Inducing causal relationships from observations is a classic problem in scientific inference, statistics, and machine learning. It is also a central part of human learning, and a task that people perform remarkably well given its notorious difficulties. People can learn causal structure in various settings, from diverse forms of data: observations of the co-occurrence frequencies between causes and effects, interactions between physical objects, or patterns of spatial or temporal coincidence. These different modes of learning are typically thought of as distinct psychological processes and are rarely studied together, but at heart they present the same inductive challenge—identifying the unobservable mechanisms that generate observable relations between variables, objects, or events, given only sparse and limited data. We present a computational-level analysis of this inductive problem and a framework for its solution, which allows us to model all these forms of causal learning in a common language. In this framework, causal induction is the product of domain-general statistical inference guided by domain-specific prior knowledge, in the form of an abstract causal theory. We identify 3 key aspects of abstract prior knowledge—the ontology of entities, properties, and relations that organizes a domain; the plausibility of specific causal relationships; and the functional form of those relationships—and show how they provide the constraints that people need to induce useful causal models from sparse data.

This chapter considers a set of questions at the interface of the study of intuitive theories, causal knowledge, and problems of inductive inference. By an intuitive theory, we mean a cognitive structure that in some important ways is analogous to a scientific theory. It is becoming broadly recognized that intuitive theories play essential roles in organizing

ABSTRACT: Mirror neurons are brain systems found in monkeys and humans that respond similarly to actions and to the perception of actions of others. This paper explores the implications of mirror neurons for several important philosophical problems, including knowledge of other minds, the nature of empathy, and moral motivation. It argues that mirror neurons provide a more direct route to other minds, empathy, and moral motivation that complements the more familiar route based on conscious, verbal inference. To show how mirror neurons accomplish these functions, I apply a neurocomputational account of representation and inference.

Abstract: Philosophy of psychology takes various forms. Some philosophers of psychology use psychological findings and theories to develop new answers to traditional philosophical issues. A smaller number of philosophers of psychology take their cue from the philosophy of science. They describe and evaluate the discovery heuristics, theories, and explanatory practices endorsed by psychologists. Finally, much philosophy of psychology can be characterized as psychological theorizing. Just like psychologists, philosophers propose empirical theories of specific aspects of our mind, trying to explain relevant psychological phenomena. Focusing mostly on this aspect of the philosophy of psychology, I will consider philosophers ’ contribution to the theoretical development of psychology in four areas: cognitive architecture and modularity (§2); situated, embodied and extended cognition (§3); concepts (§4), and mindreading (§6). 1 Before doing this, however, I will discuss philosophers’ and psychologists ’ views and arguments about the distinctive character of psychology—its mentalistic nature (§1).

We present an introduction to Bayesian inference as it is used in probabilistic models of cognitive development. Our goal is to provide an intuitive and accessible guide to the what, the how, and the why of the Bayesian approach: what sorts of problems and data the framework is most relevant for, and how and why it may be useful for developmentalists. We emphasize a qualitative understanding of Bayesian inference, but also include information about additional resources for those interested in the cognitive science applications, mathematical foundations, or machine learning details in more depth. In addition, we discuss some important interpretation issues that often arise when evaluating Bayesian models in cognitive science.