Detecting the causal relations among environmental events is an important facet of learning. Certain variables have been identified which influence both human causal attribution and animal learning: temporal priority, temporal and spatial contiguity, covariation and contingency, and prior experience, Recent research has continued to find distinct commonalities between the influence these variables have in the two domains, supporting a neo-Humean analysis ofthe origins of personal causal theories. The cues to causality determine which event relationships will be judged as causal; personal causal theories emerge as a result of these judgments and in turn affect future attributions. An examination of animal learning research motivates further extensions of the analogy. Researchers are encouraged to study real-time causal attributions, to study additional methodological analogies to conditioning paradigms, and to develop rich learning accounts of the acquisition of causal theories.It has been noted on many occasions that the concept of causality underlies much of human cognition and represents one of the major factors used in forward and backward inference. In social realms, inferential skill allows us to predict the behavior ofothers and to determine why they behave as they do. In science, we are engaged in an enterprise in which we seek to determine the causal relationships among observables, such as lesion locations and their behavioral effects, neural network architectures and their performance, and biological processes and cancer. In everyday life we strive to discover causes of events (Why is there excessive paint peeling on my house? Why is my child crying?) and to predict the effects of actions (Will she call me after what I said last night? What if I take the job?).Causal attribution affords two primary advantages: prediction and control. If an organism can discover the causal nature ofevents, it can predict what events will follow others, and thus it will be able to prepare for the arrival of events that are important to its survival, its enjoyment, and so forth. Control over events can be established if the causing event is one that the organism can produce or prevent. This dichotomy, prediction and control, is captured in the traditional animal learning literature by the processes of classical conditioning and instrumental training. The parallels between causal attribution and animal learning are extensive (Killeen, 1981;Mackintosh, 1977;Shanks & Dickinson, 1987; Wasserman, 1990a
83The work of Shanks and Dickinson (1987) and Wasserman (1990b) is the basis of a significant trend in the understanding of causal attributions from an animal learning perspective (see, e.g., Baker, Mercier, ValleeTourangeau, Frank, & Pan, 1993;Chapman, 1991;Chapman & Robbins, 1990;Reed, 1992;Shanks, 1989;Van Hamme, Kao, & Wasserman, 1993;Wasserman, Elek, Chatlosh, & Baker, 1993;Young & DeBauche, 1993). In this manuscript, I will reconsider this framework in order to (1) determine how well recent research can be integrated int...