The concordance between performance and judgements of the causal effectiveness of an instrumental action suggests that such actions are mediated by causal knowledge. Although causal learning exhibits many associative phenomena—blocking, inhibitory or preventative learning, and super-learning—judgements of the causal status of a cue can be changed retrospectively as a result of learning episodes that do not directly involve the cue. In order to explain retrospective revaluation, a modi®ed associative theory is described in which the learning processes for retrieved cue representations are the opposite to those for presented cues, and this theory is evaluated by studies of the role of within-compound associations in retrospective revaluation and blocking. However, this modi®ed theory only applies when the within-compound association represents a contiguous rather than a causal cue relationship. Causal learning and representation is a fundamental form of cognition, if not the fundamental form. Without the capacity to learn about and represent the causal relationships between our actions and their consequences, the mind would be radically disconnected from the world. However detailed and rich our knowledge, however sophisticated and complex our inferences and planning, cognition would be impotent if our thoughts could not be

In one form of a contingency judgement task individuals must judge the relationship between an action and an outcome. There are reports that depressed individuals are more accurate than are nondepressed individuals in this task. In particular, nondepressed individuals are influenced by manipulations that affect the salience of the outcome, especially outcome probability. They overestimate a contingency if the probability of an outcome is high—the “outcome-density effect”. In contrast, depressed individuals display little or no outcome-density effect. This apparent knack for depressives not to be misled by outcome density in their contingency judgements has been termed “depressive realism”, and the absence of an outcome-density effect has led to the characterization of depressives as “sadder but wiser”. We present a critical summary of the depressive realism literature and provide a novel interpretation of the phenomenon. We suggest that depressive realism may be understood from a psychophysical analysis of contingency judgements. Alloy and Abramson (1979) reported an unexpected and intriguing result that attracted the attention of many researchers and is discussed in current textbooks (e.g., Myers & Spencer, 2004; Nolen-

We present a framework for the rational analysis of elemental causal induction -- learning about the existence of a relationship between a single cause and effect -- based upon causal graphical models. This framework makes precise the intuitive distinction between causal structure and causal strength: the difference between asking whether or not a causal relationship exists, and asking how strong that causal relationship might be. We show that the two leading rational models of elemental causal induction, #P and causal power, both estimate causal strength, and introduce a new rational model, causal support, that assesses causal structure. Causal support provides a better account of a large number of existing datasets than either #P or causal power. It also predicts several phenomena that cannot be accounted for by other models, which we explore through a series of experiments. These phenomena include the complex interaction between #P and the base-rate probability of the effect in the absence of the cause, sample size effects, inferences from incomplete contingency tables, and causal learning from rates.