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

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computation. In preparation. Address for correspondence:

The majority of psychological studies on causality have focused on simple cause-effect relations. Little is known about how people approach more realistic, complex causal networks. Two experiments are presented that investigate how participants integrate causal knowledge that was acquired in separate learning tasks into a coherent causal model. To accomplish this task it is necessary to bring to bear knowledge about the structural implications of causal models. For example, whereas common-cause models imply a covariation among the different effects of a common cause, no such covariation between the different causes of a joint effect is implied by a common-effect model. The experiments show that participants have virtually no explicit knowledge of these relations, and therefore tend to misrepresent the structural implications of causal models in their explicit judgments. However, an implicit task that only required predictions of singular events showed surprisingly accurate sensitivity to the structural implications of causal models. This dissociation supports the view that people's sensitivity to structural implications is mediated by running simulations on mental analogs of the causal situations.

models provided the best account of data generated in tasks that require human observers to judge the relationship between binary events. In the intervening years, new data have been reported that provide evidence for higherorder processes. Some have argued that these new data pose a serious threat to the viability of the associative account. The purpose of the present paper is to review this evidence and to assess the severity of this threat. In 1978, Brooks described the interaction between analytic and nonanalytic processes, and argued that “there are many factors that push a person’s strategy toward one end of the scale or another – that is, toward learning individuals by codings that are designed to retain the item’s individuality, or toward tracking the validity of characteristics of the stimulus

An ongoing goal in the field of categorization has been to determine how objects ’ features provide evidence of membership in one category versus another. Well-known findings include that feature diagnosticity is a function of how often the feature appears in category members versus nonmembers, their perceptual salience, how features are used in support of inferences, and how observable features are related to other observable features. We tested how diagnosticity is affected by causal relations between observable and unobserved features. Consistent with our view of classification as diagnostic reasoning, we found that observable features are more diagnostic to the extent that they are caused by underlying features that define category membership, because the presence of the latter can be (causally) inferred from the former. Implications of these results for current views of conceptual structure and models of categorization are discussed. It is generally accepted that people’s concepts include not only the features and attributes of the entity being represented, but also the ways in which those features are related to one another. For example, we know that hormones can alter a person’s behavior, that chemical structure can affect a substance’s hardness, and that processor

Three experiments examined human processing of stimuli as predictors and causes. In Experiments 1A and 1B, two serial events that both preceded a third were assessed as predictors and as causes of the third event. Instructions successfully provided scenarios in which one of the serial (target) stimuli was viewed as a strong predictor but as a weak cause of the third event. In Experiment 2, participants ’ preexperimental knowledge was drawn upon in such a way that two simultaneous antecedent events were processed as predictors or causes, which strongly influenced the occurrence of overshadowing between the antecedent events. Although a tendency toward overshadowing was found between predictors, reliable overshadowing was observed only between causes, and then only when the test question was causal. Together with other evidence in the human learning literature, the present results suggest that predictive and causal learning obey similar laws, but there is a greater susceptibility to cue competition in causal than predictive attribution. This paper examines differences between predictive and causal learning in humans. Events often occur in our environment according to a consistent temporal distribution. Some events occur simultaneously (e.g., the sound and sight of water running out of the tap), whereas other events occur sequentially (e.g., hunger dissipates after the intake of food). When the events repeatedly take place following a sequential distribution in time, the first event (i.e., the antecedent event) can become a signal for the occurrence of the second event (i.e., the subsequent event). Learning to predict the occurrence of an event on O.P. was supported by a postdoctoral fellowship from the Spanish

In the analysis of stimulus competition in causal judgment, 4 variables have been frequently confounded with respect to the conditions necessary for stimuli to compete: causal status of the competing stimuli (causes vs. effects), temporal order of the competing stimuli (antecedent vs. subsequent) relative to the noncompeting stimulus, directionality of training (predictive vs. diagnostic), and directionality of testing (predictive vs. diagnostic). In a factorial study using an overshadowing preparation, the authors isolated the role of each of these variables and their interactions. The results indicate that competition may be obtained in all conditions. Although some of the results are compatible with various theories of learning, the observation of stimulus competition in all conditions calls for a less restrictive reformulation of current learning theories that allows similar processing of antecedent and subsequent events, as well as of causes and effects. Stimulus competition is defined as the phenomenon in which responding to a target stimulus (X), on the basis of its signaling some event, is weakened as a consequence of X’s being trained in the presence of another stimulus (A) that better signals the same

Competence and performance in causal learning The dominant theoretical approach to causal learning postulates the acquisition of associative weights between cues and outcomes. These associative weights reflect the amount of covariation between the learning events. In the past few years, the associationist approach to causal learning has been criticized by a number of researchers