The authors outline a cognitive and computational account of causal learning in children. They propose that children use specialized cognitive systems that allow them to recover an accurate “causal map ” of the world: an abstract, coherent, learned representation of the causal relations among events. This kind of knowledge can be perspicuously understood in terms of the formalism of directed graphical causal models, or Bayes nets. Children’s causal learning and inference may involve computations similar to those for learning causal Bayes nets and for predicting with them. Experimental results suggest that 2to 4-year-old children construct new causal maps and that their learning is consistent with the Bayes net formalism.

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 distinction between causal structure and causal strength: the difference between asking whether a causal relationship exists and asking how strong that causal relationship might be. We show that 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 predicts several key phenomena of causal induction that cannot be accounted for by other rational 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. Causal support also provides a better account of a number of existing datasets than either ∆P or causal power.

Recent research on contingency judgment indicates that the judged predictiveness of a cue is dependent on the predictive strengths of other cues. Two classes of models correctly predict such cue interaction: associative models and statistical models. However, these models differ in their predictions about the effect of trial order on cue interaction. In five experiments reported here, college students viewed trial-by-trial data regarding several medical symptoms and a disease, judging the predictive strength of each symptom with respect to the disease. The results indicate that trial order influences the manner in which cues interact, but that neither the associative nor the statistical models can fully account for the data pattern. A possible variation of an associative account is discussed. The ability to detect predictive relationships among envi-ronmental events grants humans and other animals a distinct benefit. Therefore, the mechanisms underlying this ability are of considerable interest. Recent research with humans on judgments of contingencies has shed light on these mecha-nisms. It has suggested two classes of theoretical models that

Charles and Miller propose that lexical associations between antonymous adjectives are formed via their co-occurrences within the same sentence (the co-occurrence hypothesis), rather than via their syntactic substitutability (the substitutability hypothesis), and that such cooccurrences must take place more often than expected by chance. This paper provides empirical support for the co-occurrence hypothesis, in a corpus analysis of all high-frequency adjectives and their antonyms and of a major group of morphologically derived antonyms (e.g., impossible, un-happy). We show that very high co-occurrence rates do appear to characterize all antonymous adjective pairs, supporting the precondition for the formation of the association; and we find that the syntactic contexts of these co-occurrences raise the intrinsic associability of antonyms when they do co-occur. We show that via one of these patterns, mutual substitution within otherwise repeated phrases in a sentence, the co-occurrence hypothesis captures the generalizations that were the basis for the substitutability hypothesis for the formation of antonymic associations. 1. Antonymic Association Much current research in linguistics is concerned with textual or discourse bases for

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.

Similarities between Pavlovian conditioning in nonhumans and causal judgment by humans suggest that similar processes operate in these situations. Notably absent among the similarities is backward blocking (i.e., retrospective devaluation of a signal due to increased valuation of another signal that was present during training), which has been observed in causal judgment by humans but not in Pavlovian responding by animals. The authors used rats to determine if this difference arises from the target cue being biologically significant in the Pavlovian case but not in causal judgment. They used a sensory preconditioning procedure in Experiments 1 and 2, in which the target cue retained low biological significance during the treatment, and obtained backward blocking. The authors found in Experiment 3 that forward blocking also requires the target cue to be of low biological significance. Thus, low biological significance is a necessary condition for a stimulus to be vulnerable to blocking. In recent years, numerous researchers have remarked on the similarity of the conditions that encourage the acquisition of causal relationships in humans and those that foster

The dynamics model, which is based on L. Talmy’s (1988) theory of force dynamics, characterizes causation as a pattern of forces and a position vector. In contrast to counterfactual and probabilistic models, the dynamics model naturally distinguishes between different cause-related concepts and explains the induction of causal relationships from single observations. Support for the model is provided in experiments in which participants categorized 3-D animations of realistically rendered objects with trajectories that were wholly determined by the force vectors entered into a physics simulator. Experiments 1–3 showed that causal judgments are based on several forces, not just one. Experiment 4 demonstrated that people compute the resultant of forces using a qualitative decision rule. Experiments 5 and 6 showed that a dynamics approach extends to the representation of social causation. Implications for the relationship between causation and time are discussed.

Five studies investigated (a) children’s ability to use the dependent and independent probabilities of events to make causal inferences and (b) the interaction between such inferences and domain-specific knowledge. In Experiment 1, preschoolers used patterns of dependence and independence to make accurate causal inferences in the domains of biology and psychology. Experiment 2 replicated the results in the domain of biology with a more complex pattern of conditional dependencies. In Experiment 3, children used evidence about patterns of dependence and independence to craft novel interventions across domains. In Experiments 4 and 5, children’s sensitivity to patterns of dependence was pitted against their domain-specific knowledge. Children used conditional probabilities to make accurate causal inferences even when asked to violate domain boundaries. The past two decades of research have demonstrated that young children understand cause and effect in a wide range of contexts. By the age of 4, children’s folk physics includes knowledge about the causal relationship between object properties and object motion

How do humans discover causal relations when the effect is not immediately observable? Previous experiments have uniformly demonstrated detrimental effects of outcome delays on causal induction. These findings seem to conflict with everyday causal cognition, where humans can apparently identify long-term causal relations with relative ease. Three experiments investigated whether the influence of delay on adult human causal judgments is mediated by experimentally induced assumptions about the timeframe of the causal relation in question, as suggested by Einhorn & Hogarth (1986). Causal judgments generally decreased when a delay separated cause and effect. This decrease was less pronounced when the thematic context of the causal relation induced participants to expect a delay. Experiment 3 ruled out an alternative explanation of the effect based on variations of cue and outcome saliencies, and showed that detrimental effects of delay are reduced even more when instructions explicitly mentioned the timeframe of the causal relation in question. Knowledge thus mediates the impact of

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