of tobacco dependence.
Anselm Rothe - One of the best experts on this subject based on the ideXlab platform.
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causal status meets coherence the Explanatory Role of causal models in categorization
Cognitive Science, 2012Co-Authors: Ralf Mayrhofer, Anselm RotheAbstract:Causal Status meets Coherence: The Explanatory Role of Causal Models in Categorization Ralf Mayrhofer (rmayrho@uni-goettingen.de) Anselm Rothe (anselm.rothe@stud.uni-goettingen.de) Department of Psychology, University of Gottingen, Goslerstrase 14, 37073 Gottingen, Germany Fratianne, 1995). A causal Bayes net consists of nodes, which represent causally relevant variables (i.e., in case of categorization: the presence or absence of features or—more general—properties of objects), and arrows, which stand for counterfactual or statistical dependencies between these variables. The arrows are placeholders for underlying causal mechanisms (Pearl, 2000) and render the variables into causes and effects. Figure 1 shows an example of a com- mon-cause network that relates a cause feature F C to three effect features F E1 , F E2 , and F E3 . The features of a category are usually coded such that the typical feature value is 1 (i.e., presence) and the atypical value is 0 (i.e., absence). Abstract Research on causal-based categorization has found two com- peting effects: According to the causal-status hypothesis, people consider causally central features more than less cen- tral ones. In contrast, people often focus upon feature patterns that are coherent with the category’s causal model (coherence hypothesis). Following up on the proposal that categorization can be seen as inference to the best explanation (e.g., Murphy & Medin, 1985), we propose that causal models might serve different Explanatory Roles. First, a causal model can serve as an explanation why the prototype of a category is as it is. Se- cond, a causal model can also serve as an explanation why an exemplar might deviate from the prototype. In an experiment, we manipulated whether typical or atypical features were linked by causal mechanism. We found a causal-status effect in the first case and a coherence effect in the latter one, sug- gesting both are faces of the same coin. F E1 Keywords: categorization; causal reasoning; causal status ef- fect; coherence effect; explanation. F C F E2 Introduction The question how people organize objects into categories and form abstract concepts about the world to make sense of it has puzzled philosophers for centuries. It is therefore not surprising that categorization has been an important topic in cognitive science since its beginnings. Early but neverthe- less prominent accounts concentrated on the Role of similari- ty between exemplars, or exemplars and category proto- types, or rules with respect to defining features of a category (e.g., Nosofsky, 1986; Rosch & Mervis, 1975; for an over- view see Ashby & Maddox, 2005). In contrast, more recent accounts emphasize the Role of abstract conceptual, mostly causal knowledge as an integral part of category representa- tions (see Murphy & Medin, 1985; Rehder, 2010; Rehder & Hastie, 2001; Sloman, Love, & Ahn, 1998): People do not only know which features are typical for a category and which not. They often represent knowledge about how strongly and why features are correlated with each other within a category (Ahn, Marsh, Luhmann, & Lee, 2002; Murphy & Medin, 1985). For instance, people do not only know that birds typically have wings, can fly, and build nests on trees. People also know that birds build nests on trees because they can fly and that they can fly because they have wings. This kind of causal knowledge underlying category con- cepts can be formalized in causal graphical models or Bayes nets (see Rehder, 2003a, 2003b; Waldmann, Holyoak, & F E3 Figure 1: An example of a simple common-cause structure that connects a cause feature F C with three effect features F E1 , F E2 , and F E3 . Due to the causal relations, the state of each effect feature depends counterfactually or statistically upon the state of the cause feature. Nowadays, it’s quite uncontroversial that causal know- ledge is an important part of people’s concepts that underlie category representation (see Rehder, 2010, for a review). But it is still in controversial debate how causal knowledge affects the classification of objects. In a typical causal-based categorization task people are introduced to a target category that possesses a set of mostly three or four features. In addition, it is pointed out how these features are causally related to each other due to some caus- al mechanisms that hold for the category (e.g., a common- cause model as shown in Figure 1). Then, participants are presented with several potential exemplars with the catego- ry’s features being either present or absent. For each of the presented exemplars, membership ratings are obtained. The enduring controversy, then, spans around the question how the instructed causal model interacts with the presence and
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CogSci - Causal Status meets Coherence: The Explanatory Role of Causal Models in Categorization
Cognitive Science, 2012Co-Authors: Ralf Mayrhofer, Anselm RotheAbstract:Causal Status meets Coherence: The Explanatory Role of Causal Models in Categorization Ralf Mayrhofer (rmayrho@uni-goettingen.de) Anselm Rothe (anselm.rothe@stud.uni-goettingen.de) Department of Psychology, University of Gottingen, Goslerstrase 14, 37073 Gottingen, Germany Fratianne, 1995). A causal Bayes net consists of nodes, which represent causally relevant variables (i.e., in case of categorization: the presence or absence of features or—more general—properties of objects), and arrows, which stand for counterfactual or statistical dependencies between these variables. The arrows are placeholders for underlying causal mechanisms (Pearl, 2000) and render the variables into causes and effects. Figure 1 shows an example of a com- mon-cause network that relates a cause feature F C to three effect features F E1 , F E2 , and F E3 . The features of a category are usually coded such that the typical feature value is 1 (i.e., presence) and the atypical value is 0 (i.e., absence). Abstract Research on causal-based categorization has found two com- peting effects: According to the causal-status hypothesis, people consider causally central features more than less cen- tral ones. In contrast, people often focus upon feature patterns that are coherent with the category’s causal model (coherence hypothesis). Following up on the proposal that categorization can be seen as inference to the best explanation (e.g., Murphy & Medin, 1985), we propose that causal models might serve different Explanatory Roles. First, a causal model can serve as an explanation why the prototype of a category is as it is. Se- cond, a causal model can also serve as an explanation why an exemplar might deviate from the prototype. In an experiment, we manipulated whether typical or atypical features were linked by causal mechanism. We found a causal-status effect in the first case and a coherence effect in the latter one, sug- gesting both are faces of the same coin. F E1 Keywords: categorization; causal reasoning; causal status ef- fect; coherence effect; explanation. F C F E2 Introduction The question how people organize objects into categories and form abstract concepts about the world to make sense of it has puzzled philosophers for centuries. It is therefore not surprising that categorization has been an important topic in cognitive science since its beginnings. Early but neverthe- less prominent accounts concentrated on the Role of similari- ty between exemplars, or exemplars and category proto- types, or rules with respect to defining features of a category (e.g., Nosofsky, 1986; Rosch & Mervis, 1975; for an over- view see Ashby & Maddox, 2005). In contrast, more recent accounts emphasize the Role of abstract conceptual, mostly causal knowledge as an integral part of category representa- tions (see Murphy & Medin, 1985; Rehder, 2010; Rehder & Hastie, 2001; Sloman, Love, & Ahn, 1998): People do not only know which features are typical for a category and which not. They often represent knowledge about how strongly and why features are correlated with each other within a category (Ahn, Marsh, Luhmann, & Lee, 2002; Murphy & Medin, 1985). For instance, people do not only know that birds typically have wings, can fly, and build nests on trees. People also know that birds build nests on trees because they can fly and that they can fly because they have wings. This kind of causal knowledge underlying category con- cepts can be formalized in causal graphical models or Bayes nets (see Rehder, 2003a, 2003b; Waldmann, Holyoak, & F E3 Figure 1: An example of a simple common-cause structure that connects a cause feature F C with three effect features F E1 , F E2 , and F E3 . Due to the causal relations, the state of each effect feature depends counterfactually or statistically upon the state of the cause feature. Nowadays, it’s quite uncontroversial that causal know- ledge is an important part of people’s concepts that underlie category representation (see Rehder, 2010, for a review). But it is still in controversial debate how causal knowledge affects the classification of objects. In a typical causal-based categorization task people are introduced to a target category that possesses a set of mostly three or four features. In addition, it is pointed out how these features are causally related to each other due to some caus- al mechanisms that hold for the category (e.g., a common- cause model as shown in Figure 1). Then, participants are presented with several potential exemplars with the catego- ry’s features being either present or absent. For each of the presented exemplars, membership ratings are obtained. The enduring controversy, then, spans around the question how the instructed causal model interacts with the presence and
Lorra Garey - One of the best experts on this subject based on the ideXlab platform.
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Pain-related anxiety and smoking processes: The Explanatory Role of dysphoria.
Addictive behaviors, 2018Co-Authors: Tanya Smit, Kirsten J Langdon, Lorra Garey, Natalia Peraza, Joseph W. Ditre, Andrew H. Rogers, Kara Manning, Michael J ZvolenskyAbstract:Abstract Scientific evidence suggests that pain-related anxiety may contribute to the maintenance of tobacco addiction among smokers with varying levels of pain. Yet, no work has investigated the relation between pain-related anxiety and cognitive-based smoking processes within an indirect effect model. Dysphoria may explain the relation between pain-related anxiety and cigarette smoking, as it is a construct that relates to both pain and smoking outcomes. Thus, the current cross-sectional study examined the indirect effect of pain-related anxiety and three clinically significant smoking processes: perceived barriers to cessation, negative affect reduction motives, and negative mood abstinence expectancies via dysphoria. Participants included 101 (Mage = 32.74 years, SD = 13.60; 35.6% female) adult tobacco cigarette smokers with low cigarette dependence. Results indicated that pain-related anxiety had an indirect effect on all dependent variables through dysphoria. The current findings provide evidence that dysphoria may serve to maintain maladaptive smoking processes in smokers who experience pain-related anxiety. This study furthers research on pain-smoking relations by providing initial evidence for a conceptual model in which smokers with elevated pain-related anxiety endorse greater dysphoric symptoms and use smoking to reduce or escape symptoms of their pain-related anxiety and dysphoria, thus contributing to the maintenance of tobacco dependence.
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Sex differences in smoking constructs and abstinence: The Explanatory Role of smoking outcome expectancies.
Psychology of addictive behaviors : journal of the Society of Psychologists in Addictive Behaviors, 2018Co-Authors: Lorra Garey, Clayton Neighbors, Natalia Peraza, Tanya Smit, Nubia A. Mayorga, Amanda M. Raines, Norman B. Schmidt, Michael J ZvolenskyAbstract:Scientific evidence suggests women experience more severe problems when attempting to quit smoking relative to men. Yet, little work has examined potential Explanatory variables that maintain sex differences in clinically relevant smoking processes. Smoking outcome expectancies have demonstrated sex differences and associative relations with the smoking processes and behavior, including problems when attempting to quit, smoking-specific experiential avoidance, perceived barriers to quitting, and smoking abstinence. Thus, expectancies about the consequences of smoking may explain sex differences across these variables. Accordingly, the current study examined the Explanatory Role of smoking-outcome expectancies (e.g., long-term negative consequences, immediate negative consequences, sensory satisfaction, negative affect reduction, and appetite weight control) in models of sex differences across cessation-related problems, smoking-specific experiential avoidance, perceived barriers to quitting, and smoking abstinence. Participants included 450 (48.4% female; Mage = 37.45, SD = 13.50) treatment-seeking adult smokers. Results indicated that sex had an indirect effect on problems when attempting to quit smoking through immediate negative consequences and negative affect reduction expectancies; on smoking-specific experiential avoidance through long-term negative consequences, immediate negative consequences, and negative affect reduction expectancies; on barriers to quitting through negative affect reduction expectancies; and on abstinence through appetite weight control expectancies. The current findings suggest that sex differences in negative affect reduction expectancies and negative consequences expectancies may serve to maintain maladaptive smoking processes, whereas appetite weight control expectancies may promote short-term abstinence. These findings provide initial evidence for the conceptual Role of smoking expectancies as potential "linking variables" for sex differences in smoking variables. (PsycINFO Database Record
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financial strain and cognitive based smoking processes the Explanatory Role of depressive symptoms among adult daily smokers
Addictive Behaviors, 2017Co-Authors: Zuzuky Robles, Lorraine R Reitzel, Kirsten J Langdon, Sahar Anjum, Lorra Garey, Brooke Y Kauffman, Ruben Rodriguezcano, Clayton Neighbors, Michael J ZvolenskyAbstract:Abstract Little work has focused on the underlying mechanisms that may link financial strain and smoking processes. The current study tested the hypothesis that financial strain would exert an indirect effect on cognitive-based smoking processes via depressive symptoms. Three clinically significant dependent variables linked to the maintenance of smoking were evaluated: negative affect reduction motives, negative mood abstinence expectancies, and perceived barriers for quitting. Participants included 102 adult daily smokers (Mage = 33.0 years, SD = 13.60; 35.3% female) recruited from the community to participate in a self-guided (unaided; no psychological or pharmacological intervention) smoking cessation study. Results indicated that depressive symptoms explain, in part, the relation between financial strain and smoking motives for negative affect reduction, negative mood abstinence expectancies, and perceived barriers for quitting. Results indicate that smoking interventions for individuals with high levels of financial strain may potentially benefit from the addition of therapeutic tactics aimed at reducing depression.
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Body Mass Index and functional impairment: the Explanatory Role of anxiety sensitivity among treatment-seeking smokers.
Psychology health & medicine, 2017Co-Authors: Brooke Y Kauffman, Lorra Garey, Amanda M. Raines, Norman B. Schmidt, Charles Jardin, Michael W. Otto, Michael J ZvolenskyAbstract:Obesity and smoking are highly prevalent public health concerns in the United States. Data indicate that elevated Body Mass Index (BMI) is related to functional impairment. However, there is limited understanding of mechanisms that may explain their comorbidity among smokers. The current study sought to test whether anxiety sensitivity explained the relation between BMI and functional impairment among 420 (46.9% females; Mage = 38 years, SD = 13.42) treatment-seeking, adult smokers. Results indicated that BMI yielded a significant indirect effect through anxiety sensitivity for functional impairment, b = 0.01, SE = .01, 95% CI = [.002, .021]. These findings remained significant after controlling for participant sex, negative affectivity, tobacco dependence, psychopathology, and medical conditions (i.e. hypertension, heart problems, respiratory disease, asthma). Such data provide novel empirical evidence that, among smokers, BMI may be a risk factor for functional impairment indirectly through anxiety sensitivity. Overall, such findings could potentially inform the development of personalized interventions among this particularly vulnerable segment of the smoking population.
Philip Kitcher - One of the best experts on this subject based on the ideXlab platform.
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On the Explanatory Role of Correspondence Truth
Philosophy and Phenomenological Research, 2002Co-Authors: Philip KitcherAbstract:An intuitive argument for scientific realism suggests that our successes in predicting and intervening would be inexplicable if the theories that generate them were not approximately true. This argument faces many objections, some of which are briefly addressed in this paper, and one of which is treated in more detail. The focal criticism alleges that appeals to success cannot deliver conclusions that parts of science are true in the sense of truth-as-correspondence that realists prefer. The paper responds to that criticism, in versions proposed by Michael Williams, Michael Levin, and, especially, Paul Horwich, by arguing that critics typically stop at a shallow level of psychological explanation. If we probe more deeply we discover a genuine Explanatory Role for correspondence truth.
Ralf Mayrhofer - One of the best experts on this subject based on the ideXlab platform.
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causal status meets coherence the Explanatory Role of causal models in categorization
Cognitive Science, 2012Co-Authors: Ralf Mayrhofer, Anselm RotheAbstract:Causal Status meets Coherence: The Explanatory Role of Causal Models in Categorization Ralf Mayrhofer (rmayrho@uni-goettingen.de) Anselm Rothe (anselm.rothe@stud.uni-goettingen.de) Department of Psychology, University of Gottingen, Goslerstrase 14, 37073 Gottingen, Germany Fratianne, 1995). A causal Bayes net consists of nodes, which represent causally relevant variables (i.e., in case of categorization: the presence or absence of features or—more general—properties of objects), and arrows, which stand for counterfactual or statistical dependencies between these variables. The arrows are placeholders for underlying causal mechanisms (Pearl, 2000) and render the variables into causes and effects. Figure 1 shows an example of a com- mon-cause network that relates a cause feature F C to three effect features F E1 , F E2 , and F E3 . The features of a category are usually coded such that the typical feature value is 1 (i.e., presence) and the atypical value is 0 (i.e., absence). Abstract Research on causal-based categorization has found two com- peting effects: According to the causal-status hypothesis, people consider causally central features more than less cen- tral ones. In contrast, people often focus upon feature patterns that are coherent with the category’s causal model (coherence hypothesis). Following up on the proposal that categorization can be seen as inference to the best explanation (e.g., Murphy & Medin, 1985), we propose that causal models might serve different Explanatory Roles. First, a causal model can serve as an explanation why the prototype of a category is as it is. Se- cond, a causal model can also serve as an explanation why an exemplar might deviate from the prototype. In an experiment, we manipulated whether typical or atypical features were linked by causal mechanism. We found a causal-status effect in the first case and a coherence effect in the latter one, sug- gesting both are faces of the same coin. F E1 Keywords: categorization; causal reasoning; causal status ef- fect; coherence effect; explanation. F C F E2 Introduction The question how people organize objects into categories and form abstract concepts about the world to make sense of it has puzzled philosophers for centuries. It is therefore not surprising that categorization has been an important topic in cognitive science since its beginnings. Early but neverthe- less prominent accounts concentrated on the Role of similari- ty between exemplars, or exemplars and category proto- types, or rules with respect to defining features of a category (e.g., Nosofsky, 1986; Rosch & Mervis, 1975; for an over- view see Ashby & Maddox, 2005). In contrast, more recent accounts emphasize the Role of abstract conceptual, mostly causal knowledge as an integral part of category representa- tions (see Murphy & Medin, 1985; Rehder, 2010; Rehder & Hastie, 2001; Sloman, Love, & Ahn, 1998): People do not only know which features are typical for a category and which not. They often represent knowledge about how strongly and why features are correlated with each other within a category (Ahn, Marsh, Luhmann, & Lee, 2002; Murphy & Medin, 1985). For instance, people do not only know that birds typically have wings, can fly, and build nests on trees. People also know that birds build nests on trees because they can fly and that they can fly because they have wings. This kind of causal knowledge underlying category con- cepts can be formalized in causal graphical models or Bayes nets (see Rehder, 2003a, 2003b; Waldmann, Holyoak, & F E3 Figure 1: An example of a simple common-cause structure that connects a cause feature F C with three effect features F E1 , F E2 , and F E3 . Due to the causal relations, the state of each effect feature depends counterfactually or statistically upon the state of the cause feature. Nowadays, it’s quite uncontroversial that causal know- ledge is an important part of people’s concepts that underlie category representation (see Rehder, 2010, for a review). But it is still in controversial debate how causal knowledge affects the classification of objects. In a typical causal-based categorization task people are introduced to a target category that possesses a set of mostly three or four features. In addition, it is pointed out how these features are causally related to each other due to some caus- al mechanisms that hold for the category (e.g., a common- cause model as shown in Figure 1). Then, participants are presented with several potential exemplars with the catego- ry’s features being either present or absent. For each of the presented exemplars, membership ratings are obtained. The enduring controversy, then, spans around the question how the instructed causal model interacts with the presence and
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CogSci - Causal Status meets Coherence: The Explanatory Role of Causal Models in Categorization
Cognitive Science, 2012Co-Authors: Ralf Mayrhofer, Anselm RotheAbstract:Causal Status meets Coherence: The Explanatory Role of Causal Models in Categorization Ralf Mayrhofer (rmayrho@uni-goettingen.de) Anselm Rothe (anselm.rothe@stud.uni-goettingen.de) Department of Psychology, University of Gottingen, Goslerstrase 14, 37073 Gottingen, Germany Fratianne, 1995). A causal Bayes net consists of nodes, which represent causally relevant variables (i.e., in case of categorization: the presence or absence of features or—more general—properties of objects), and arrows, which stand for counterfactual or statistical dependencies between these variables. The arrows are placeholders for underlying causal mechanisms (Pearl, 2000) and render the variables into causes and effects. Figure 1 shows an example of a com- mon-cause network that relates a cause feature F C to three effect features F E1 , F E2 , and F E3 . The features of a category are usually coded such that the typical feature value is 1 (i.e., presence) and the atypical value is 0 (i.e., absence). Abstract Research on causal-based categorization has found two com- peting effects: According to the causal-status hypothesis, people consider causally central features more than less cen- tral ones. In contrast, people often focus upon feature patterns that are coherent with the category’s causal model (coherence hypothesis). Following up on the proposal that categorization can be seen as inference to the best explanation (e.g., Murphy & Medin, 1985), we propose that causal models might serve different Explanatory Roles. First, a causal model can serve as an explanation why the prototype of a category is as it is. Se- cond, a causal model can also serve as an explanation why an exemplar might deviate from the prototype. In an experiment, we manipulated whether typical or atypical features were linked by causal mechanism. We found a causal-status effect in the first case and a coherence effect in the latter one, sug- gesting both are faces of the same coin. F E1 Keywords: categorization; causal reasoning; causal status ef- fect; coherence effect; explanation. F C F E2 Introduction The question how people organize objects into categories and form abstract concepts about the world to make sense of it has puzzled philosophers for centuries. It is therefore not surprising that categorization has been an important topic in cognitive science since its beginnings. Early but neverthe- less prominent accounts concentrated on the Role of similari- ty between exemplars, or exemplars and category proto- types, or rules with respect to defining features of a category (e.g., Nosofsky, 1986; Rosch & Mervis, 1975; for an over- view see Ashby & Maddox, 2005). In contrast, more recent accounts emphasize the Role of abstract conceptual, mostly causal knowledge as an integral part of category representa- tions (see Murphy & Medin, 1985; Rehder, 2010; Rehder & Hastie, 2001; Sloman, Love, & Ahn, 1998): People do not only know which features are typical for a category and which not. They often represent knowledge about how strongly and why features are correlated with each other within a category (Ahn, Marsh, Luhmann, & Lee, 2002; Murphy & Medin, 1985). For instance, people do not only know that birds typically have wings, can fly, and build nests on trees. People also know that birds build nests on trees because they can fly and that they can fly because they have wings. This kind of causal knowledge underlying category con- cepts can be formalized in causal graphical models or Bayes nets (see Rehder, 2003a, 2003b; Waldmann, Holyoak, & F E3 Figure 1: An example of a simple common-cause structure that connects a cause feature F C with three effect features F E1 , F E2 , and F E3 . Due to the causal relations, the state of each effect feature depends counterfactually or statistically upon the state of the cause feature. Nowadays, it’s quite uncontroversial that causal know- ledge is an important part of people’s concepts that underlie category representation (see Rehder, 2010, for a review). But it is still in controversial debate how causal knowledge affects the classification of objects. In a typical causal-based categorization task people are introduced to a target category that possesses a set of mostly three or four features. In addition, it is pointed out how these features are causally related to each other due to some caus- al mechanisms that hold for the category (e.g., a common- cause model as shown in Figure 1). Then, participants are presented with several potential exemplars with the catego- ry’s features being either present or absent. For each of the presented exemplars, membership ratings are obtained. The enduring controversy, then, spans around the question how the instructed causal model interacts with the presence and
