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.

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.

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

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

In the previous chapter (Tenenbaum, Griffiths, & Niyogi, this volume), we introduced a framework for thinking about the structure, function, and acquisition of intuitive theories inspired by an analogy to the research program of generative grammar in linguistics. We argued that a principal function for intuitive theories, just as for grammars for natural

A connectionist model of causal attribution is presented, emphasizing the use of domain-general principles of processing and learning previously employed in models of semantic cognition. The model categorizes objects dependent upon their observed “causal properties ” and is capable of making several types of inferences that four-year-old children have been shown to be capable of. The model gives rise to approximate conformity to normative models of causal inference and gives approximate estimates of the probability that an object presented in an ambiguous situation actually possesses a particular causal power, based on background knowledge and recent observations. It accounts for data from three sets of experimental studies of the causal inferencing abilities of young children. The model provides a base for further efforts to delineate the intuitive mechanisms of causal inference employed by children and adults, without appealing to inherent principles or mechanisms specialized for causal as opposed to other forms of reasoning.

Abstract. The newly established cluster of excellence COTESYS 1 investigates the realization of cognitive capabilities such as perception, learning, reasoning, planning, and execution for technical systems including humanoid robots, flexible manufacturing systems, and autonomous vehicles. In this paper we describe cognitive technical systems using a sensor-equipped kitchen with a robotic assistant as an example. We will particularly consider the role of Artificial Intelligence in the research enterprise. Key research foci of Artificial Intelligence research in COTESYS include (◦) symbolic representations grounded in perception and action, (◦) first-order probabilistic representations of actions, objects, and situations, (◦) reasoning about objects and situations in the context of everyday manipulation tasks, and (◦) the representation and revision of robot plans for everyday activity. 1

Abstract: Applications of multivariate statistical techniques, including graphical models, are seldom found in e-commerce studies. However, as this paper demonstrates, we find that probabilistic graphical models are useful in this area, both because of their ability to handle large numbers of potentially interrelated variables, and because of their ability to communicate statistical relationships clearly to both the researcher and the ultimate business audience. We show an application of this methodology to intranets, internal corporate information systems employing Internet technology. In particular, we study both the interrelationships among intranet benefits and the interrelationships among intranet applications. This approach confirms some hypothesized relationships, and uncovers heretofore-unanticipated relationships among intranet variables, providing guidance for business professionals seeking to develop effective intranet systems. The techniques described here also have potential applicability in other e-commerce arenas, including business-to-consumer and business-to-business applications. Key words: Contingency tables, graphic models, log-linear models. 1.

Causal learning requires integrating constraints provided by domain-specific theories with domaingeneral statistical learning. In order to investigate the interaction between these factors, the authors presented preschoolers with stories pitting their existing theories against statistical evidence. Each child heard 2 stories in which 2 candidate causes co-occurred with an effect. Evidence was presented in the

One of the deepest and most ancient problems in philosophy is what we might call the problem of knowledge. There seems to be an unbridgeable gap between our abstract, complex, highly structured knowledge of the world, and the concrete, limited and confused information provided by our senses. Since the Meno, there have been two basic ways of approaching this problem, rationalism and empiricism. Each era seems to have its matched pair of advocates of each view, making their way through the centuries like couples in some eternal philosophical gavotte, Plato and Aristotle, Descartes and Locke, Leibniz and Berkeley, Kant and Mill. The rationalist approach says that although it looks as if we learn about the world from our experience, we don’t really. Actually, we knew about it all along. The most important things we know were there to begin with, planted innately in our minds by God or evolution (or chance). The empiricist approach says that although it looks as if our knowledge is far removed from our experience, it isn’t really. If we rearrange the elements of our experience in particular ways, by associating ideas, or putting together stimuli and responses, we’ll end up with our knowledge of the world. There is both a tension and a kind of complementarity between these two ideas,