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.

The ability to derive predictions for the outcomes of potential actions from observational data is one of the hallmarks of true causal reasoning. We present four learning experiments with deterministic and probabilistic data showing that people indeed make different predictions from causal models, whose parameters were learned in a purely observational learning phase, depending on whether learners believe that an event within the model has been merely observed (“seeing”) or was actively manipulated (“doing”). The predictions reflect sensitivity both to the structure of the causal models and to the size of their parameters. This competency is remarkable because the predictions for potential interventions were very different from the patterns that had actually been observed. Whereas associative and probabilistic theories fail, recent developments of causal Bayes net theories provide tools for modeling this competency. Causal knowledge underlies our ability to predict future events, to explain the occurrence of present events, and to achieve goals by means of actions. Thus, causal knowledge belongs to one of our most central cognitive competencies. However, the nature of causal knowledge has been debated. A number of philosophers and

For over 200 years, philosophers and mathematicians have been using probability theory to describe human cognition. While the theory of probabilities was first developed as a means of analyzing games of chance, it quickly took on a larger and deeper significance as a formal account of how rational agents should reason in situations of uncertainty

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

computation. In preparation. Address for correspondence:

thank members of the UBC Baby Cognition Lab for their help with data collection, and Paul Bloom, Geoff Hall, and Terry Regier for helpful discussion. We owe a particular debt to Liz Bonawitz, for discussions and pilot work on an earlier version of this work.

The role of action has been strongly emphasized, not only in cognitive research on learning and problem solving, but also in education and instructional psychology. The Constructivism tradition has long asserted that action plays a crucial role for learners in constructing their own knowledge. In an educational context, active engagement entails students examining their own ideas, considering alternative explanations for newly taught concepts, and evaluating competing perspectives. Some theorists (e.g., Anzai & Simon, 1979) propose that these processes are found when learning is by doing. However, a constructivist perspective implies that instructional formats enable self-monitoring (e.g., Covington, 2000; Pintrich & De Groot, 1990), which includes reflective activities such as describing, explaining, and evaluative thinking (e.g., Covington, 2000; Zimmerman, 1990), which are not exclusive to action. The present article discusses findings that concern two related and thus far, unexplored two questions: How affective is observation-based learning in a complex skill learning task that usually requires processes that involve active engagement with it? How does monitoring affect the transfer of problem solving ability in complex skill learning task? The first aim of the article is to introduce ways of using common educational tools like the self-observation technique, which involves re-exposing individuals to their own self-generated behaviors, in novel ways that can provides insight into how people use self-regulatory mechanisms like monitoring on internally represented behaviors. The second aim is provide support for the view that in the absence of active learning, learning indirectly (i.e. Observation-based learning) is a practical and in some cases necessary method of knowledge and skill acquisition, and does not in turn lead to decrements in acquired knowledge and skill. Finally, the article presents the argument that the degree of self-monitoring that takes place may be a mediating factor in preserving the view that action has a special status in knowledge acquisition.

People have a keen ability to reason about others ’ mental states, which is central for communication and cooperation. A core question for cognitive science is what mental representations support this ability. We offer one proposal based on the framework of influence diagrams, an extension of Bayes nets that is suited for representing intentional goal-directed agents. We evaluate this framework in two experiments that require participants to make inferences about what another person knows or values. In both experiments, participants ’ judgments were better predicted by our influence diagrams account than by several alternative accounts.

Previous studies have suggested better learning when people actively intervene rather than when they passively observe the stimuli in a judgment task. In 4 experiments, the authors investigated the hypothesis that this improvement is associated with a shift from exemplar memory to cue abstraction. In a multiple-cue judgment task with continuous cues, the data replicated the improvement with intervention and participants who experimented more actively produced more accurate judgments. In a multiple-cue judgment task with binary cues, intervention produced poorer accuracy and participants who experimented more actively produced poorer judgments. These results provide no support for a representational shift but suggest that the improvement with active intervention may be limited to certain tasks and environments.

A normative framework for modeling causal and counterfactual reasoning has been proposed by Spirtes, Glymour, and Scheines (1993; cf. Pearl, 2000). The framework takes as fundamental that reasoning from observation and intervention differ. Intervention includes actual manipulation as well as counterfactual manipulation of a model via thought. To represent intervention, Pearl employed the do operator that simplifies the structure of a causal model by disconnecting an intervened-on variable from its normal causes. Construing the do operator as a psychological function affords predictions about how people reason when asked counterfactual questions about causal relations that we refer to as undoing, a family of effects that derive from the claim that intervened-on variables become independent of their normal causes. Six studies support the prediction for causal (A causes B) arguments but not consistently for parallel conditional (if A then B) ones. Two of the studies show that effects are treated as diagnostic when their values are observed but nondiagnostic when they are intervened on. These results cannot be explained by theories that do not distinguish interventions from other sorts of events.