Nadya Vasilyeva

Generating Inductive Inferences: Premise Relations and Property Effects

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.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.

Contextual utility affects the perceived quality of explanations

Are explanations of different kinds (formal, mechanistic, teleological) judged differently depending on their contextual utility, defined as the extent to which they support the kinds of inferences required for a given task? We report three studies demonstrating that the perceived “goodness” of an explanation depends on the evaluator’s current task: Explanations receive a relative boost when they support task-relevant inferences, even when all three explanation types are warranted. For example, mechanistic explanations receive higher ratings when participants anticipate making further inferences on the basis of proximate causes than when they anticipate making further inferences on the basis of category membership or functions. These findings shed light on the functions of explanation and support pragmatic and pluralist approaches to explanation.

Chapter 5 - Generating Inductive Inferences: Premise Relations and Property Effects

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.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.

Stable Causal Relationships Are Better Causal Relationships

We report three experiments investigating whether peoples judgments about causal relationships are sensitive to the robustness or stability of such relationships across a range of background circumstances. In Experiment 1, we demonstrate that people are more willing to endorse causal and explanatory claims based on stable (as opposed to unstable) relationships, even when the overall causal strength of the relationship is held constant. In Experiment 2, we show that this effect is not driven by a causal generalizations actual scope of application. In Experiment 3, we offer evidence that stable causal relationships may be seen as better guides to action. Collectively, these experiments document a previously underappreciated factor that shapes peoples causal reasoning: the stability of the causal relationship.

The development of structural thinking about social categories.

Representations of social categories help us make sense of the social world, supporting predictions and explanations about groups and individuals. In an experiment with 156 participants, we explore whether children and adults are able to understand category-property associations (such as the association between “girls” and “liking pink”) in structural terms, locating an object of explanation within a larger structure and identifying structural constraints that act on elements of the structure. We show that children as young as 3–4 years old show signs of structural thinking, and that 5–6-year-olds show additional differentiation between structural and nonstructural thinking, yet still fall short of adult performance. These findings introduce structural connections as a new type of nonaccidental relationship between a property and a category, and present a viable alternative to internalist accounts of social categories, such as psychological essentialism.