The book can also be used as an excellent text for graduate-level courses in AI, operations research, or applied probability.

The final work of a distinguished physicist, this remarkable volume examines the emotive significance of time, the time order of mechanics, the time direction of thermodynamics and microstatistics, the time direction of macrostatistics, and the time of quantum physics. Coherent discussions include accounts of analytic methods of scientific philosophy in the investigation of probability, quantum mechanics, the theory of relativity, and causality. "[Reichenbach’s] best by a good deal."—Physics Today. 1971 ed.

Experiments with young infants provide evidence for early-developing capacities to represent physical objects and to reason about object motion. Early physical reasoning accords with 2 constraints at the center of mature physical conceptions: continuity and solidity. It fails to accord with 2 constraints that may be peripheral to mature conceptions: gravity and inertia. These experiments suggest that cognition develops concurrently with perception and action and that development leads to the enrichment of conceptions around an unchanging core. The experiments challenge claims (...) that cognition develops on a foundation of perceptual or motor experience, that initial conceptions are inappropriate to the world, and that initial conceptions are abandoned or radically.. (shrink)

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)

Causality offers the first comprehensive coverage of causal analysis in many sciences, including recent advances using graphical methods. Pearl presents a unified account of the probabilistic, manipulative, counterfactual and structural approaches to causation, and devises simple mathematical tools for analyzing the relationships between causal connections, statistical associations, actions and observations. The book will open the way for including causal analysis in the standard curriculum of statistics, artificial intelligence, business, epidemiology, social science and economics.

Do children have a theory of mind? If they do, at what age is it acquired? What is the content of the theory, and how does it differ from that of adults? The Child's Theory of Mind integrates the diverse strands of this rapidly expanding field of study. It charts children's knowledge about a fundamental topic - the mind - and characterizes that developing knowledge as a coherent commonsense theory, strongly advancing the understanding of everyday theories as well as the (...) commonsense theory of mind. Wellman presents evidence that children as young as age three do possess a commonsense theory of mind - that they grasp the distinction between mental constructs and physical entities and that they have an understanding of the relationship between individuals' mental states and their overt actions. He delves in detail into questions about the nature of adults' commonsense theories of mind and about the nature of commonsense theories. Wellman then examines the content of the three-year-old's theory of mind, the nature of children's notions of mind before age three, the changes in the theory during subsequent development from ages three to six, and the young child's conception of mind in comparison with those of older children and adults. Henry M. Wellman is a Professor in the Department of Psychology and the Center for Human Growth and Development at the University of Michigan at Ann Arbor. (shrink)

A model of writing in cognitive development, Understanding the Representational Mind synthesizes the burgeoning literature on the child’s theory of mind to provide an integrated account of children’s understanding of representational and mental processes, which is crucial in their acquisition of our commonsense psychology. Perner describes experimental work on children’s acquisition of a theory of mind and representation, offers a theoretical account of this acquisition, and gives examples of how the increased sophistication in children’s theory of mind improves their understanding (...) of social interaction and how, in the case of autistic children, an impairment results in social ineptitude. He analyzes the concepts of representation and metarepresentation as they appear in current discussion in the philosophy of cognitive science and explains how the unfolding of mental representation enables infants to comprehend change over time, engage in pretence, and use representational systems like pictures and language. Perner goes on to show that around age four children become able to understand the representational nature of pictures and language and to distinguish appearance from reality. Introducing basic distinctions in philosophy of mind for characterizing the mental, Perner discusses differences in how commonsense and cognitive psychology view the mind. Tracing the onset of a commonsense psychology in the social and emotional awareness of early infancy, he reveals how the child begins to take a cognitive, representational view of the mind with repercussions for children’s episodic memory, self control, and their ability to engage in deception. Perner concludes by describing the observed developmental changes as a case of theory change And contrasts his thesis with competing proposals. Josef Perner is Lecturer in Experimental Psychology at Sussex University, Brighton, England. (shrink)

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.

The Rescorla–Wagner model has been a leading theory of animal causal induction for nearly 30 years, and human causal induction for the past 15 years. Recent theories 367) have provided alternative explanations of how people draw causal conclusions from covariational data. However, theoretical attempts to compare the Rescorla–Wagner model with more recent models have been hampered by the fact that the Rescorla–Wagner model is an algorithmic theory, while the more recent theories are all computational. This paper provides a detailed derivation (...) of the long-run behavior of the Rescorla– Wagner model under a wide range of parameters and experimental setups, so that the model can be compared with computational theories. It also shows that the model agrees with competing theories on a wider range of cases than had previously been thought. The paper concludes by showing how recently suggested modifications of the Rescorla–Wagner model impact the long-run behavior of the model. (shrink)