In this response, I begin by defending and clarifying the notion of concept proposed in Doing without Concepts (Machery 2009) against the alternatives proposed by several commentators. I then discuss whether psychologists and philosophers who theorize about concepts are talking about distinct phenomena or about different aspects of the same phenomenon, as argued in some commentaries. Next, I criticize the idea that the cognitive-scientific findings about induction, categorization, concept combination, and so on, could be explained by positing a single kind (...) of concept, and I insist that many categories (substances, types of events, etc.) are represented by distinct coreferential concepts that belong to very different kinds of concept. This is followed by an assessment of the hybrid theories of concepts offered by commentators, according to which categories, substances, and types of events are represented by hybrid concepts made of several parts. Finally, I defend the proposal that it may be useful to eliminate concept from the theoretical vocabulary of psychology. (shrink)

Learning to understand a single causal system can be an achievement, but humans must learn about multiple causal systems over the course of a lifetime. We present a hierarchical Bayesian framework that helps to explain how learning about several causal systems can accelerate learning about systems that are subsequently encountered. Given experience with a set of objects, our framework learns a causal model for each object and a causal schema that captures commonalities among these causal models. The schema organizes the (...) objects into categories and specifies the causal powers and characteristic features of these categories and the characteristic causal interactions between categories. A schema of this kind allows causal models for subsequent objects to be rapidly learned, and we explore this accelerated learning in four experiments. Our results confirm that humans learn rapidly about the causal powers of novel objects, and we show that our framework accounts better for our data than alternative models of causal learning. (shrink)

It is often claimed that the greatest value of the Bayesian framework in cognitive science consists in its unifying power. Several Bayesian cognitive scientists assume that unification is obviously linked to explanatory power. But this link is not obvious, as unification in science is a heterogeneous notion, which may have little to do with explanation. While a crucial feature of most adequate explanations in cognitive science is that they reveal aspects of the causal mechanism that produces the phenomenon to be (...) explained, the kind of unification afforded by the Bayesian framework to cognitive science does not necessarily reveal aspects of a mechanism. Bayesian unification, nonetheless, can place fruitful constraints on causal–mechanical explanation. 1 Introduction2 What a Great Many Phenomena Bayesian Decision Theory Can Model3 The Case of Information Integration4 How Do Bayesian Models Unify?5 Bayesian Unification: What Constraints Are There on Mechanistic Explanation?5.1 Unification constrains mechanism discovery5.2 Unification constrains the identification of relevant mechanistic factors5.3 Unification constrains confirmation of competitive mechanistic models6 ConclusionAppendix. (shrink)