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.

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Associative learning theory postulates two main determinants for human causal learning: contingency and contiguity. In line with such an account, participants in Shanks, Pearson, and Dickinson (1989) failed to discover causal relations involving delays of more than two seconds. More recent research has shown that the impact of contiguity and delay is mediated by prior knowledge about the timeframe of the causal relation in question. Buehner and May (2002, 2003) demonstrated that the detrimental effect of delay can be significantly reduced if reasoners are aware of potential delays. Here we demonstrate for the first time that the negative influence of delay can be abolished completely by a subtle change in the experimental instructions. Temporal contiguity is thus not essential for human causal learning. An associative learning analysis of human causal learning postulates two main determinants of judged causal strength: the contingency and the contiguity between the potential cause (cue) and the effect (outcome) (e.g., see Shanks & Dickinson, 1987). Empirical research in the last decades has largely focused on the congruency between cue–outcome contingency and judged causal strength. Early reports suggested that human causal judgements closely track variations in cue-outcome contingency (e.g., Jenkins & Ward, 1965), while more recent studies revealed a more complex picture (e.g., Chapman & Robbins, 1990). In fact, the theoretical and empirical relations between contingency and judged causality are still the subject of a hot debate

Three experiments investigated the impact of delay on human causal learning. We present a new paradigm based on the presentation of continuous event streams, and use it to test two hypotheses drawn from associative learning theories of causal inference. Unlike free-operant procedures traditionally used to study temporal aspects of causal learning (Shanks, Pearson, & Dickinson, 1989; Shanks & Dickinson, 1987; Buehner & May, 2002, 2003, 2004), the procedure employed here allows full control over all aspects of stimulus delivery while at the same time overcoming the ecologically invalid notion of discrete learning trials. Results show that delays generally impair causal learning, but prior knowledge and experience mediate this detrimental effect. In accordance with associative learning theory, pre-exposure to an unreinforced background context facilitates the discovery of delayed causal relationships. However, contrary to associative learning theory, increasing the amount of experience with a delayed causal relationship does not improve discovery. Implications for associative learning and causal model theories are discussed.

In multiple‐cue learning people acquire information about cue‐outcome relations and combine these into predictions or judgments. Previous studies claim that people can achieve high levels of performance without explicit knowledge of the task structure or insight into their own judgment policies. It has also been argued that people use a variety of suboptimal strategies to solve such tasks. In two experiments we re‐examined these conclusions by introducing novel measures of task knowledge and self‐insight, and using ‘rolling regression ’ methods to analyze individual learning. Participants successfully learned a four‐cue probabilistic environment and showed accurate knowledge of both the task structure and their own judgment processes. Learning analyses suggested that the apparent use of suboptimal strategies emerges from the incremental tracking of statistical contingencies in the environment. These findings have wide repercussions for the study of multicue learning in both normal and patient populations. Insight 3

Many studies of contingency judgments have used a task in which, on each trial, the participant is free either to respond or not to respond, and an outcome may, or may not, be presented. Typically, the experimenter specifies a nominal value for the contingency between responding and outcome, but the actual values of a variety of variables experienced by a particular participant depend on that participant’s frequency of responding. The results of computer simulations of various strategies for implementing the contingency manipulation, and the results of an experiment, indicate that the same nominal contingency value will lead to considerable variability in the actual contingency experienced by participants. Moreover, nominal contingency manipulations are confounded with the probability that the subject experiences an outcome. While researchers might be aware of these issues, not enough attention has been paid to their potential impact. © 2006 Published by Elsevier B.V.