Judea Pearl has been at the forefront of research in the burgeoning field of causal modeling, and Causality is the culmination of his work over the last dozen or so years. For philosophers of science with a serious interest in causal modeling, Causality is simply mandatory reading. Chapter 2, in particular, addresses many of the issues familiar from works such as Causation, Prediction and Search by Peter Spirtes, Clark Glymour, and Richard Scheines. But philosophers with a more general interest in (...) causation will also profit from reading Pearl’s book, especially the material in chapters 7, 9, and 10, which is self-contained and less technical than other parts of the book. The present review is aimed primarily at readers of the second type. (shrink)

The essentialist approach to word meaning has been used to undermine the fundamental assumptions of the cognitive psychology of concepts. Essentialism assumes that a word refers to a natural kind category in virtue of category members possessing essential properties. In support of this thesis, Kripke and Putnam deploy various intuitions concerning word use under circumstances in which discoveries about natural kinds are made. Although some studies employing counterfactual discoveries and related transformations appear to vindicate essentialism, we argue that the intuitions (...) have not been investigated exhaustively. In particular, we argue that discoveries concerning the essential properties of whole categories (rather than simply of particular category members) are critical to the essentialist intuitions. The studies reported here examine such discovery contexts, and demonstrate that words and concepts are not used in accordance with essentialism. The results are, however, consistent with "representational change" views of concepts, which are broadly Fregean in their motivation. We conclude that since essentialism is not vindicated by ordinary word use, it fails to undermine the cognitive psychology of concepts. (shrink)

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

I argue that psychologists interested in human causal judgment should understand and adopt a representation of causal mechanisms by directed graphs that encode conditional independence (screening off) relations. I illustrate the benefits of that representation, now widely used in computer science and increasingly in statistics, by (i) showing that a dispute in psychology between ‘mechanist’ and ‘associationist’ psychological theories of causation rests on a false and confused dichotomy; (ii) showing that a recent, much-cited experiment, purporting to show that human subjects, (...) incorrectly let large causes ‘overshadow’ small causes, misrepresents the most likely, and warranted, causal explanation available to the subjects, in the light of which their responses were normative; (iii) showing how a recent psychological theory (due to P. Cheng) of human judgment of causal power can be considerably generalized: and (iv) suggesting a range of possible experiments comparing human and computer abilities to extract causal information from associations. (shrink)

A theory of categorization is presented in which knowledge of causal relationships between category features is represented in terms of asymmetric and probabilistic causal mechanisms. According to causal‐model theory, objects are classified as category members to the extent they are likely to have been generated or produced by those mechanisms. The empirical results confirmed that participants rated exemplars good category members to the extent their features manifested the expectations that causal knowledge induces, such as correlations between feature pairs that are (...) directly connected by causal relationships. These expectations also included sensitivity to higher‐order feature interactions that emerge from the asymmetries inherent in causal relationships. Quantitative fits of causal‐model theory were superior to those obtained with extensions to traditional similarity‐based models that represent causal knowledge either as higher‐order relational features or “prior exemplars” stored in memory. (shrink)

What makes a liquid water? A strong version of ``psychological essentialis'' predicts that people use the presence or absence of H2O as the primary determinant of what liquids they call ``water.'' To test this prediction, subjects were asked to judge the amount of H2O in liquids called ``water'' and liquids not called ``water.'' Neither their beliefs about the simple presence/absence of H2O nor about the proportion of H2O in the liquids accounted well for which ones are normally called "water." Typicality (...) ratings and an extended tree solution on similarity ratings suggested that use, location, and source of a liquid may also influence whether it is considered to be water. Sentence acceptability judgments further suggested that there may be a sense of "water" that corresponds to the strong essentialist view, but that there is also a more general sense in common use encompassing mixtures with varying amounts of H2O. These findings indicate that essentialist beliefs alone may not fully explain category membership judgments and word use, and they suggest a modified version of psychological essentialism. (shrink)