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

The philosophical theory of scientific explanation proposed here involves a radically new treatment of causality that accords with the pervasively statistical character of contemporary science. Wesley C. Salmon describes three fundamental conceptions of scientific explanation--the epistemic, modal, and ontic. He argues that the prevailing view is untenable and that the modal conception is scientifically out-dated. Significantly revising aspects of his earlier work, he defends a causal/mechanical theory that is a version of the ontic conception. Professor Salmon's theory furnishes a robust (...) argument for scientific realism akin to the argument that convinced twentieth-century physical scientists of the existence of atoms and molecules. To do justice to such notions as irreducibly statistical laws and statistical explanation, he offers a novel account of physical randomness. The transition from the "reviewed view" of scientific explanation to the causal/mechanical model requires fundamental rethinking of basic explanatory concepts. (shrink)

& Carnegie Mellon University Abstract The rationality of human causal judgments has been the focus of a great deal of recent research. We argue against two major trends in this research, and for a quite different way of thinking about causal mechanisms and probabilistic data. Our position rejects a false dichotomy between "mechanistic" and "probabilistic" analyses of causal inference -- a dichotomy that both overlooks the nature of the evidence that supports the induction of mechanisms and misses some important probabilistic (...) implications of mechanisms. This dichotomy has obscured an alternative conception of causal learning: for discrete events, a central adaptive task is to induce causal mechanisms in the environment from probabilistic data and prior knowledge. Viewed from this perspective, it is apparent that the probabilistic norms assumed in the human causal judgment literature often do not map onto the mechanisms generating the probabilities. Our alternative conception of causal judgment is more congruent with both scientific uses of the notion of causation and observed causal judgments of untutored reasoners. We illustrate some of the relevant variables under this conception, using a framework for causal representation now widely adopted in computer science and, increasingly, in statistics. We also review the formulation and evidence for a theory of human causal induction (Cheng, 1997) that adopts this alternative conception. (shrink)

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.

Ever since David Hume, empiricists have barred powers and capacities from nature. In this book Cartwright argues that capacities are essential in our scientific world, and, contrary to empiricist orthodoxy, that they can meet sufficiently strict demands for testability. Econometrics is one discipline where probabilities are used to measure causal capacities, and the technology of modern physics provides several examples of testing capacities (such as lasers). Cartwright concludes by applying the lessons of the book about capacities and probabilities to the (...) explanation of the role of causality in quantum mechanics. (shrink)

Whenever the use of non-experimental data for discovering causal relations or predicting the outcomes of experiments or interventions is contemplated, two difficulties are routinely faced. One is the problem of latent variables, or confounders: factors influencing two or more measured variables may not themselves have been measured or recorded. The other is the problem of sample selection bias: values of the variables or features under study may themselves influence whether a unit is included in the data sample.

Reflectance spectroscopy is a standard tool for studying the mineral composition of rock and soil samples and for remote sensing of terrestrial and extraterrestrial surfaces. We describe research on automated methods of mineral identification from reflectance spectra and give evidence that a simple algorithm, adapted from a well-known search procedure for Bayes nets, identifies the most frequently occurring classes of carbonates with reliability equal to or greater than that of human experts. We compare the reliability of the procedure to the (...) reliability of several other automated methods adapted to the same purpose. Evidence is given that the procedure can be applied to some other mineral classes as well. Since the procedure is fast with low memory requirements, it is suitable for on-board scientific analysis by orbiters or surface rovers. (shrink)

This essay explains what the Causal Markov Condition says and defends the condition from the many criticisms that have been launched against it. Although we are skeptical about some of the applications of the Causal Markov Condition, we argue that it is implicit in the view that causes can be used to manipulate their effects and that it cannot be surrendered without surrendering this view of causation.

This textbook on vision reflects the integrated computational approach of modern research scientists, combining psychological, computational and neuroscientific perspectives.

An examination of the controversial "theory of mind" hypothesis, which states that children with autism are unable to comprehend other people's mental states. The theory relates to the most fundamental questions of normal development as well as to autism i.

In Concepts, Kinds, and Cognitive Development, Frank C. Keil provides a coherent account of how concepts and word meanings develop in children, adding to our understanding of the representational nature of concepts and word meanings at all ages. Keil argues that it is impossible to adequately understand the nature of conceptual representation without also considering the issue of learning. Weaving together issues in cognitive development, philosophy, and cognitive psychology, he reconciles numerous theories, backed by empirical evidence from nominal kinds studies, (...) natural-kinds studies, and studies of fundamental categorical distinctions. He shows that all this evidence, when put together, leads to a better understanding of semantic and conceptual development. The book opens with an analysis of the problems of modeling qualitative changes in conceptual development, investigating how concepts of natural kinds, nominal kinds, and artifacts evolve. The studies on nominal kinds document a powerful and unambiguous developmental pattern indicating a shift from a reliance on global tabulations of characteristic features to what appears to be a small set of defining ones. The studies on natural kinds document an analogous shift toward a core theory instead of simple definition. Both sets of studies are strongly supported by cross cultural data. While these patterns seem to suggest that the young child organizes concepts according to characteristic features, Keil argues that there is a framework of conceptual categories and causal beliefs that enables even very young children to understand kinds at a deeper, theoretically guided, level. This account suggests a new way of understanding qualitative change and carries strong implications for how concepts are represented at any point in development. A Bradford Book. (shrink)

This book on the philosophy of science argues for an empiricism, opposed to the tradition of David Hume, in which singular rather than general causal claims are primary; causal laws express facts about singular causes whereas the general causal claims of science are ascriptions of capacities or causal powers, capacities to make things happen. Taking science as measurement, Cartwright argues that capacities are necessary for science and that these can be measured, provided suitable conditions are met. There are case studies (...) from both econometrics and quantum mechanics. (shrink)

One of Hume's most well-known works and a masterpiece of philosophy, A Treatise of Human Nature is indubitably worth taking the time to read.

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)

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)

The discovery of conjunctive causes--factors that act in concert to produce or prevent an effect--has been explained by purely covariational theories. Such theories assume that concomitant variations in observable events directly license causal inferences, without postulating the existence of unobservable causal relations. This article discusses problems with these theories, proposes a causal-power theory that overcomes the problems, and reports empirical evidence favoring the new theory. Unlike earlier models, the new theory derives (a) the conditions under which covariation implies conjunctive causation (...) and (b) functions relating observable events to unobservable conjunctive causal strength. This psychological theory, which concerns simple cases involving 2 binary candidate causes and a binary effect, raises questions about normative statistics for testing causal hypotheses regarding categorical data resulting from discrete variables. (shrink)

In this enlightening exploration of our nearest primate relatives, Michael Tomasello and Josep Call address the current state of our knowledge about the cognitive skills of non-human primates and integrate empirical findings from the beginning of the century to the present.

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)

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. Four-year-olds, but not 3-year-olds were able to make this inference. (shrink)

Reflectance spectroscopy is a standard tool for studying the mineral composition of rock and soil samples and for remote sensing of terrestrial and extraterrestrial surfaces. We describe research on automated methods of mineral identification from reflectance spectra and give evidence that a simple algorithm, adapted from a well-known search procedure for Bayes nets, identifies the most frequently occurring classes of carbonates with reliability equal to or greater than that of human experts. We compare the reliability of the procedure to the (...) reliability of several other automated methods adapted to the same purpose. Evidence is given that the procedure can be applied to some other mineral classes as well. Since the procedure is fast with low memory requirements, it is suitable for on- board scientific analysis by orbiters or surface rovers. (shrink)

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)

Chris Meek. Strong-Completeness and Faithfulness in Belief Networks.

As the cognitive revolution was slow to come to the study of animal behavior, the vast majority of what we know about primate cognition has been discovered in the last 30 years. Building on the recognition that the physical and social worlds of humans and their living primate relatives pose many of the same evolutionary challenges, programs of research have established that the most basic cognitive skills and mental representations that humans use to navigate those worlds are already possessed by (...) other primates. There may be differences between humans and other primates, however, in more complex cognitive skills, such as reasoning about relations, causality, time, and other minds. Of special importance, the human primate seems to possess a species-unique set of adaptations for “cultural intelligence,” which are broad reaching in their effects on human cognition. (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 Fast Casual Inference (FCI) algorithm searches for features common to observationally equivalent sets of causal directed acyclic graphs. It is correct in the large sample limit with probability one even if there is a possibility of hidden variables and selection bias. In the worst case, the number of conditional independence tests performed by the algorithm grows exponentially with the number of variables in the data set. This affects both the speed of the algorithm and the accuracy of the algorithm (...) on small samples, because tests of independence conditional on large numbers of variables have very low power. In this paper, I prove that the FCI algorithm can be interrupted at any stage and asked for output. The output from the interrupted algorithm is still correct with probability one in the large sample limit, although possibly less informative (in the sense that it answers “Can’t tell” for a larger number of questions) than if the FCI algorithm had been allowed to continue uninterrupted. (shrink)

Simon Blackburn defends a 'quasi-realist' view intended to preserve much of what realists want to say about moral discourse. According to error theory, moral discourse is committed to indefensible metaphysical assumptions. Quasi-realism seems to preserve ontological frugality, attributing no mistaken commitments to our moral practices. In order to make good this claim, quasi-realism must show that (a) the seemingly realist features of the 'surface grammar' of moral discourse can be made compatible with projectivism; and (b) certain realist-sounding statements which we (...) might use in describing the nature of our moral commitments can be understood in projectivist terms. Much attention has been devoted to whether quasi-realism can deliver (a). I raise an important difficulty with regard to (b). (shrink)