Recently, the theory theory has led to much interesting research. However, this is the first book to look at the theory in extensive detail and to systematically contrast it with other theories.

We propose that children employ 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 2- to 4-year-old children (...) construct new causal maps and that their learning is consistent with the Bayes net formalism. (shrink)

Understanding causal structure is a central task of human cognition. Causal learning underpins the development of our concepts and categories, our intuitive theories, and our capacities for planning, imagination and inference. During the last few years, there has been an interdisciplinary revolution in our understanding of learning and reasoning: Researchers in philosophy, psychology, and computation have discovered new mechanisms for learning the causal structure of the world. This new work provides a rigorous, formal basis for theory theories of concepts and (...) cognitive development, and moreover, the causal learning mechanisms it has uncovered go dramatically beyond the traditional mechanisms of both nativist theories, such as modularity theories, and empiricist ones, such as association or connectionism. (shrink)

I argue that explanation should be thought of as the phenomenological mark of the operation of a particular kind of cognitive system, the theory-formation system. The theory-formation system operates most clearly in children and scientists but is also part of our everyday cognition. The system is devoted to uncovering the underlying causal structure of the world. Since this process often involves active intervention in the world, in the case of systematic experiment in scientists, and play in children, the cognitive system (...) is accompanied by a theory drive, a motivational system that impels us to interpret new evidence in terms of existing theories and change our theories in the light of new evidence. What we usually think of as explanation is the phenomenological state that accompanies the satisfaction of this drive. However, the relation between the phenomenology and the cognitive system is contingent, as in similar cases of sexual and visual phenomenology. Distinctive explanatory phenomenology may also help us to identify when the theory-formation system is operating. (shrink)

This paper argues that there are powerful similarities between cognitive development in children and scientific theory change. These similarities are best explained by postulating an underlying abstract set of rules and representations that underwrite both types of cognitive abilities. In fact, science may be successful largely because it exploits powerful and flexible cognitive devices that were designed by evolution to facilitate learning in young children. Both science and cognitive development involve abstract, coherent systems of entities and rules, theories. In both (...) cases, theories provide predictions, explanations, and interpretations. In both, theories change in characteristic ways in response to counterevidence. These ideas are illustrated by an account of children's developing understanding of the mind. (shrink)

At least since Augustine, philosophers have constructed developmental just-so stories about the origins of certain concepts. In these just-so stories, philosophers tell us how children must develop these concepts. However, philosophers have by and large neglected the empirical data about how children actually do develop their ideas about the world. At best they have used information about children in an anecdotal and unsystematic, though often illuminating, way.

This chapter examines several ways in which philosophical attention to intuition can contribute to empirical scientific psychology. The authors then discuss one prevalent misuse of intuition. An unspoken assumption of much argumentation in the philosophy of mind has been that to articulate our folk psychological intuitions, our ordinary concepts of belief, truth, meaning, and so forth, is itself sufficient to give a theoretical account of what belief, truth, meaning, and so forth, actually are. It is believed that this assumption rests (...) on an inadequate understanding of the nature of intuition and its appropriate applications, and that it results in errors. Three notable examples of this sort of misuse of intuition in philosophy are briefly discussed. Finally, the authors provide developmental evidence for the mutability and fallibility of everyday intuitions about the mind, evidence that undermines arguments, that depend on taking such intuitions as a final authority for substantive claims about what the mind is like. (shrink)

Young children spend a large portion of their time pretending about non-real situations. Why? We answer this question by using the framework of Bayesian causal models to argue that pretending and counterfactual reasoning engage the same component cognitive abilities: disengaging with current reality, making inferences about an alternative representation of reality, and keeping this representation separate from reality. In turn, according to causal models accounts, counterfactual reasoning is a crucial tool that children need to plan for the future and learn (...) about the world. Both planning with causal models and learning about them require the ability to create false premises and generate conclusions from these premises. We argue that pretending allows children to practice these important cognitive skills. We also consider the prevalence of unrealistic scenarios in children's play and explain how they can be useful in learning, despite appearances to the contrary. (shrink)

Two studies examined the specificity of effects of explanation on learning by prompting 3- to 6-year-old children to explain a mechanical toy and comparing what they learned about the toy’s causal and non-causal properties to children who only observed the toy, both with and without accompanying verbalization. In Study 1, children were experimentally assigned to either explain or observe the mechanical toy. In Study 2, children were classified according to whether the content of their response to an undirected prompt involved (...) explanation. Dependent measures included whether children understood the toy’s functional-mechanical relationships, remembered perceptual features of the toy, effectively reconstructed the toy, and (for Study 2) generalized the function of the toy when constructing a new one. Results demonstrate that across age groups, explanation promotes causal learning and generalization, but does not improve (and in younger children, can even impair) memory for causally-irrelevant perceptual details. (shrink)

Previous research suggests that children can infer causal relations from patterns of events. However, what appear to be cases of causal inference may simply reduce to children recognizing relevant associations among events, and responding based on those associations. To examine this claim, in Experiments 1 and 2, children were introduced to a “blicket detector”, a machine that lit up and played music when certain objects were placed upon it. Children observed patterns of contingency between objects and the machine’s activation that (...) required them to use indirect evidence to make causal inferences. Critically, associative models either made no predictions, or made incorrect predictions about these inferences. In general, children were able to make these inferences, but some developmental differences between 3- and 4- year-olds were found. We suggest that children’s causal inferences are not based on recognizing associations, but rather that children develop a mechanism for Bayesian structure learning. Experiment 3 explicitly tests a prediction of this account. Children were asked to make an inference about ambiguous data based on the base-rate of certain events occurring. Fouryear-olds, but not 3-year-olds were able to make this inference. (shrink)

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. (shrink)

People are adept at inferring novel causal relations, even from only a few observations. Prior knowledge about the probability of encountering causal relations of various types and the nature of the mechanisms relating causes and effects plays a crucial role in these inferences. We test a formal account of how this knowledge can be used and acquired, based on analyzing causal induction as Bayesian inference. Five studies explored the predictions of this account with adults and 4-year-olds, using tasks in which (...) participants learned about the causal properties of a set of objects. The studies varied the two factors that our Bayesian approach predicted should be relevant to causal induction: the prior probability with which causal relations exist, and the assumption of a deterministic or a probabilistic relation between cause and effect. Adults’ judgments (Experiments 1, 2, and 4) were in close correspondence with the quantitative predictions of the model, and children’s judgments (Experiments 3 and 5) agreed qualitatively with this account. (shrink)

This paper argues that there are powerful similarities between cognitive development in children and scientific theory change. These similarities are best explained by postulating an underlying abstract set of rules and representations that underwrite both types of cognitive abilities. In fact, science may be successful largely because it exploits powerful and flexible cognitive devices that were designed by evolution to facilitate learning in young children. Both science and cognitive development involve abstract, coherent systems of entities and rules, theories. In both (...) cases, theories provide predictions, explanations, and interpretations. In both, theories change in characteristic ways in response to counterevidence. These ideas are illustrated by an account of children's developing understanding of the mind. (shrink)

We used a new method to assess how people can infer unobserved causal structure from patterns of observed events. Participants were taught to draw causal graphs, and then shown a pattern of associations and interventions on a novel causal system. Given minimal training and no feedback, participants in Experiment 1 used causal graph notation to spontaneously draw structures containing one observed cause, one unobserved common cause, and two unobserved independent causes, depending on the pattern of associations and interventions they saw. (...) We replicated these findings with less‐informative training (Experiments 2 and 3) and a new apparatus (Experiment 3) to show that the pattern of data leads to hidden causal inferences across a range of prior constraints on causal knowledge. (shrink)

Although mind‐wandering research is rapidly progressing, stark disagreements are emerging about what the term “mind‐wandering” means. Four prominent views define mind‐wandering as task‐unrelated thought, stimulus‐independent thought, unintentional thought, or dynamically unguided thought. Although theorists claim to capture the ordinary understanding of mind‐wandering, no systematic studies have assessed these claims. Two large factorial studies present participants with vignettes that describe someone’s thoughts and ask whether her mind was wandering, while systematically manipulating features relevant to the four major accounts of mind‐wandering. Dynamics (...) explains between four and 40 times more variance in participants’ mind‐wandering judgments than other features. Our third study tests and supports a unique prediction of the dynamic framework—obsessive rumination contrasts with mind‐wandering. Our final study used vignettes that resemble mind‐wandering experiments. Dynamics had significant and large effects, while task‐unrelatedness was nonsignificant. These results strongly suggest that the central feature of mind‐wandering is its dynamics. (shrink)

We outline a cognitive and computational account of causal learning in children. We propose that children employ specialized cognitive systems that allow them to recover an accurate “causal map” of the world: an abstract, coherent representation of the causal relations among events. This kind of knowledge can be perspicuously represented by the formalism of directed graphical causal models, or “Bayes nets”. Human causal learning and inference may involve computations similar to those for learnig causal Bayes nets and for predicting with (...) them. Preliminary experimental results suggest that 2- to 4-year-old children spontaneously construct new causal maps and that their learning is consistent with the Bayes-Net formalism. (shrink)

Word count (excluding abstract and references): 2,498 words. Address for correspondence: T. Kushnir, Psychology Department, University of California, 3210 Tolman Hall #1650, Berkeley, CA 94720-1650. Phone: 510-205-9847. Fax: 510-642- 5293. E-mail: [email protected]

The ability to represent same-different relations is an important condition for abstract thought. However, there is mixed evidence for when this ability develops, both ontogenetically and phylogenetically. Apparent success in relational reasoning may be evidence for genuine conceptual understanding or may be the result of low-level, perceptual strategies. We introduce a method to discriminate these possibilities by pitting two conditions that are perceptually matched but conceptually different: in a "fused" condition, same and different objects are joined, creating single objects that (...) have the same perceptual features as the two object pairs in the "relational" condition. However, the "fused" objects do not provide evidence for the relation 'same.' Using this method with human toddlers in a causal relational reasoning task provides evidence for genuine conceptual understanding. This novel technique offers a simple manipulation that may be applied to a variety of existing match-to-sample procedures used to assess same-different reasoning to include in future research with non-human animals across species, as well as human infants. (shrink)

My research program proposes that children have representations and learning mechanisms that can be characterized as causal models of the world Bayesian Fundamentalism.”.

Block argues for a method and a substantive thesis – that consciousness overflows accessibility. The method can help answer the question of what it is like to be a baby. Substantively, infant consciousness may be accessible in some ways but not others. But development itself can also add important methodological tools and substantive insights to the study of consciousness.

Three experiments investigated young children’s ability to use a causal property, making a machine light up and play music, to sort objects together (sorting task), and then to predict how to make the machine work (action task). The results show that the performance of 30-month-old children is guided in both tasks by the causal properties of the objects. This suggests that causal information is used to categorize objects even in a task that does not involve naming. The causal interpretation of (...) the results is supported by data showing that non-causal temporal association and perceptual prominence cannot account for the results. Finally, 36-month-olds, but not 30-month-olds, sorted objects together on the basis of a “negative” feature, namely, the fact that they lacked a particular causal property. © 2003 Elsevier Science Inc. All rights reserved. (shrink)